Target detection method, integrated circuit, sensor, device and medium

Abstract

A target detection method, integrated circuit, sensor, device, and medium, the method including: storing frame data for 1D-FFT data, the 1D-FFT data being acquired by performing range-dimensional FFT processing on echo signals; performing inter-frame FFT processing based on the stored frame data to acquire an RD spectrum; and detecting a target in a region of interest based on the RD spectrum.

Description

[Technical Field]

[0001] This application claims priority to a Chinese patent application filed on July 24, 2023, bearing application number 202310913653.8 and entitled "Target detection method and system, integrated circuit, sensor and device," the contents of which are hereby incorporated by reference. FIELD OF THE DISCLOSURE Embodiments of the present disclosure relate to, but are not limited to, the technical field of electromagnetic wave sensors, and in particular to target detection methods and systems, integrated circuits, sensors and devices. [Background technology]

[0002] When target detection is performed in a sealed or relatively sealed spatial region, such as the inside of a car cabin, there are technical challenges such as low detection rate, many false alarms, and inaccurate angle estimation. Summary of the Invention

[0003] The following is a general overview of the subject matter discussed in detail in the text, which is not intended to limit the scope of protection of the claims.

[0004] An embodiment of the present invention provides a target detection method, the method comprising: Frame data is accumulated for 1D-FFT data, and the 1D-FFT data is acquired by performing distance-dimensional FFT processing on the echo signal; performing FFT processing between frames based on the accumulated frame data to obtain an RD spectrum; and realizing detection of a target in a region of interest based on the RD spectrum.

[0005] As an option, the method may include performing frame data accumulation on the 1D-FFT data, accumulating up to a predetermined data amount, and then performing FFT processing between frames to obtain an RD spectrum.

[0006] Optionally, for a multi-channel application scenario, detecting a target in a region of interest based on the RD spectrum may include performing non-coherent integration on the RD spectrum of each channel, followed by a constant false alarm probability process based on noise estimation, wave direction estimation, target determination, positioning and recognition operations.

[0007] Alternatively, the constant false alarm probability process may be a DAE CFAR process for azimuth and elevation, respectively.

[0008] Optionally, detecting targets in the region of interest may further include performing target recognition operations based at least in part on extracting multiple frames of accumulated data, frame-to-frame Fourier transformed data, non-coherent integrated data, and / or constant false alarm probability detection data.

[0009] Optionally, for a multi-channel application scenario, realizing target detection in a region of interest based on the RD spectrum may include performing a constant false alarm probability process on the RD spectrum of each channel, followed by channel domain binary integration processing, wave arrival direction estimation, and then performing target determination, location, and recognition operations.

[0010] Optionally, realizing target detection in the region of interest based on the RD spectrum includes performing two-dimensional digital beam synthesis on the RD spectrum, followed by target classification processing, thereby realizing target determination, location and recognition operations.

[0011] Optionally, performing deep learning target detection, deep learning target localization, and / or deep learning target recognition on the 1D-FFT data may include obtaining target point cloud data based on the 1D-FFT data, and performing ML-based false alarm suppression processing, clustering processing, and / or ML-based target classification processing on the target point cloud data, thereby realizing target determination, localization, and / or recognition operations in the region of interest.

[0012] Optionally, the method may further include performing two-dimensional digital beam synthesis on the 1D-FFT data, followed by frame data accumulation, accumulating up to a preset data amount, and then performing FFT processing between frames to obtain an RD spectrum, and performing DL-based target classification processing based on the RD spectrum, thereby realizing target determination, positioning, and recognition operations.

[0013] An embodiment of the present invention further provides a target detection method, which may include: performing range-dimension FFT processing based on echo signals to obtain 1D-FFT data; performing two-dimensional digital beam synthesis on the 1D-FFT data, followed by frame data accumulation; and performing target classification processing based on DL after accumulating a predetermined amount of data, thereby realizing target determination, positioning, and recognition operations.

[0014] An embodiment of the present application further provides an integrated circuit, which may include: a signal transmitting module configured to be used with electromagnetic waves for target detection; a signal receiving module configured to be used to receive echoes formed by reflection and / or scattering of the electromagnetic waves; and a processing module configured to be used to perform signal and data processing on the echoes based on the method of any of the embodiments to realize target detection.

[0015] As an option, the processing module may include a baseband unit and an MCU unit, and the baseband unit may be configured to be used to realize distance dimension FFT processing and inter-frame FFT processing in the method described in any embodiment of the present application, and the MCU unit may be configured to be used to realize frame data accumulation and target detection in a region of interest in the method described in any embodiment of the present application.

[0016] Optionally, when the method includes digital beam synthesis and frame data accumulation, the baseband unit may be configured to be used to realize the digital beam synthesis, and the MCU unit may be configured to be used to realize the frame data accumulation.

[0017] Optionally, the integrated circuit is a millimeter wave chip or a sensor chip.

[0018] An embodiment of the present application further provides an electromagnetic wave sensor, which may include a carrier, an integrated circuit according to any embodiment of the present application mounted on the carrier, and an antenna, wherein the antenna is mounted on the carrier, or the antenna is integrated with the integrated circuit as an integrated device and mounted on the carrier, and the integrated circuit is connected to the antenna and used to transmit the electromagnetic wave signal and / or receive the echo signal.

[0019] An embodiment of the present application further provides a terminal device, which may include a device main body and an electromagnetic wave sensor described in any of the embodiments installed on the device main body, wherein the electromagnetic wave sensor is used for target detection and / or communication, thereby providing reference information for the operation of the device main body.

[0020] An embodiment of the present disclosure further provides a non-transitory computer-readable storage medium having computer-readable instructions stored thereon, the computer-readable instructions, when executed by a processor, causing the processor to perform a method according to any of the embodiments of the present disclosure.

[0021] Other aspects may be understood after reading and understanding the accompanying drawings and detailed description.

[0022] These and other objects, features, and advantages of the present application will become more apparent from the following description of the embodiments of the present application taken in conjunction with the drawings. [Brief explanation of the drawings]

[0023] [Figure 1] 1 is a schematic flow chart illustrating a process for realizing target detection based on SISO-combine in an embodiment of the present application. [Figure 2] FIG. 10 is another flowchart diagram illustrating target detection based on SISO-combine in an embodiment of the present application. [Figure 3] 1 is a flowchart of a target detection method according to an embodiment of the present application; [Figure 4] FIG. 2 is a schematic diagram of an operating cycle of a radar according to an embodiment of the present application. [Figure 5] 1 is a waveform diagram of a detection signal of an FMCW radar according to an embodiment of the present application. [Figure 6] 4 is another flowchart of a target detection method according to an embodiment of the present application; [Figure 7] 4 is another flowchart of a target detection method according to an embodiment of the present application; [Figure 8] 4 is another flowchart of a target detection method according to an embodiment of the present application; [Figure 9] 4 is another flowchart of a target detection method according to an embodiment of the present application; [Figure 10] 1 is a schematic diagram of data readout in a target detection method according to an embodiment of the present application; [Figure 11] 4 is another flowchart of a target detection method according to an embodiment of the present application; [Figure 12] 4 is another flowchart of a target detection method according to an embodiment of the present application; [Figure 13] FIG. 2 is a schematic diagram of data transfer in a target detection method according to an embodiment of the present application. [Figure 14] 4 is another flowchart of a target detection method according to an embodiment of the present application; [Figure 15] FIG. 2 is a schematic diagram illustrating division of preset areas according to a target detection method according to an embodiment of the present application. [Figure 16] 4 is another flowchart of a target detection method according to an embodiment of the present application; [Figure 17] 4 is another flowchart of a target detection method according to an embodiment of the present application; [Figure 18] 10 is a schematic diagram illustrating distribution of target points in a preset area according to the target detection method according to the embodiment of the present application. [Figure 19] 4 is another flowchart of a target detection method according to an embodiment of the present application; [Figure 20] 1 is a schematic flow chart illustrating target detection based on per-channel in an embodiment of the present application; [Figure 21] 1 is a flowchart of a target detection method according to an embodiment of the present application; [Figure 22] 4 is another flowchart of a target detection method according to an embodiment of the present application; [Figure 23] 4 is another flowchart of a target detection method according to an embodiment of the present application; [Figure 24] 1 is a flowchart illustrating a target detection method according to an embodiment of the present disclosure combined with a multi-frame joint processing scheme; [Figure 25] 4 is another flowchart of a target detection method according to an embodiment of the present application; [Figure 26] 4 is another flowchart of a target detection method according to an embodiment of the present application; [Figure 27] 4 is another flowchart of a target detection method according to an embodiment of the present application; [Figure 28] 3A and 3B are schematic diagrams illustrating outputs according to a target detection method according to an embodiment of the present application. [Figure 29] FIG. 1 is a schematic flow chart illustrating a method for realizing target detection based on a combination of frame-level FFT and DAE CFAR in an embodiment of the present application. [Figure 30]FIG. 4 is a schematic diagram of azimuth or elevation CFAR based on the target detection flow shown in FIG. 3. [Figure 31] 1 is a flowchart of a target detection method according to an embodiment of the present disclosure. [Figure 32] FIG. 1 is a schematic flow chart illustrating target detection based on a combination of frame-level FFT and DAE CFAR in an embodiment of the present disclosure. [Figure 33] FIG. 10 is a schematic diagram of the result after noncoherent integration. [Figure 34] FIG. 10 is a partial view of a noncoherent integration result. [Figure 35] FIG. 10 is a schematic diagram of target points in six regions in an embodiment of the present disclosure. [Figure 36A] The processing results for a single-person scene (baby in aisle C) are shown. [Figure 36B] The processing results for a single-person scene (baby in aisle C) are shown. [Figure 36C] The processing results for a single-person scene (baby in aisle C) are shown. [Figure 36D] The processing results for a single-person scene (baby in aisle C) are shown. [Figure 37A] shows the processing results for a two-person scene (baby in seat B, adult in seat A). [Figure 37B] shows the processing results for a two-person scene (baby in seat B, adult in seat A). [Figure 37C] shows the processing results for a two-person scene (baby in seat B, adult in seat A). [Figure 37D] shows the processing results for a two-person scene (baby in seat B, adult in seat A). [Figure 38] FIG. 10 is a schematic flow chart illustrating post-processing of a target in an embodiment of the present application. [Figure 39] 1 is a schematic flow chart illustrating post-processing of a target in combination with a DL algorithm in an embodiment of the present application. [Figure 40]1 is a schematic flowchart of a target detection method combined with a DL algorithm in an embodiment of the present application. [Figure 41] 10 is a schematic flowchart of another target detection method combined with a DL algorithm in an embodiment of the present application. [Figure 42] 10 is a schematic flowchart of yet another target detection method combined with a DL algorithm in an embodiment of the present application. DETAILED DESCRIPTION OF THE INVENTION

[0024] To facilitate understanding of the present application, the present application will be described in more detail below with reference to the accompanying drawings. The drawings illustrate preferred embodiments of the present application. However, the present application is not limited to the embodiments described herein and may be embodied in various forms. The purpose of providing these embodiments is to provide a more complete understanding of the contents of the present application.

[0025] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. The terms used in this specification are for the purpose of describing specific examples only and are not intended to be limiting of this application.

[0026] An embodiment of the present disclosure provides a target detection method, which includes steps S1 to S3. S1: Frame data accumulation is performed on 1D-FFT data, and the 1D-FFT data is acquired by performing distance-dimensional FFT processing on echo signals. S2: Perform FFT processing between frames based on the accumulated frame data to obtain the RD spectrum. S3, realizing target detection in the region of interest based on the RD spectrum.

[0027] For example, all of the 1D-FFT data after the 1D-FFT processing may be subjected to inter-frame accumulation and inter-frame FFT processing, or only a portion of the 1D-FFT data may be subjected to inter-frame accumulation. For example, if the region of interest is smaller than the range of the echo data, distance-dimensional FFT processing may be performed on only a portion of the echo signals to obtain 1D-FFT data, and then further inter-frame accumulation and inter-frame FFT processing may be performed, or only a portion of the 1D-FFT data may be subjected to inter-frame accumulation and inter-frame FFT processing.

[0028] For example, the distance-dimension FFT processing and the inter-frame FFT processing may be performed simultaneously, for example, the distance-dimension FFT processing may be performed continuously at the same time as the inter-frame FFT processing.

[0029] When target detection is performed in a sealed or relatively sealed space, in some alternative embodiments, target detection can be achieved by performing region determination based on a point cloud obtained by single-frame high-speed time processing, CFAR (Constant False-Alarm Rate) detection, and angle measurement; or by performing operations such as DoA (Direction of Arrival) estimation using an algorithm such as Capon after single-frame high-speed time processing to form a range-azimuth (RA) map, and then extracting, recognizing, and identifying features such as energy within a preset region to achieve target detection; or by detecting human breathing and heartbeats after filtering the phase of received echoes. For example, when target detection is achieved by performing region determination based on a point cloud obtained by single-frame high-speed time processing, CFAR detection, and angle measurement, 1D-FFT (e.g., range-dimensional FFT) processing on the echo signal may be performed, followed by a time correlation analysis based on multiple echo data to achieve human target discrimination; and then position determination may be performed with reference to point cloud information, ultimately achieving the goal of detecting targets within the cabin. For example, when target detection is achieved by performing area determination based on the point cloud obtained by single-frame high-speed and low-speed time processing, CFAR detection, and angle measurement, 1D-FFT (e.g., distance-dimensional FFT) processing on the echo signal can be performed, and then temporal correlation analysis can be performed based on multiple echo data to achieve human target discrimination. After that, position determination can be performed by referring to point cloud information, etc., to ultimately achieve the goal of detecting targets inside the cabin.

[0030] In some alternative embodiments of the present application, another target detection and / or recognition scheme is proposed, namely, a scheme for detecting targets in enclosed spaces such as cabins, rooms, factory buildings, etc. using an inter-frame accumulation method, which can effectively improve the detection rate, reduce the number of false alarms, and significantly improve the accuracy of angle estimation, etc., thereby enabling accurate detection of special or weak targets such as infants in cabins, and realizing applications such as CPD (Child Presence Detection), SBR (Safety Belt Reminder), etc. In the implementation process, accurate detection of targets in cabins may be achieved based on one or a combination of at least two of multi-frame joint processing technology, deep learning detection, positioning and recognition technology, etc.

[0031] The multi-frame joint processing scheme may involve performing a sliding window FFT on multi-frame data to obtain a range-Doppler spectrum, or FIR (Finite Impulse Response) or other complex time-frequency transform processing, and then performing CFAR, DOA (Direction of Arrival), etc., on the region of interest. Then, by referring to the set region determination logic and region parameters, etc., detection and positioning of living objects such as adults, children, pets, etc., or other non-living objects can be achieved. CFAR may be Doppler-dimensional NR-CFAR, RD-CFAR, or DAE (Doppler-Azimuth-Elevation)-CFAR, etc. At the same time, post-processing such as clustering, false alarm suppression, and multiple point cloud-related processing may be performed after CFAR and DoA. The region parameters may be determined by at least one or a combination of at least two of the following operations: clustering the point cloud detected after performing a certain sliding window multi-frame processing; detecting and removing outliers from the point cloud detected after performing a certain sliding window multi-frame processing; etc.

[0032] When applied to a closed or relatively closed spatial region, the spatial region may be divided in advance to define and detect different regions of interest (divided regions). For example, in the case of target detection in a vehicle cabin, the region monitored by the radar may be divided into a seat section, an aisle section, etc., and then corresponding parameter types and thresholds may be pre-set for different types of regions, and then combined with corresponding processing steps, accurate detection of targets of interest in specific regions may be achieved.

[0033] In some alternative embodiments, for a family car, the interior space may generally be simply divided into a head space, a rear space, and a trunk space. If the rear space is a key monitoring area, the rear space may be further divided into a seat section and an aisle section, etc. At the same time, the seat section may be divided into a corresponding number of seat section units based on the seats. Similarly, the aisle section may be divided into a corresponding number of aisle section units corresponding to the seat section units. For example, in a five-seat family car, the three seats in the rear space may be divided into three seat section units and three corresponding aisle section units. At the same time, corresponding parameter types and thresholds may be preset for different types of areas (or sections or section units), and adaptive signal data processing methods and steps may be adopted to accurately detect a target of interest (specific target) in a specific section or section unit. Adjacent section units may have a partial overlapping area, or may be adjacent or have a preset gap width.

[0034] When combining a detection, location, and recognition method with deep learning, it can be realized by performing 1D-FFT (e.g., range dimension FFT) processing on the echo signal, followed by multi-frame sliding window FFT, and then N-point 2D-DBF processing to form an N-channel RD (Range-Doppler) spectrum, and then using a deep learning model to perform monitoring, location, and recognition operations based on the N-channel RD spectrum; or by performing 1D-FFT processing on the echo signal, followed by N-point 2D-DBF processing, and then using a deep learning model to perform monitoring, location, and recognition operations based on the N-channel RD spectrum; or by first performing 1D-FFT processing on the echo signal, followed by N-point 2D-DBF processing, and then multi-frame sliding window FFT to form an N-channel RD spectrum, and then using a deep learning model to perform monitoring, location, and recognition operations based on the N-channel RD spectrum. The above N may be the number of section units. For example, if a rear section having three rear seats is divided into three seat section units and three corresponding aisle section units, then N may be 6.

[0035] According to the above solution, after testing with actual collected data, it can effectively achieve a relatively high detection rate and extremely low false alarm and false miss rates in the application scenarios of top-mounted radar installation. For example, when detecting targets inside a cabin, it can achieve a detection rate of over 99%, a false miss rate of less than 0.5%, and a false alarm rate of less than 1%.

[0036] The following describes in detail the technical solution of the present application with reference to the drawings, taking the frame-level Fourier transform as an example. That is, after performing a fast time-dimension Fourier transform on the chirp in the echo signal, at least two frames of fast time-dimension Fourier transform data are accumulated, and then a frame-dimension Fourier transform is performed on the at least two frames of data to obtain a Range-Doppler (abbreviated as RD) spectrum, and then the range and / or velocity of the target are estimated based on the RD spectrum.

[0037] 1 is a schematic flowchart illustrating target detection based on SISO-combine in an embodiment of the present application. As shown in FIG. 1, after performing operations such as ADC conversion (analog-to-digital conversion) and sampling on the echo signal, range DC removal and range Fourier transform (Range FFT, 1D-FFT, or fast time-dimensional FFT) are sequentially performed. After that, multi-frame accumulation (e.g., storing 128 frames of data) is performed. Next, Doppler DC removal, frame FFT, and non-coherent integration are performed. Further, a certain false alarm probability detection (e.g., peak detection with peak selection (CFAR)) is performed based on the noise obtained by noise estimation (e.g., using a noise variance estimator). Finally, angle detection and target classification are performed, thereby obtaining target range, velocity, and / or angle information. For example, azimuth and elevation estimation may be performed first based on the CFAR results, and may be achieved using techniques such as DBF (digital beam forming) and DoA (direction of arrival estimation).

[0038] In some alternative embodiments, for an electromagnetic wave sensor having a BB (Baseband) unit and an MCU (Microcontroller Unit) unit, operations such as frame data storage (store 128 frames) and target classification (Region HIST classification) may be performed in the MCU module based on the target detection flowchart shown in FIG. 1 , while range dimension DC component removal (Range DC removal) and Doppler dimension DC component removal (Doppler DC removal) may be performed in the BB module or the MCU module. The flow shown in FIG. 1 illustrates an example in which DC filtering is performed on downlink ADC data in the BB module.

[0039] As shown in FIG. 1, after performing 1D-FFT processing in the BB module to obtain distance dimension information, the 1D-FFT data may be cached in the MCU module. After caching up to a preset amount of data (e.g., 128, 56, or 32 frames), the cached data for the preset number of frames may undergo DC removal and frame-level FFT in the frame dimension to obtain the Range-Doppler (RD) spectrum.

[0040] If the system includes multiple channels, non-coherent integration may be further performed on the RD spectrum of the multiple channels, as shown in Figure 1, and the noise floor of each range bin in the accumulated data may be estimated using an NVE module. For example, the noise floor estimation may be performed based on a preset formula, and subsequent NR (Noise Reference)-CFAR processing may be performed based on the estimated noise floor value. The above preset formula is i =min(n i ,α*n g ), and n iis the noise floor estimate for the ith range bin, and n g is the noise floor estimate for the last range bin of interest, and α is a coefficient that may be set and updated based on demand or process data, experience, etc.

[0041] For angle estimation, as shown in Figure 1, CFAR detection point data may be subjected to azimuth dimension DBF and DoA, and elevation dimension DBF and DoA, respectively, to obtain azimuth and elevation angle estimation data for each CFAR detection point. Also, target points detected in each frame (or a preset amount of data, i.e., each time) may be counted using a preset (or divided) area unit design, and the counting results may be judged using a preset rule to determine whether a physical target of interest (e.g., an adult, child, infant, or pet) exists in that processing time and determine its location area.

[0042] In the above embodiment, a frame-level FFT is used instead of the conventional chirp-level Doppler FFT to determine target velocity and achieve accurate detection of targets of interest. Multi-frame sliding-window FFT processing can further improve the update frequency of results and further enhance the real-time performance of the system. Processing can also be performed only on the range and / or Doppler regions of interest to reduce the amount of data processed. Furthermore, background noise estimation can be performed by taking the minimum value based on the current range bin estimate and the last range bin estimate of interest, thereby better adapting to application scenarios in relatively sealed environments, such as the interior of a vehicle or cabin. Region logic determination involves counting detected target points in each frame using a preset region design, and then judging the count results using preset rules to accurately determine whether a target exists within each preset region.

[0043] Figure 2 shows an example of digital signal processing implemented by another target detection method applied to a SISO-combine radar.

[0044] The radar receives echo signals via an antenna and performs processes such as analog-to-digital conversion and sampling to obtain digital signals. The digital signals are then processed by a master control module (e.g., a microcontroller unit (MCU)) and a baseband (BB) module (e.g., a baseband chip) shown in FIG. 2 (taking target detection by a frequency modulated continuous wave (FMCW) radar as an example) to achieve target detection. For example, in FIG. 2, the digital signals undergo range DC removal and range Fourier transform (Range FFT, also referred to as 1D-FFT or fast time-dimensional FFT) in the baseband module. The baseband module may then perform further processing on the data, such as complex averaging on multiple chirp data in one frame, or, of course, no further processing may be required.The signal processed by the baseband module is then sent to the master control module for accumulation of multiple frames. After the data has been accumulated up to a preset number of frames, for example, 128 frames, the master control module transfers the accumulated multiple frames of data from the static random-access memory (SRAM) of the CPU to the baseband module. The baseband module then performs Doppler DC removal, frame FFT, and inter-channel accumulation (non-coherent integration processing, channel accumulation, etc.). The baseband module then performs constant false alarm probability detection (for example, peak detection with peak selection (CFAR)) based on the noise obtained by noise estimation (for example, using a noise variance estimator). Finally, angle detection and target classification are performed, thereby obtaining target range, speed, and / or angle information, etc. When detecting the angle, azimuth angle (Azimut) estimation and elevation angle (Elevation) estimation may be performed first based on the CFAR results, which may be achieved by techniques such as Digital Beam Forming (DBF) and wave arrival direction estimation.

[0045] The implementation of the digital signal processing shown in FIG. 2 will now be described.

[0046] The target of distance dimension DC component removal is the data corresponding to each chirp in each frame data. In this case, distance dimension DC component removal averages the data collected in each receive (RX) channel of each chirp along the fast time dimension, and then subtracts the DC component from all sampling points of each RX channel.

[0047] The distance dimension FFT may be realized by performing window processing on the data after removing the distance dimension DC component, and then performing 1D-FFT.

[0048] During the target detection process, the radar performs ADC or sampling processing based on the echo signal to acquire each chirp data in each frame of data, which is then sent to the master control module after undergoing the above-mentioned operations. Sending data to the master control module may be achieved by transferring the data to the CPU SRAM via direct memory access (DMA).

[0049] The Doppler dimension DC component removal may be achieved by calculating a complex average along the frame dimension for the multi-frame data stored in the master control module (i.e., averaging data on the same distance unit (bin) of the same channel in different frames), obtaining the average value of different distances of different channels as the DC component of the corresponding distance unit of the corresponding channel, and subtracting the respective DC components for each distance unit of each channel.

[0050] The inter-frame Fourier transform may be realized by performing window processing along the frame dimension on the data after removing the DC component in the Doppler dimension, and then performing a 2D-FFT along the frame dimension, thereby obtaining the range-Doppler information of each transmit and receive channel.

[0051] Inter-channel accumulation may be realized by the following equation, taking non-coherent integration as an example: JPEG2025530621000002.jpg980 or JPEG2025530621000003.jpg980where, TIFF2025530621000004.tif7150 are TIFF2025530621000005.tif6150th distance unit and TIFF2025530621000006.tif6 is the power and amplitude in 150th Doppler unit, TIFF2025530621000007.tif6150 TIFF2025530621000008.tif6150th distance unit TIFF2025530621000009.tif6150th Doppler unit, TIFF2025530621000010.tif6150th transmission channel and TIFF2025530621000011.tif6 is a complex value obtained by subjecting the signal echo in the 150th receiving channel to inter-frame Fourier transform.

[0052] Constant false alarm probability detection based on the noise floor obtained in the noise estimate has already been described in conventional CFAR techniques and will not be described in further detail here.

[0053] Angle detection may be achieved by performing azimuth dimension digital beam forming (DBF) and DOA on the target point acquired by CFAR, and elevation dimension DBF and DOA, which have already been described in the conventional DBF and DOA techniques and will not be described in further detail here.

[0054] Target classification may be achieved by counting the target points detected in each frame (or a preset amount of data, i.e., each time) using a preset (or divided) area unit design, and judging the counting result using a preset rule, thereby obtaining whether a physical target of interest (e.g., an adult, a child, an infant, or a pet) exists in that processing time, and determining the location area where it is located.

[0055] 2 is only an example for a radar having a baseband module and a master control module, i.e., operations such as frame data accumulation and target classification (which can be realized using Region HIST classification, etc.) are performed in the master control module, and the remaining operations, such as range dimension DC component removal and Doppler dimension DC component removal, are performed in the baseband module. In other embodiments, frame data accumulation may also be performed in the baseband module, and Doppler dimension DC component removal, etc. may also be performed in the master control module.

[0056] Although FIG. 2 illustrates an example of caching only 128 frames of data, in other embodiments, 56 frames of data or 32 frames of data may be cached. The embodiments of the present application are not limited to this, and may be determined based on demand, hardware support capabilities, etc. In this manner, the cached multi-frame data is subsequently subjected to Doppler dimension DC component removal and inter-frame FFT, thereby obtaining a range-Doppler (RD) spectrum.

[0057] In the flow shown in FIG. 2 , some steps, for example, distance dimension DC component removal or distance dimension DC component removal or window processing, may be skipped, some steps may be simplified, for example, noncoherent integration may be skipped and CFAR detection may be performed on only one of the channels, or, for example, only some channels may be selected and noncoherent integration may be performed and CFAR detection may be performed on only these channels.

[0058] To help those skilled in the art better understand the flow shown in FIG. 2, the flow of different target detection methods will be described below.

[0059] In some embodiments, the flow of the target detection method includes the following steps 201 to 203, as shown in FIG.

[0060] In step 201, distance dimension FFT is performed on the frame data.

[0061] In step 202, target data is extracted from the frame data acquired by the distance dimension FFT processing.

[0062] In step 203, window processing is performed on the target data based on the sliding window, and digital signal processing is performed on the windowed data.

[0063] The length of the sliding window has a time length of the same order as the period of the periodic motion of the target.

[0064] To help those skilled in the art better understand the target detection method described in the embodiment shown in FIG. 3, the steps are described below.

[0065] In step 201, frame data refers to data corresponding to an echo signal formed when one frame of detection signals is reflected by a target. As can be understood, a target detection operation by a radar typically has a fixed cycle, as shown in FIG. 4. Within one detection cycle, the radar first transmits one frame of detection signals. Taking frequency-modulated continuous wave (FMCW) radar detection as an example, as shown in FIG. 5, one frame of detection signals is composed of multiple chirp signals (though there is no idle time between the chirp signals shown in FIG. 5, this is merely an example; in some cases, there may be some idle time between the chirp signals). Simultaneously with the start of transmission of the detection signals, the radar begins preparation to receive the echo signals returned by the detection signals reflected by the target. The echo signals corresponding to one frame of detection signals undergo the aforementioned analog-to-digital conversion, range-dimensional DC component removal, and other processes, resulting in the above-mentioned frame data. Furthermore, each frame of data undergoes the aforementioned range-dimensional FFT to achieve step 201, which will not be described in further detail here.

[0066] In step 202, the embodiment of the present application does not impose any limitation on the target data, and the target data may be any data content corresponding to the frame data. For example, when storage resources and computing power are sufficient, the extraction of target data may be to extract all data. Alternatively, when maximizing the real-time performance of target detection, at least a portion of the data or at least a portion of pre-processed data (such as data obtained by pre-processing such as complex averaging, complex weighted averaging, or complex summing) may be extracted. The target data may be extracted according to demands and hardware conditions. For ease of understanding, different implementation methods for extracting a portion of data as target data will be described below.

[0067] In some embodiments, as shown in FIG. 6, extracting target data from frame data acquired by distance dimension FFT processing is achieved by the following step 2021.

[0068] In step 2021, data corresponding to one or a specified index chirp period (e.g., the second chirp period in each frame) is extracted as target data from each frame data acquired by distance-dimension FFT processing, and if it is decided to adopt data corresponding to the preset rule chirp period as target data, the rule for selecting a chirp in each frame is the same.

[0069] In other words, by using the data corresponding to one chirp period as frame data, target data processing is simple and easy to implement, the selected data is more realistic, target detection can be performed more efficiently, and this is advantageous for improving the real-time nature of target detection.

[0070] In some embodiments, as shown in FIG. 7, extracting target data from frame data acquired by distance dimension FFT processing may be further achieved by the following step 2022.

[0071] In step 2022, parameters corresponding to each distance unit in each frame data are determined based on each frame data acquired in the distance dimension FFT processing, and target data is generated based on the parameters.

[0072] That is, by compressing the multiple data of each distance unit in the frame data into corresponding parameters, the overall characteristics of the frame data can be maintained, so that when later performing target detection based on the target data, the information that can be referenced is more comprehensive, and the results obtained are more accurate.

[0073] Of course, the above embodiments are merely examples provided to reduce the resource occupation of target data. In some embodiments, target data extraction may be performed taking into account data availability. For example, radar signals can usually cover a relatively wide range, but not all of the radar coverage is of interest, and processing data corresponding to these areas would result in a waste of resources. Therefore, in some embodiments, as shown in FIG. 8, extracting target data from frame data acquired by range-dimension FFT processing may be further achieved by the following step 2023:

[0074] In step 2023, data within a preset distance range is extracted as target data from the frame data acquired by the distance dimension FFT processing.

[0075] That is, the range of interest in the frame data (represented by a preset distance range) is used as target data, which allows accurate target detection information to be maintained and reduces the storage resources, calculation resources, etc. occupied by the target data, thereby achieving a balance between the accuracy and real-time nature of target detection.

[0076] In addition, in addition to the method shown in FIG. 8, retaining data within the range of interest can also be achieved in some embodiments by performing window processing on the target data based on a sliding window, as shown in FIG. 9, and then performing digital signal processing on the windowed data, which can be achieved by the following step 2031.

[0077] In step 2031, window processing is performed on the target data based on a sliding window, and digital signal processing is performed on data within the preset Doppler range in the windowed data.

[0078] This also achieves the same effect as in the embodiment shown in Figure 8, but the dimensions of both data are different, one is the distance dimension and the other is the Doppler dimension, so the method of selecting the range of interest is different. Of course, it is also possible to extract target data in the distance dimension as in the embodiment shown in Figure 7, and extract data in the Doppler dimension as in the embodiment shown in Figure 9, and then perform digital signal processing, etc., and this will not be described in further detail here.

[0079] Of course, the above is merely an example of how step 202 can be implemented, and in some embodiments it may be implemented in other ways, such as modulo-calculating distance units in pairs and using the larger distance unit as the target data (similar to max-pooling), which will not be described in further detail here.

[0080] In step 203, the method of window processing is not limited, and for example, the window may be a sliding window, and further, the sliding window may be a fixed-length window, or an indefinite-length window, etc., and no further detailed explanation will be given here. For ease of understanding later, a fixed-length sliding window may be used as an example, but this does not mean that only a fixed-length sliding window can be used; for example, an indefinite-length window that changes according to changes in the target's periodic movement, such as respiratory frequency, may also be used.

[0081] In some embodiments, as shown in FIG. 10, for example, if the window length of the sliding window is 128 frames for the 1st to 129th frame data generated after radar activation, the data acquired the first time by the sliding window is the 1st to 128th frame data (data in the shaded part of the first row in FIG. 10), and the data acquired the second time is the 2nd to 129th frame data (data in the shaded part of the second row in FIG. 9), i.e., the sliding step length of the sliding window is 1 frame.

[0082] Of course, the above is merely an example, and in other embodiments, other parameters may be adopted for the window length and sliding step length of the sliding window, and they will not be listed here.

[0083] Step 203 is not limited to data signal processing, and it is understandable that different processing methods may require different data to be acquired for different needs. For example, in some embodiments, detecting target points may achieve the effect of target detection, and in some embodiments, target classification may be performed to acquire targets with greater accuracy, reliability, and referenceability, as shown in Figure 2. This will not be detailed here. The following description will be given using the target detection flow shown in Figure 2 as an example, but this does not necessarily mean that target classification must be performed after processing such as CFAR, as shown in Figure 2.

[0084] As shown in Figure 2, the digital signal processing process for target detection typically includes constant false alarm probability detection, and one typical constant false alarm probability detection is constant false alarm probability detection based on the noise floor. It can be understood that the more accurate the noise floor estimation, the better the constant false alarm probability detection effect and the more accurate the target detection result. Therefore, in some embodiments, as shown in Figure 11, windowing the target data based on a sliding window and performing digital signal processing on the windowed data may be achieved by the following steps 2032 and 2034.

[0085] In step 2032, windowing is performed on the target data based on a sliding window, and a noise floor estimation is performed on the windowed data within a preset range, and the upper bound of the preset range is determined based on the noise floor estimation result of the farthest distance unit.

[0086] Step 2034 performs constant false alarm probability detection based on the estimated noise floor.

[0087] That is, the noise floor estimation is constrained by setting a preset range, whereby an upper bound is constructed based on the noise floor estimation result of the farthest distance unit, which is beneficial to improving the accuracy of the noise estimation.

[0088] In some examples, noise floor estimation within a preset range is achieved by the following equation: n i '=min(n i ,α*n g ) n i ' is the noise floor estimation result within the preset range of the i-th distance unit, and n i is the initial noise floor estimation result of the i-th distance unit, α is a preset parameter, TIFF2025530621000012.tif7150, n gis the initial noise floor estimate for the furthest distance unit.

[0089] The preset parameter α may be set and updated based on demand or process data, experience, and the like.

[0090] Furthermore, the above is merely an exemplary description of noise floor estimation. In some embodiments, the initial noise floor estimation result of the i-th distance unit may be directly selected as the noise floor result used in the case of CFAR. Alternatively, the minimum, maximum, quantile, average, median, etc. of the initial noise floor estimation results of the last few regions of interest may be selected as the noise floor estimation. Alternatively, when estimating the noise floor, only some of the Doppler units of a distance unit may be selected rather than all of the Doppler units. No further method of noise floor estimation will be listed here.

[0091] Furthermore, although the radar shown in FIG. 2 is a SISO-combine system, if the radar includes multiple channels, inter-channel accumulation, such as non-coherent integration or related accumulation, may be performed on the multi-channel RD spectrum, and the noise floor of each range bin in the accumulated data may be estimated using an NVE module. The noise floor estimation method may employ any of the noise floor estimation methods described above, and will not be described in further detail here.

[0092] In step 203, the data is windowed, and if the data is out of order, the temporal correlation of the data is destroyed and the accumulation of a certain amount of motion is not reflected, which is detrimental to subsequent target detection. Meanwhile, as shown in FIG. 2, data is accumulated in the master control module. Once a certain amount of data is accumulated, the baseband module reads the data for further processing. In this process, the data write and read orders may differ depending on the storage method. In some embodiments, the data must be rearranged to ensure the correct order. For example, in some embodiments, it is assumed that the data is cached using a Ring FIFO. In this case, as shown in FIG. 12, the target detection method further includes the following step 204:

[0093] In step 204, data is read from the Ring FIFO and the read data is rearranged.

[0094] This allows for data rearrangement to improve subsequent processing efficiency.

[0095] To enable those skilled in the art to better understand the above data storage and rearrangement, the following description will be made with reference to FIG.

[0096] As shown in FIG. 13, if it is assumed that the data in the range of interest (e.g., from the 5th distance unit to the 20th distance unit) among the data acquired after the baseband module (denoted by BB in the figure) performs distance-dimensional FFT on the frame data belongs to the target data, then a two-dimensional matrix as shown in FIG. 13 needs to be accumulated in the master control module (denoted by MCU in the figure), with the rows of the matrix corresponding to the frame numbers and the columns of the matrix corresponding to the distance units.

[0097] Assuming that the master control module employs a Ring FIFO to store the above two-dimensional matrix, the storage process may be as follows:

[0098] Initialize one CPU SRAM and f Each column has N r ×N r x×N tx can store a number of f is the maximum number of frames for which inter-frame FFT is designed, and N r , N rx , N tx are the number of distance units of interest, the number of transmit (Tx) channels, and the number of receive (Rx) channels, respectively. The cache may be initialized to all 0s at initialization, and the FIFO head pointer (p_head) points to the beginning of the 0th column of the Ring-FIFO.

[0099] At the 0th frame, the corresponding data (for each distance unit, each Tx channel, and each Rx channel) is cached in the 0th column of the Ring-FIFO, and then p_head points to the 1st column.

[0100] Similarly, for the k-th frame data (k=1, 2, . . . , 126), the corresponding data is cached in the k-th column of the Ring-FIFO, and then p_head points to the k+1-th column.

[0101] At the 127th frame, the corresponding data is cached in the 127th column of the Ring-FIFO. At this time, 128 frames of data are stored, so the data in the Ring-FIFO needs to be moved to the baseband module to perform the inter-frame FFT processing mentioned above. In this case, the data from the column pointed to by p_head to the 127th column is moved first, and then the data from column 0 to the column pointed to by p_head is moved. Obviously, at this time, the data read order and the data write order are different, and this needs to be restored, that is, the data needs to be rearranged, so that the data is distributed continuously in the frame dimension, thereby improving the calculation efficiency of the baseband module.

[0102] After the transfer is completed, the pointer p_head of the Ring-FIFO points back to column 0. Similarly, in a new data storage and transfer process, the data of the 128th frame is stored in column 0 of the Ring-FIFO, and during the transfer, the data of frames 1 to 127 are first transferred to the baseband module, and then the data of column 0 is transferred to the baseband module, and the subsequent frames follow the same rule, which will not be described in detail here.

[0103] In the above example, inter-frame processing is performed after the Ring-FIFO is completely filled with valid data, but in other embodiments, once some frame data, for example, data from frames 0 to 63, is stored, the data in the Ring-FIFO can be moved to the baseband module in a similar manner to the above to perform an inter-frame FFT, etc. In this case, since columns 64 to 127 are all initial values ​​given at initialization, this can correspond to an inter-frame FFT in which the baseband module pads zeros before the data sequence. Of course, the above is merely an illustrative explanation and does not mean that 128 frame data must be used to perform the inter-frame FFT or that a Ring-FIFO must be used for storage, and so no further detailed explanation will be given here.

[0104] Also, as shown in FIG. 2, if the user desires to obtain more intuitive and accurate results, in some embodiments, the following steps 205-206 are also performed after acquiring the target, as shown in FIG. 14.

[0105] In step 205, targets in each preset region are verified based on targets that fall within each preset region, and the preset regions are obtained by dividing the detection space.

[0106] In step 206, the detection results for each preset region are output based on the targets that have passed the verification.

[0107] That is, considering that for a target that has a certain volume and occupies a certain space, multiple target points are usually detected, the currently detected target is used to continue combining region division to perform target verification, thereby improving the detection result, and based on the division of the detection space in the application scenario, the detection result of each preset region is output, so that the target distribution within the preset region appears, which is more intuitive and accurate, and is convenient for users to make decisions based on the output result, resulting in a better user experience.

[0108] To help those skilled in the art better understand the embodiment shown in FIG. 14, the steps are described below.

[0109] In step 205, the detection space and the preset area are not limited and may vary according to different application scenarios, application needs, etc. For example, in an application scenario of an on-board radar, the detection space may be the interior of a vehicle, and the preset area in this case may include at least one of an area corresponding to a seat and an area corresponding to an aisle (footrest), thereby allowing subjects such as a driver or an adult to better perceive the situation inside the vehicle. Also, in a factory work scenario, for example, the detection space may be a factory building, and the preset area may include each employee's workspace, thereby avoiding production risks caused by employees not being able to monitor the operating status of machines because they are not in their workspaces. This will not be described in further detail here. For ease of understanding, the following description will use the above interior space as an example, but this does not mean that the corresponding solution can only be realized inside a vehicle.

[0110] Taking the interior of a vehicle as an example of the detection space, the interior space of the vehicle is abstracted into a coordinate system as shown in Figure 15, where the abscissa of the coordinate system indicates the azimuth angle relative to the radar installed in the vehicle, and the ordinate of the coordinate system indicates the elevation / depression angle relative to the radar installed in the vehicle. In this case, the interior of the vehicle is divided into a total of six preset areas, including the three seats in the rear row and the three aisles in front of the three seats, that is, different filling areas as shown in Figure 15. Depending on needs, as shown in Figure 15, the different preset areas may or may not overlap with each other, and some areas in the vehicle may belong to multiple preset areas at the same time, or may not belong to any area.

[0111] Note that Figure 15 is just one example of a method for abstracting the detection space, and in some embodiments, the detection area may be abstracted from all or any one of the dimensions of distance, azimuth, and elevation / depression angles, or from all or any one of the dimensions along the x, y, and z dimensions of a Cartesian coordinate system, and the preset area may be further divided, which will not be described in further detail here.

[0112] In step 205, the verification method is not limited, and verification may be performed based on, for example, the number of target points, the occupancy rate, or the like.

[0113] For example, in some embodiments, as shown in FIG. 16, validating the targets of each preset region based on the targets that fall within each preset region may be accomplished by the following step 2051: In step 2051, targets in each preset region are verified based on the occupancy rate of targets falling within each preset region among detected targets.

[0114] Further for example, in some embodiments, as shown in FIG. 17, validating the targets of each preset region based on the targets that fall within each preset region may be accomplished by the following step 2052. In step 2052, targets in each preset region are verified based on the signal-to-noise ratio of targets falling within each preset region.

[0115] Of course, the above are merely examples, and in some cases targets at other locations may be verified based on the situation where the target point falls at a known target location, such as the driver's seat, and will not be described in further detail here.

[0116] In step 206, the output method is not limited, and a point cloud may be output directly for the detection results of each preset area, or the divided preset areas may be represented and a point cloud for each preset area may be output at the same time. No further details will be listed here.

[0117] To help those skilled in the art better understand the above embodiment, the following description will take occupancy verification as an example.

[0118] Suppose that a total of 11 target points are detected in a target detection process, and the division of the preset area is as shown in FIG. 15, and at the same time, the distribution of the target points in the preset area is as shown in FIG. 18, namely, the area is seat A (6 target points), seat B (2 target points), seat C (0 target points), aisle A (0 target points), aisle B (1 target point), aisle C (0 target points), and in FIG. 18, the target points are shown as solid circles.

[0119] The algorithm obtained according to the above embodiment is then as follows: The flag bits flag_region_i in the six regions, which indicate whether a person is present or not, are all initialized to 0, i.e., there is no person in any of them (i=0, 1, ..., 5). If the total valid target number total_valid_tgt_num is 0, it is determined that there is no one in the car and the process ends. Otherwise, the following operations are performed: All regions are traversed, and if the statistical value tgt_num_region_i of the number of valid targets belonging to the region i exceeds 25% of the total number of valid targets total_valid_tgt_num, the flag bit flag_region_i of the region i is set to 1; If there is a region where flag_region_i is 1, set flag_region_empty to 0, i.e., there is a person in the vehicle. The system patrols the seating areas, and if any of the seating areas flag_region_i is 1, it determines that there is no person in the corresponding aisle area (for example, if it determines that there is a person in seat A, it determines that there is no person in aisle A), Output the results.

[0120] At this time, the output result is as shown in FIG. 18, where six target points are displayed in area seat A, and there are no people in the remaining preset areas.

[0121] It should be noted that the threshold value of 25% in the above example is merely an example, and other threshold values ​​or other criteria may be used in other embodiments, which will not be described in further detail here.

[0122] It can also be understood that in the detection process, different periodic motions may need to be observed in combination, so that the target detection result can be more accurately obtained. Accordingly, in some embodiments, as shown in FIG. 19, the target detection method includes the following steps 1801 to 1803:

[0123] In step 1801, distance dimension FFT is performed on the frame data.

[0124] In step 1802, target data is extracted from the frame data acquired by the distance dimension FFT processing.

[0125] In step 1803, window processing is performed on the target data based on sliding windows corresponding to the periods of different biometric characteristic parameters of the living body, and the windowed data corresponding to the sliding windows of different lengths is digitally processed.

[0126] The periods of the biometric characteristic parameters corresponding to different sliding windows are different, and the length of each sliding window has a time length of the same order as the period of the biometric characteristic parameters.

[0127] Steps 1801 and 1802 are almost the same as the above-mentioned steps 201 and 202, but the difference is that the following steps 1803 and 203 describe detection based on multiple different periodic movements, while the other describes only one. As can be seen, when detection is based on multiple different periodic movements, data accumulation continues even after short-period data accumulation is completed, and processing of the data is completed when long-period data accumulation is completed. For example, assuming that one periodic motion corresponds to 32 frames and the other periodic motion corresponds to 128 frames, and that both need to be observed, the target detection process will begin outputting one detection result once acquisition of the target data corresponding to the 32nd frame data is complete, and then output one new detection result after acquisition of the target data corresponding to the 33rd frame data...and when acquisition of the target data corresponding to the 128th frame data is complete, two detection results (one detection result corresponding to 32 frames of data and the other detection result corresponding to 128 frames of data) or a result obtained by combining and processing these two results will be output.

[0128] In this way, by obtaining the period of the biometric characteristic parameters of the living body, the length of the sliding window can be adjusted in a timely manner, thereby better detecting the living body target and better monitoring the state of the living body (e.g., children, pets, etc.), thereby monitoring and performing processing corresponding to the state of the living body in a timely manner.

[0129] In the embodiments of the present application, the biometric characteristic parameters are not limited. For example, the biometric characteristic parameters may include respiration. Also, for example, the biometric characteristic parameters may include heart rate and / or pulse rate. Of course, the above are merely examples, and other biometric characteristic parameters that can be embodied as movement characteristics may be used according to needs, and will not be listed here.

[0130] In this way, in the above embodiment, by adopting frame-level FFT instead of the conventional chirp-level Doppler FFT, the kinetic energy of the target can be effectively accumulated to achieve accurate detection and measurement, and by performing multi-frame sliding window FFT processing, the update frequency of the results can be further improved, and by performing post-processing such as area statistics on the target points detected by the radar, it is possible to determine whether there is a person in each area.

[0131] In the above embodiment, a frame-level FFT is used instead of the conventional chirp-level Doppler-dimensional FFT to determine target velocity and achieve accurate detection of targets of interest. Multi-frame sliding-window FFT processing can further improve the update frequency of results and further enhance the real-time performance of the system. Processing can also be performed only on the range and / or Doppler regions of interest to reduce the amount of data processed. Furthermore, background noise estimation can be performed by taking the minimum value of the current range unit (bin) estimate combined with the last range unit of interest estimate to obtain background noise, which is more suitable for application scenarios in relatively sealed environments, such as the interior of a vehicle or cabin. Region logic determination involves counting detected target points in each frame using a preset region design, and then judging the count results using preset rules to achieve accurate determination of whether a target exists within each preset region.

[0132] FIG. 20 is a schematic flowchart illustrating target detection based on a per-channel approach in an embodiment of the present application. As shown in FIG. 20, in contrast to the proposal based on the flow structure shown in FIG. 1 in which noncoherent integration is performed on multi-channel RD spectra to generate single-channel data and then CFAR is performed, this embodiment performs CFAR processing on all channels, followed by binary integration in the channel domain, and then estimates the horizontal / azimuth angles and elevation / depression angles. For example, for a system with M channels, the inter-frame FFT shown in FIG. 1 is performed on the multiple channels, and then CFAR processing is performed on the multiple channels to obtain M bit masks, each of which may be of Boolean type, to determine whether a target exists in each range bin and / or Doppler bin (a value of 1 defines presence, and a value of 0 defines absence). The M bit masks may then be cumulatively added, and each element may be defined to have a range of 0 to (M-1). Then, a secondary detection may be performed on the accumulated mask, i.e., each element of the accumulated mask may be compared with a predetermined threshold M0. If the value x of the unit to be detected (e.g., an element of the accumulated mask) is less than i If M0, output that a target exists in the detection target unit; If the image is TIFF2025530621000013.tif6150, it may be output that there is no target in the detection target unit. i , M, and M0 are positive integers.

[0133] In the detection method flow shown in FIG. 20, by performing CFAR processing for each channel independently, the performance loss caused by the imbalance between channels can be effectively reduced, and the method has better robustness in practical applications.

[0134] An embodiment of the present disclosure provides yet another target detection method, which includes the following steps 101 to 102, as shown in FIG.

[0135] In step 101, a constant false alarm probability process is performed independently on at least two transmitting and receiving channels based on the range-Doppler spectrum to obtain candidate target data for at least two transmitting and receiving channels.

[0136] In step 102, processing is performed based on the candidate target data of at least two transmitting and receiving channels to obtain final target data.

[0137] In this way, by performing a fixed false alarm probability detection independently for at least two transmitting / receiving channels, the results of the at least two transmitting / receiving channels will not interfere with each other or affect other channels. Therefore, even if an abnormal channel or abnormal data exists between the at least two transmitting / receiving channels, it will not affect the processing of the other normal channels. Furthermore, processing is then performed based on the candidate target data of the at least two transmitting / receiving channels to obtain the final target data, which supports detection for the two transmitting / receiving channels. This ensures normal processing of the channel containing the abnormal channel or abnormal data, and also prevents the channel containing the abnormal channel or abnormal data from interfering with other channels. This reduces the problems of imbalance between channels, and the delay and phase differences caused by the wiring lengths from the transmitting / receiving channels to the antennas not matching between channels and not being accurately compensated for, thereby improving detection accuracy.

[0138] To better understand the target detection method according to the above embodiment, the steps thereof will be described below.

[0139] In step 101, the number of transmitting and receiving channels that are independently subjected to the constant false alarm probability processing is not limited, and may be, for example, two, three, five, or all the transmitting and receiving channels.

[0140] For example, in some embodiments, constant false alarm probability processing may be performed independently on all transmit and receive channels, completely avoiding mutual interference between channels and improving accuracy. In this case, as shown in FIG. 22, constant false alarm probability processing is performed on at least two transmit and receive channels based on the range-Doppler spectrum by the following method: step 201, constant false alarm probability processing is performed on each transmit and receive channel based on the range-Doppler spectrum. Correspondingly, processing based on candidate target data of at least two transmit and receive channels is performed by the following method: step 202, processing candidate target data of each transmit and receive channel.

[0141] Of course, the above is merely an example, and in some embodiments, constant false alarm probability detection may be performed for some transmit and receive channels alone, thereby avoiding the adverse effects of channels with relatively low signal-to-noise ratios or severe transmit and receive leakage (TRX), which will not be described in further detail here.

[0142] The method of the single-transmitter / receiver constant false alarm probability processing in step 101 is not limited. For example, in some embodiments, it may be implemented based on the existing constant false alarm probability processing, by simply changing the data accumulation of the conventional multiple-transmitter / receiver channels to the data accumulation of the single-transmitter / receiver channel.

[0143] In some embodiments, as shown in FIG. 23, performing constant false alarm probability processing for at least two transmit and receive channels based on the range-Doppler spectrum may be realized by the following steps 1011-1012.

[0144] In step 1011, noise floor estimation is performed independently for the transmitting and receiving channels that are independently subjected to constant false alarm probability processing based on the range-Doppler spectrum, and a noise floor estimation result of the single transmitting and receiving channel is obtained.

[0145] In step 1012, based on the noise floor estimation result of a single transmitting / receiving channel, a constant false alarm probability detection is performed independently for the corresponding transmitting / receiving channel to obtain candidate target data corresponding to each transmitting / receiving channel.

[0146] That is, when performing constant false alarm probability processing on a single transmitting / receiving channel, noise floor estimation is performed on the single transmitting / receiving channel as an independent whole, and noise floor-based CFAR is realized based on the noise floor obtained by each estimation.

[0147] The noise floor estimation method in step 1011 is also not limited. For example, in some embodiments, noise floor estimation for a transmitting / receiving channel that is independently subjected to a constant false alarm probability process based on the range-Doppler spectrum may be achieved by the following method: noise floor estimation for a transmitting / receiving channel that is independently subjected to a constant false alarm probability process within a preset range based on the range-Doppler spectrum to obtain a noise floor estimation result for a single transmitting / receiving channel, with the upper bound of the preset range being determined based on the noise floor estimation result of the farthest distance unit. That is, the noise floor estimation is constrained by setting a preset range, and the upper bound is established based on the noise floor estimation result of the farthest distance unit, which is beneficial to improving the accuracy of the noise estimation.

[0148] In some examples, noise floor estimation for each transmit and receive channel independently is achieved by the following equation: n i '=min(n i ,α*n g ) where n i ' is the noise floor estimation result within the preset range of the i-th distance unit of a single transmit / receive channel, and n i is the initial noise floor estimation result of the i-th range unit in a single transmit / receive channel determined based on the range-Doppler spectrum, α is a preset parameter, TIFF2025530621000014.tif7150, n g is the initial noise floor estimate of the farthest distance unit in a single transmit / receive channel.

[0149] The preset parameter α may be set and updated based on, for example, demand or process data, experience, and the like.

[0150] It should be noted that the above is merely an exemplary explanation of noise floor estimation. In some embodiments, the initial noise floor estimation result of the i-th distance unit in a single transmitting / receiving channel may be directly selected as the noise floor result used when performing CFAR processing on the transmitting / receiving channel. Alternatively, the minimum, maximum, quantile, average, median, etc. of the initial noise floor estimation results of the last few regions of interest in a selectable single transmitting / receiving channel may be selected as the noise floor estimation. Alternatively, when estimating the noise floor, only some of the Doppler units of a distance unit may be selected rather than all of the Doppler units. No further enumeration of noise floor estimation methods will be given here.

[0151] In step 1012, the constant false alarm probability detection may be realized by a threshold detection method, for example, the noise floor estimation result of a single transmitting / receiving channel is composed of the noise floor estimation result of each distance unit in the single transmitting / receiving channel, and the constant false alarm probability detection result corresponding to the single transmitting / receiving channel is composed of the constant false alarm probability detection result of each Doppler unit of each distance unit in the single transmitting / receiving channel.

[0152] Based on the noise floor estimation result of a single transmit / receive channel, the constant false alarm probability detection for the corresponding transmit / receive channel alone can be realized by the following formula: JPEG2025530621000015.jpg1375 TIFF2025530621000016.tif6150 is the constant false alarm probability detection result of the vth Doppler unit of the rth distance unit of the cth channel, TIFF2025530621000017.tif6150 is the echo energy at the vth Doppler unit of the rth distance unit of the cth channel, TIFF2025530621000018.tif6150 is the noise floor estimation result for the rth distance unit of the cth channel, TIFF2025530621000019.tif7160 is the preset parameter, The file is TIFF2025530621000020.tif8150.

[0153] As can be seen from the above formula, If TIFF2025530621000021.tif6150 is 1, it indicates that a target is present at the vth Doppler unit of the rth range unit, If TIFF2025530621000022.tif6150 is 0, it indicates that there is no target at the v-th Doppler unit of the r-th distance unit. In other words, by binarizing the detection results of a single transmitting and receiving channel, the detection process is not affected by specific values ​​(especially extreme data), improving detection accuracy.

[0154] The above formula is an explanation of only performing binarization processing on the results to obtain candidate target data, but in some embodiments, the results may be further quantified using a quantification method other than binarization processing, which can also reduce the interference of extreme data to a certain extent, and does not necessarily have to be achieved by a binarization method, but we will not list them here.

[0155] The above is only an example of a single-transmitter / receiver constant false alarm probability process provided in combination with a CFAR based on noise floor estimation. However, this does not mean that the single-transmitter / receiver constant false alarm probability process in the present embodiment can only be implemented in the above manner. For example, it may be implemented in combination with CA (Cell Averaging)-CFAR, OS-CFAR, GO-CFAR, etc. In this case, the multi-transmitter / receiver channel process in the conventional algorithm can be regarded as a single-transmitter / receiver channel process, and no further detailed description will be given here.

[0156] In step 102, processing the candidate target data of at least two transmitting and receiving channels can be regarded as actually performing target verification based on the data of a single transmitting and receiving channel in step 101, and then performing target verification again based on the candidate target data of the transmitting and receiving channels, thereby improving the accuracy and reliability of the target. Therefore, step 102 can be realized by the concept of constant false alarm probability detection.

[0157] For example, in some embodiments, as shown in FIG. 23, performing processing on the candidate target data of each transmitting and receiving channel to obtain the final target data may be realized by the following step 1021.

[0158] In step 1021, a certain false alarm probability detection is performed based on the currently acquired candidate target data and a preset threshold to obtain the final target data.

[0159] That is, in accordance with the situation where the conventional constant false alarm probability processing performs threshold judgment based on the result of non-coherent integration, a similar threshold processing is performed on the candidate target data to obtain the final detection result, which is easy to implement, highly efficient, improves detection accuracy, and is fast in real time, improving the user experience.

[0160] In this case, constant false alarm probability detection based on currently acquired candidate target data and a preset threshold is realized by the following formula: JPEG2025530621000023.jpg958 JPEG2025530621000024.jpg1470 TIFF2025530621000025.tif6150 is the constant false alarm probability detection result of the vth Doppler unit of the rth distance unit of the cth channel, TIFF2025530621000026.tif6150 is the total number of channels, TIFF2025530621000027.tif6150 is the preset threshold, TIFF2025530621000028.tif6150 is the constant false alarm probability detection result for the vth Doppler unit of the rth distance unit.

[0161] That is, the candidate target data of the transmitting and receiving channels are accumulated, and then the result of the accumulation is determined by determining whether it exceeds a preset threshold to determine the final target data, which indicates whether a target exists at the v-th Doppler unit of the r-th distance unit, thereby avoiding monitoring errors and missed detections due to multipath, etc., and improving the accuracy and reliability of target detection.

[0162] Of course, the above is also only an example of obtaining final target data by using a threshold method. In some embodiments, when the detection result of a certain false alarm probability of the vth Doppler unit of the rth distance unit of the cth channel is expressed as a quantified numerical value, obtaining the final target data can also be determined by determining in how many channels the detection result of a certain false alarm probability of the vth Doppler unit of the rth distance unit exceeds a preset value, or the above cumulative addition process can be replaced by processes such as weighted addition or averaging, which will not be described in further detail here.

[0163] It should be noted that the above embodiment is limited only to target verification in the target detection process (i.e., obtaining results similar to those of conventional CFAR processing), and does not limit other data processing processes in the target detection process. It can be understood that the embodiments shown in Figures 21 to 23 may also be realized by combining them with other data processing implementation methods, for example, multi-frame joint processing methods.

[0164] To facilitate understanding of the above target detection method and multi-frame joint processing method, the multi-frame joint processing method will be described first.

[0165] As shown in FIG. 2, a radar (taking Frequency Modulated Continuous Wave (FMCW) radar as an example) receives an echo signal via an antenna and performs processes such as analog-to-digital conversion and sampling to obtain a digital signal. The digital signal is then processed by a master control module (which can be realized, for example, based on a microcontroller unit (MCU)) and a baseband (BB) module (which can be realized, for example, based on a baseband chip) shown in FIG. 2 to achieve target detection. For example, in FIG. 2, the digital signal undergoes range DC removal and range Fourier transform (Range FFT, which can also be represented as 1D-FFT or fast time-dimensional FFT) in the baseband module. After that, the signal processed by the baseband module is sent to the master control module to accumulate multiple frames. After accumulating the data up to a preset number of frames, for example, 128 frames, the master control module transfers the accumulated multiple frames of data from the static random-access memory (SRAM) of the CPU to the baseband module. Then, the baseband module performs Doppler DC removal, frame FFT, and inter-channel accumulation (non-coherent integration, correlation accumulation, etc.). Furthermore, the baseband module performs constant false alarm probability detection (for example, peak detection with peak average average (CFAR)) based on the noise obtained by noise estimation (for example, using a noise variance estimator). Finally, processing such as angle detection and target classification is performed, thereby obtaining information such as the distance, speed, and / or angle of the target.When detecting the angle, azimuth angle (Azimut) estimation and elevation angle (Elevation) estimation may be performed first based on the CFAR results, which may be achieved by techniques such as Digital Beam Forming (DBF) and wave arrival direction estimation.

[0166] The implementation of the digital signal processing shown in FIG. 2 has been described above.

[0167] Therefore, as can be seen from the above, the multi-frame joint processing scheme is combined with the multi-frame joint technology to acquire the RD spectrum. In other words, the noncoherent integration and CFAR processing in the multi-frame joint technology scheme are replaced with the target detection method according to the embodiment shown in Figures 21 to 23. That is, the method according to the embodiment is combined with the flow shown in Figure 2, resulting in a target detection flow as shown in Figure 24. In this case, after CFAR processing is performed on each channel, binary integration in the channel domain is performed, and then horizontal / azimuth angles and elevation / depression angles are estimated. For example, for a system with M channels, the inter-frame FFT shown in Figure 2 is performed on each channel, and then CFAR processing is performed on each channel to obtain M bit masks, each element of which may be of Boolean type. Then, it is determined whether a target exists in each range bin and / or Doppler bin (a 1 indicates presence, and a 0 indicates absence). Then, the M bit masks may be cumulatively added, and each element may be defined as a range of 0 to (M-1). Then, a secondary detection may be performed on the cumulatively added mask, that is, each element of the cumulatively added mask may be compared with a preset threshold M0. If the value x of the detection target unit (for example, an element of the cumulatively added mask) is smaller than i If M0, it may output that a target exists in the detection target unit (i.e., the final target data indicates that a target exists); If the target is TIFF2025530621000029.tif6170, it may be output that there is no target in the detection target unit (i.e., the final target data indicates that there is no target). i , M, M0 are positive integers. In the detection method flow shown in Figure 24, by performing CFAR processing for each channel independently, the performance loss due to the imbalance between channels can be effectively reduced and the robustness in practical applications is better.

[0168] To help those skilled in the art better understand the flow shown in FIG. 24, the following describes the flow according to different target detection methods.

[0169] In some embodiments, the target detection flow further includes the following steps 103 to 105, as shown in FIG.

[0170] In step 103, distance dimension FFT is performed on the frame data.

[0171] In step 104, target data is extracted from the frame data acquired by the distance dimension FFT processing.

[0172] In step 105, sliding window processing is performed on the target data, and a 2D FFT is performed on the windowed data to obtain a range-Doppler spectrum.

[0173] The length of the sliding window has a time length of the same order as the period of the periodic motion of the target.

[0174] To help those skilled in the art better understand the target detection method described in the embodiment shown in FIG. 25, the steps are described below.

[0175] In step 103, frame data refers to data corresponding to an echo signal formed when one frame of detection signals is reflected by a target. As can be understood, a target detection operation by a radar typically has a fixed cycle, as shown in FIG. 4. Within one detection cycle, the radar first transmits one frame of detection signals. Taking frequency-modulated continuous wave (FMCW) radar detection as an example, as shown in FIG. 5, one frame of detection signals is composed of multiple chirp signals. Simultaneously with the start of transmission of the detection signals, the radar begins preparation to receive echo signals resulting from the detection signals being reflected back by the target. The echo signals corresponding to one frame of detection signals undergo the aforementioned analog-to-digital conversion, range-dimensional DC component removal, and other processes, resulting in the above-mentioned frame data. Furthermore, each frame of data undergoes the aforementioned range-dimensional FFT to achieve step 103, which will not be described in further detail here.

[0176] In step 104, the embodiment of the present application does not impose any limitation on the target data, and the target data may be any data content corresponding to the frame data, for example, when there are sufficient storage resources and sufficient computing power, the extraction of the target data may be to extract all the data, or when it is desired to maximize the real-time performance of the target detection, only a portion of the data may be extracted, and the target data may be extracted according to the demands and hardware conditions, etc. For ease of understanding, different implementation methods for extracting a portion of the data as the target data will be described below.

[0177] In some embodiments, extracting target data from frame data acquired by distance-dimensional FFT processing is achieved by extracting data corresponding to one chirp period as target data from each frame data acquired by distance-dimensional FFT processing.

[0178] In other words, by using the data corresponding to one chirp period as frame data, target data processing is simple and easy to implement, the selected data is more realistic, target detection can be performed more efficiently, and this is advantageous for improving the real-time nature of target detection.

[0179] In some embodiments, extracting target data from frame data acquired by distance dimension FFT processing may further be achieved by determining parameters corresponding to each distance unit in each frame data based on each frame data acquired by distance dimension FFT processing, thereby generating target data.

[0180] That is, by compressing the multiple data of each distance unit in the frame data into corresponding parameters, the overall characteristics of the frame data can be maintained, so that when later performing target detection based on the target data, the information that can be referenced is more comprehensive, and the results obtained are more accurate.

[0181] Of course, the above embodiments are merely examples provided to reduce the resources occupied by target data, and in some embodiments, target data extraction may be performed taking into account data availability. For example, radar signals can usually cover a relatively wide range, but not all of the ranges covered by the radar are of interest, and processing data corresponding to these ranges would result in a waste of resources. Therefore, in some embodiments, extracting target data from frame data acquired by distance-dimension FFT processing may further be achieved by extracting data within a preset distance range from frame data acquired by distance-dimension FFT processing as target data.

[0182] That is, the range of interest in the frame data (represented by a preset distance range) is used as target data, which allows accurate target detection information to be maintained and reduces the storage resources, calculation resources, etc. occupied by the target data, thereby achieving a balance between the accuracy and real-time nature of target detection.

[0183] In addition, in addition to the above-mentioned method for retaining data within the range of interest, in some embodiments, the step of performing window processing on the target data based on a sliding window and performing digital signal processing on the data within a preset Doppler range in the windowed data may also involve performing window processing on the target data based on a sliding window and performing digital signal processing on the windowed data, which can also achieve the effect of retaining the region of interest data, but the dimensions of the two data are different, one is the distance dimension and the other is the Doppler dimension, so the method of selecting the range of interest is different. Of course, it is also possible to extract target data in the distance dimension while extracting data in the Doppler dimension and then perform digital signal processing, etc., which will not be described in further detail here.

[0184] Of course, the above is merely an example of how step 104 can be implemented, and in some embodiments it may be implemented in other ways, such as modulo-calculating distance units in pairs and using the larger distance unit as the target data (similar to max-pooling), which will not be described in further detail here.

[0185] In step 105, the sliding window method is not limited, and for example, the sliding window may be a fixed-length window, or for example, the sliding window may be an indefinite-length window, etc., and no further detailed explanation will be given here. For ease of understanding later, a fixed-length sliding window may be used as an example, but this does not mean that only a fixed-length sliding window can be used, and for example, an indefinite-length window that changes according to changes in the target's periodic movement, such as respiratory frequency, may also be used.

[0186] In some embodiments, as shown in FIG. 10, for example, if the window length of the sliding window is 128 frames for the 1st to 129th frame data generated after radar activation, the data acquired the first time by the sliding window is the 1st to 128th frame data (data in the shaded part of the first row in FIG. 9), and the data acquired the second time is the 2nd to 129th frame data (data in the shaded part of the second row in FIG. 10), i.e., the sliding step length of the sliding window is 1 frame.

[0187] Of course, the above is merely an example, and in other embodiments, other parameters may be adopted for the window length and sliding step length of the sliding window, and they will not be listed here.

[0188] In step 105, when windowing the data, the data must be in a certain order; if the order is disrupted, the temporal correlation of the data will be destroyed and the accumulation of movement over a certain period of time will not be reflected, which will be detrimental to subsequent target detection. Meanwhile, as shown in FIG. 24, data is accumulated in the master control module. Once a certain amount of data has been accumulated, the baseband module reads the data for further processing. In this process, the data may be written and read in different orders depending on the storage method. In some embodiments, the data must be rearranged to ensure the correct order. For example, in some embodiments, it is assumed that the data is cached using a Ring FIFO. In this case, as shown in FIG. 26, the target detection method further includes the following step 106:

[0189] In step 106, data is read from the Ring FIFO and the read data is rearranged.

[0190] This allows for data rearrangement to improve subsequent processing efficiency.

[0191] To enable those skilled in the art to better understand the above data storage and rearrangement, the following description will be made with reference to FIG.

[0192] As shown in Figure 13, if the data in the range of interest (for example, from the 5th distance unit to the 20th distance unit) among the data obtained after the baseband module (denoted by BB in the figure) performs distance-dimensional FFT on the frame data belongs to the target data, the master control module (denoted by MCU in the figure) needs to store a two-dimensional matrix as shown in Figure 13, where the rows of the matrix correspond to the frame numbers and the columns of the matrix correspond to the distance units. The detailed storage process can be referred to above and will not be repeated here.

[0193] It can also be seen in step 105 that in the detection process, multi-frame joint detection may require combining and observing different periodic motions, which can result in more accurate target detection results. Based on this, performing sliding window processing on the target data and performing 2D FFT on the windowed data may be realized by performing sliding window processing on the target data according to the periods of different biometric feature parameters of the biometric body, and performing digital signal processing on the windowed data corresponding to sliding windows of different lengths.

[0194] The periods of the biometric feature parameters corresponding to different sliding windows are different, and the length of each sliding window is the same order of time as the periods of the biometric feature parameters. It can be understood that when detecting based on multiple different periodic movements, data accumulation continues even after the completion of data accumulation with a short period, and data processing is completed when data accumulation with a long period is completed. For example, assuming that one periodic movement corresponds to 32 frames and another periodic movement corresponds to 128 frames, and both need to be observed, in the target detection process, a detection result will be output when the target data corresponding to the 32nd frame data is acquired, and after the target data corresponding to the 33rd frame data is acquired, a new detection result will be output...When the target data corresponding to the 128th frame data is acquired, two detection results (one detection result corresponding to 32 frames data and the other detection result corresponding to 128 frames data) or a result of integrating and processing the two results will be output.

[0195] In this way, by obtaining the period of the biometric characteristic parameters of the living body, the length of the sliding window can be adjusted in a timely manner, thereby better detecting the living body target and better monitoring the state of the living body (e.g., children, pets, etc.), thereby monitoring and performing processing corresponding to the state of the living body in a timely manner.

[0196] In the embodiments of the present application, the biometric characteristic parameters are not limited. For example, the biometric characteristic parameters may include respiration. Also, for example, the biometric characteristic parameters may include heart rate and / or pulse rate. Of course, the above are merely examples, and other biometric characteristic parameters that can be embodied as movement characteristics may be used according to needs, and will not be listed here.

[0197] It will be appreciated that once the target data is acquired, it may be further processed, such as for target classification, further validation, etc., if the user desires to obtain more intuitive and accurate results.

[0198] Based on this, in some embodiments, as shown in FIG. 27, after the target is acquired, the following steps 107 to 108 are further executed.

[0199] In step 107, targets in each preset region are verified based on targets that fall within each preset region, and the preset regions are obtained by dividing the detection space.

[0200] In step 108, the detection results for each preset region are output based on the targets that have passed the verification.

[0201] That is, considering that for a target that has a certain volume and occupies a certain space, multiple target points are usually detected, the currently detected target is used to continue combining region division to perform target verification, thereby improving the detection result, and based on the division of the detection space in the application scenario, the detection result of each preset region is output, so that the target distribution within the preset region appears, which is more intuitive and accurate, and is convenient for users to make decisions based on the output result, resulting in a better user experience.

[0202] To help those skilled in the art better understand the embodiment shown in FIG. 27, the steps are described below.

[0203] In step 107, the detection space and the preset area are not limited and may vary according to different application scenarios, application needs, etc. For example, in an application scenario of an on-board radar, the detection space may be the interior of a vehicle, and the preset area in this case may include at least one of an area corresponding to a seat and an area corresponding to a footrest, thereby allowing subjects such as a driver or an adult to better perceive the situation inside the vehicle. Also, for example, in a factory work scenario, the detection space may be a factory building, and the preset area may include each employee's workspace, thereby avoiding production risks such as not being able to monitor the operating status of machines because the employees are not in their workspaces. This will not be described in further detail here. For ease of understanding, the following description will use the above interior space as an example, but this does not mean that the corresponding solution can only be realized inside a vehicle.

[0204] Taking the interior of a vehicle as an example of the detection space, the interior space of the vehicle is abstracted into a coordinate system as shown in Figure 15, where the abscissa of the coordinate system indicates the azimuth angle relative to the radar installed in the vehicle, and the ordinate of the coordinate system indicates the elevation / depression angle relative to the radar installed in the vehicle. In this case, the interior of the vehicle is divided into a total of six preset areas, including the three seats in the rear row and the three aisles in front of the three seats, that is, different filling areas as shown in Figure 15. Depending on needs, as shown in Figure 15, the different preset areas may or may not overlap with each other, and some areas in the vehicle may belong to multiple preset areas at the same time, or may not belong to any area.

[0205] Note that Figure 15 is just one example of a method for abstracting the detection space, and in some embodiments, the detection area may be abstracted from all or any one of the dimensions of distance, azimuth, and elevation / depression angles, or from all or any one of the dimensions along the x, y, and z dimensions of a Cartesian coordinate system, and the preset area may be further divided, which will not be described in further detail here.

[0206] In step 108, the verification method is not limited, and verification may be performed based on, for example, the number of target points, the occupancy rate, or the like.

[0207] For example, in some embodiments, validating the targets in each preset region based on the targets that fall within each preset region may be accomplished by validating the targets in each preset region based on the occupancy rate of the targets that fall within each preset region among the detected targets.

[0208] Further for example, in some embodiments, validating the targets in each preset region based on the targets falling within each preset region may be accomplished by validating the targets in each preset region based on the signal-to-noise ratio of the targets falling within each preset region.

[0209] Of course, the above are merely examples, and in some cases targets at other locations may be verified based on the situation where the target point falls at a known target location, such as the driver's seat, and will not be described in further detail here.

[0210] In step 108, the output method is not limited, and the point cloud may be output directly for the detection results of each preset area, or the divided preset areas may be represented and the point cloud for each preset area may be output at the same time. No further enumeration is required here.

[0211] To help those skilled in the art better understand the above embodiment, the following description will take occupancy verification as an example.

[0212] Suppose that a total of 11 target points are detected in a target detection process, and the division of the preset area is as shown in FIG. 15, and at the same time, the distribution of the target points in the preset area is as shown in FIG. 18, namely, the area is seat A (6 target points), seat B (2 target points), seat C (0 target points), aisle A (0 target points), aisle B (1 target point), aisle C (0 target points), and in FIG. 18, the target points are shown as solid circles.

[0213] The algorithm obtained according to the above embodiment is then as follows: The flag bits flag_region_i in the six regions, which indicate whether a person is present or not, are all initialized to 0, i.e., there is no person in any of them (i=0, 1, ..., 5). If the total valid target number total_valid_tgt_num is 0, it is determined that there is no one in the car and the process ends. Otherwise, the following operations are performed: All regions are traversed, and if the statistical value tgt_num_region_i of the number of valid targets belonging to the region i exceeds 25% of the total number of valid targets total_valid_tgt_num, the flag bit flag_region_i of the region i is set to 1; If there is a region where flag_region_i is 1, set flag_region_empty to 0, i.e., there is a person in the vehicle. The system patrols the seating areas, and if any of the seating areas flag_region_i is 1, it determines that there is no person in the corresponding aisle area (for example, if it determines that there is a person in seat A, it determines that there is no person in aisle A), Output the results.

[0214] At this time, the output result is as shown in FIG. 28, where six target points are displayed only in seat A in the area, and there are no people in the remaining preset areas.

[0215] It should be noted that the threshold value of 25% in the above example is merely an example, and other threshold values ​​or other criteria may be used in other embodiments, which will not be described in further detail here.

[0216] In this way, by performing a fixed false alarm probability process independently for a single transmit / receive channel, interference from abnormal channels or abnormal data can be avoided. Furthermore, by adopting a frame-level FFT instead of the conventional chirp-level Doppler FFT, target kinetic energy can be effectively accumulated to achieve accurate detection and measurement. Multi-frame sliding window FFT processing further improves the update frequency of results. Post-processing such as area statistics can be performed on target points detected by the radar to determine whether or not there is a person in each area. For example, by adopting a frame-level FFT instead of the conventional chirp-level Doppler dimension FFT to determine target velocity to achieve accurate detection of targets of interest. Multi-frame sliding window FFT processing further improves the update frequency of results, further improving the real-time performance of the system. Furthermore, by performing processing only on the range and / or Doppler region of interest, the amount of data processed can be reduced. Furthermore, when estimating background noise, the background noise can be obtained by taking the minimum value based on the current range unit (bin) estimate and combining it with the last range unit of interest estimate. This is more suitable for application scenarios in relatively sealed environments, such as the interior of a vehicle or cabin. During region logic determination, the detected target points in each frame are counted according to a preset region design, and the counting result is determined according to a preset rule, thereby realizing accurate determination of whether a target exists within each preset region.

[0217] Figure 29 is a schematic flowchart illustrating target detection based on a combination of frame-level FFT and DAE CFAR in an embodiment of the present application. As shown in Figure 29, based on the flow structure shown in Figure 1, azimuth and elevation angles are estimated using a frame-dimensional FFT (frame-FFT) and a DAE CFAR (Constant False Alarm Rate) algorithm for CFAR data. For example, after Doppler-dimensional CFAR detection is performed on inter-frame FFT data, an azimuth-dimensional DBF (Azimuth DBF) is performed, which is combined with an NVE (noise variance estimator) noise floor estimation, followed by azimuth CFAR processing (Azimuth CFAR). Similarly, an elevation-dimensional DBF (Elevation DBF) is performed on the azimuth CFAR data, which is combined with an NVE noise floor estimation, followed by elevation CFAR processing (Elevation CFAR), thereby obtaining target azimuth and elevation information.

[0218] Figure 30 is a schematic diagram of azimuth or elevation CFAR based on the target detection flow shown in Figure 29. As shown in Figure 30, the NVE engine may calculate azimuth / elevation energy data (power data) using the HIST method, and the noise floor of the azimuth / elevation DBF may be estimated by selecting a certain rank value (e.g., average, median, etc.) from the calculated power data. When performing DBF, all peaks in the DBF spectrum may be acquired, and whether or not to output them as peaks may be determined based on the SNR of all peak points and the estimated noise floor.

[0219] For example, when DBF is performed, the amplitudes of all peaks in the DBF spectrum are obtained, including the global maximum (Global max) peak and the local maximum (Local max) peak. If the SNR of any peak exceeds a preset threshold, and the difference between its amplitude (Local max) and the global maximum amplitude (Global max) is within a preset range, and the number of peaks already output is less than a preset number, then that peak is output as a true target point. Compared to methods that directly use peak values ​​in the Doppler spectrum to locate targets, the Doppler spectrum may contain noise, which means that the peak value may not reflect the true target. Therefore, using this method to locate the true target point is more accurate. Furthermore, there may be multiple targets in the Doppler spectrum, and searching for targets using only peak values ​​may result in target recognition failure. On the other hand, when target detection is performed using this embodiment, the relationship between candidate targets and the global maximum amplitude value is taken into account, enabling accurate multi-target detection.

[0220] An embodiment of the present disclosure provides a target detection method, which includes the following steps 10 to 20, as shown in FIG.

[0221] In step 10, after performing distance-dimensional FFT processing based on the echo signal, 1D-FFT data is obtained, and multi-frame joint processing is performed on the 1D-FFT data to obtain an RD spectrum, which is, for example, a range-Doppler two-dimensional spectrum, and the spectrum contains distance-Doppler data, i.e., two-dimensional range-velocity data.

[0222] In step 20, detection of targets in the region of interest is realized based on the RD spectrum.

[0223] Detecting a target in the region of interest includes at least one of the following actions: determining, locating, recognizing, and the like.

[0224] The target detection method and related device according to the embodiments of the present disclosure can realize a target detection scheme in enclosed spaces such as cabins, rooms, factory buildings, etc., using multi-frame joint processing technology, i.e., inter-frame accumulation, thereby effectively improving the detection rate, reducing the number of false alarm targets, and significantly improving the accuracy of angle estimation, etc., so that special targets or weak targets such as infants in cabins can be accurately detected, and applications such as CPD (Child Presence Detection), SBR (Safety Belt Reminder), etc. can be realized.

[0225] An embodiment of the present disclosure further provides a target detection method, which may include: performing intra-chirp range dimension FFT and inter-frame velocity dimension FFT on an echo signal to obtain range-velocity dimension data; performing a first constant false alarm probability processing on the range-velocity dimension data to obtain candidate target detection points; performing a second constant false alarm probability processing on the candidate target detection points; and detecting targets based on a result of the second constant false alarm probability processing.

[0226] In the present embodiment, the false alarms are effectively eliminated by the two-time constant false alarm probability process, and the detection and measurement performance is effectively improved, so that special or weak targets such as infants in the cabin can be accurately detected, and applications such as CPD, SBR, etc. can be realized.

[0227] In an exemplary embodiment, the step of acquiring the distance-velocity dimension data includes: performing distance-dimension FFT processing within a chirp on the echo signal to acquire 1D-FFT data; and performing frame data accumulation on the 1D-FFT data until a predetermined data amount is reached, and then performing velocity-dimension FFT processing between frames to acquire the RD spectrum, i.e., the distance-velocity dimension data. For example, a sliding window method is employed to read a predetermined number of frames of 1D-FFT data each time, and perform FFT processing between frames.

[0228] In this implementation, accurate detection of targets within the cabin can be achieved using multi-frame joint processing technology. Multi-frame joint processing methods may involve performing a sliding window FFT on multi-frame data to obtain a range-Doppler spectrum, or FIR (Finite Impulse Response) or other complex time-frequency transform processing, followed by processing operations such as CFAR and DOA on the region of interest. Then, by referring to the configured region determination logic and region parameters, detection and location of living targets such as adults, children, and pets, or other non-living targets, can be achieved. CFAR may be Doppler-dimensional NR-CFAR, RD-CFAR, or DAE (Doppler-Azimuth-Elevation)-CFAR, etc. After CFAR and DoA, post-processing such as clustering, false alarm suppression, and multiple point cloud-related processing may be employed. The region parameters may be determined by at least one or a combination of at least two of the following operations: clustering the point cloud detected after performing a certain sliding window multi-frame processing; detecting and removing outliers from the point cloud detected after performing a certain sliding window multi-frame processing; etc.

[0229] In an exemplary embodiment, the step of performing the first constant false alarm probability processing includes performing non-coherent integration on the multi-channel RD spectrum, and performing a first constant false alarm probability processing based on noise estimation based on the non-coherent integration result to obtain candidate target detection points.

[0230] As an option, the first constant false alarm probability processing includes performing non-coherent integration on the multi-channel RD spectrum, estimating the noise floor of each distance unit after non-coherent integration to obtain a noise floor estimate for each distance unit, and using the noise floor estimate to perform non-coherent constant false alarm probability processing, and when estimating the noise floor of each distance unit after non-coherent integration, using the global noise floor to adjust the noise floor of each distance unit.

[0231] One possible adjustment method is to adjust the noise floor of each distance unit: This includes adjusting the image in the format TIFF2025530621000030.tif6150, where: TIFF2025530621000031.tif6150 TIFF2025530621000032.tif6 is the original noise floor estimate for the 150th distance unit, TIFF2025530621000033.tif6150 is the average noise floor estimate of multiple distance units, TIFF2025530621000034.tif6150 TIFF2025530621000035.tif6 is the adjusted noise floor estimate for the 150th distance unit, TIFF2025530621000036.tif7150. By adjusting the noise floor, it is possible to avoid targets not being detected due to the noise floor being too high.

[0232] In an exemplary embodiment, the second constant false alarm probability process includes performing Doppler-Azimuth-Elevation (DAE) CFAR processing for azimuth and elevation, respectively.

[0233] In an exemplary embodiment, the step of performing the second constant false alarm probability processing includes performing a digital beamforming processing on the candidate target detection points, and then performing a second constant false alarm probability processing based on noise estimation to obtain a selection result in which false target points are filtered out.

[0234] One possible method is to perform azimuth-dimensional DBF on the candidate target detection points, and then perform constant azimuth-angle false alarm probability processing with reference to the noise floor estimation result to obtain azimuth-dimensional target selection results; and / or perform elevation / depression-angle-dimensional DBF on the candidate target detection points, and then perform constant elevation / depression-angle-based false alarm probability processing with reference to the noise floor estimation result to obtain elevation / depression-angle-dimensional target selection results.

[0235] Another feasible method is to perform two-dimensional DBF of azimuth and elevation for the candidate target detection points, and then perform constant false alarm probability processing of azimuth and elevation angles with reference to the noise floor estimation results to obtain two-dimensional target selection results for azimuth and elevation.

[0236] The noise floor estimation result may be obtained by performing statistics on the DBF spectrum of the current dimension and selecting a quantile (for example, median or mean) as the noise floor estimation result of the current dimension.

[0237] For example, the above-mentioned azimuth angle constant false alarm probability processing, elevation / depression angle constant azimuth angle false alarm probability processing, or azimuth angle / elevation / depression angle constant false alarm probability processing may all be performed using the following method. During processing with a certain false alarm probability, one or more of global maximum filtering, first threshold filtering, and second threshold filtering are performed on the candidate target detection points to obtain target detection results. The global maximum filtering is used to filter whether the global maximum is a target detection point. The first threshold filtering is used to determine whether the current candidate filtering result is a target detection point based on the relationship between the candidate filtering result and the noise floor estimate value, thereby filtering out false targets. The second threshold filtering is used to determine whether the current candidate filtering result is a target detection point based on the relationship between the candidate filtering result and the global maximum, thereby realizing multi-target detection.

[0238] The global maximum filtering includes determining whether a difference between a digital beamforming spectrum amplitude value corresponding to a current global maximum and a noise floor estimate is within a first preset range, and if so, the candidate filtering result corresponding to the global maximum is the target detection point; the first threshold filtering includes determining whether a difference between a current candidate filtering result and a noise floor estimate is within a second preset range, and if so, the current candidate filtering result is the target detection point; and the second threshold filtering includes determining whether a difference between a power value of the current candidate filtering result and the power of the global maximum is within a third preset range, and if so, the current candidate filtering result is the target detection point.

[0239] In an exemplary embodiment, when target detection in a region of interest is realized based on the RD spectrum, the region of interest is pre-divided into multiple target sub-regions, the number and position of target points in each sub-region are determined based on the target detection point positions obtained after target detection, and whether there is a target to be detected in the current sub-region is determined based on whether the ratio of the number of target points in each sub-region to the total number of targets is greater than a preset proportional value.

[0240] When applied to a closed or relatively closed spatial region, the spatial region may be divided in advance to define and detect different regions of interest (divided regions). For example, in the case of target detection in a vehicle cabin, the region monitored by the radar may be divided into a seat section, an aisle section, etc., and then corresponding parameter types and thresholds may be pre-set for different types of regions, and then combined with corresponding processing steps, accurate detection of targets of interest in specific regions may be achieved.

[0241] In some alternative embodiments, for a family car, the interior space may generally be simply divided into a head space, a rear space, and a trunk space. If the rear space is a key monitoring area, the rear space may be further divided into a seat section and an aisle section, etc. The seat section may be divided into a corresponding number of seat section units based on the seats. Similarly, the aisle section may be divided into a corresponding number of aisle section units corresponding to the seat section units. For example, in a five-seat family car, the three seats in the rear space may be divided into three seat section units and three corresponding aisle section units. Corresponding parameter types and thresholds may be preset for different types of areas (or sections or section units), and adaptive signal data processing methods and steps may be adopted to accurately detect a target of interest (specific target) in a specific section or section unit. Adjacent section units may have a partial overlapping area, or may be adjacent or have a preset gap width.

[0242] An embodiment of the present disclosure further provides a target detection method applicable to target detection in a specific target area, the method including: performing a first constant false alarm probability processing on range-Doppler data, and then performing a second constant false alarm probability processing on an angle dimension to obtain target data.

[0243] In the above embodiment, a frame-level FFT is used instead of the conventional chirp-level Doppler FFT to determine target velocity and achieve accurate detection of targets of interest. Multi-frame sliding-window FFT processing can further improve the update frequency of results and further enhance the real-time performance of the system. Processing can also be performed only on the range and / or Doppler regions of interest to reduce the amount of data processed. Furthermore, background noise estimation can be performed by taking the minimum value based on the current range bin estimate and the last range bin estimate of interest, thereby better adapting to application scenarios in relatively sealed environments, such as the interior of a vehicle or cabin. Region logic determination involves counting detected target points in each frame using a preset region design, and then judging the count results using preset rules to accurately determine whether a target exists within each preset region. The purpose detection method of the present disclosure will be described in detail below using an application example, and the processing process may be as shown in FIG.

[0244] In step 1, when the target detection system detects a target within its detection range, the transmitting antenna in the target detection system may transmit a frequency modulated continuous wave (FMCW) signal containing some chirps, and the FMCW signal forms an echo signal after being refracted and / or reflected by the target, and the echo signal can be received by the receiving antenna in the target detection system.

[0245] The target detection system may be an antenna array system or a radar system having at least one transmit / receive channel, such as a Multiple Input Multiple Output (MIMO) radar system or a Single Input Multiple Output (SIMO) system, etc. The target detection system usually has N transmit antennas (TX) and M receive antennas (RX), where N and M are both positive integers greater than or equal to 1, and a virtual antenna array of N × M transmit / receive channels can be formed by rationally arranging the positions of each antenna.

[0246] In step 2, distance dimension FFT (1D-FFT) processing is performed on each chirp data of the echo signal of each received frame to obtain distance dimension data, that is, 1D-FFT data.

[0247] Before the 1D-FFT processing, the chirp data may optionally be first subjected to analog-to-digital conversion (ADC), and then the acquired ADC data may be subjected to a first direct current (DC) filtering process. The first DC filtering process involves calculating the average value of the data collected in each RX channel of each chirp along the fast time dimension and subtracting the DC component from all sampling points of each RX channel. The fast time dimension refers to the data sequence formed by sampling the radar echo signal in the range (or angle) direction. The DC filtering can remove the DC offset in the signal and zero the center line of the signal.

[0248] Alternatively, before 1D-FFT processing, windowing may be performed on the DC-filtered data first, and then 1D-FFT processing may be performed to obtain distance dimension information. Windowing can reduce spectral leakage and improve the performance of spectral estimation. The windowing operation is to multiply the original signal by a window function, which may be a weighting function with a specific shape, such as 64, 96, or 128. Common window functions include a rectangular window, a Hamming window, a Hanning window, a Blackman window, etc.

[0249] The 1D-FFT data may be moved from the BB to the SRAM in the CPU or MPU by DMA and cached.

[0250] In step 3, distance dimension FFT (2D-FFT) processing is performed on the 1D-FFT data of the preset number of frames to obtain distance dimension-velocity dimension data, that is, 2D-FFT data (or referred to as RD spectrum).

[0251] The velocity dimension may also be referred to as the Doppler dimension, and the above-mentioned distance dimension-velocity dimension information may also be referred to as distance dimension-Doppler information.

[0252] Unlike the slow time processing between chirps in radar signal processing in related art, this step accumulates multiple frame data and performs inter-frame 2D-FFT processing on 1D-FFT data for a preset number of frames each time using a sliding window, thereby improving the update frequency of the results and thereby improving the real-time performance of the system. The 2D-FFT processing in this step may also be referred to as inter-frame FFT processing. Taking an example where the preset number of frames is 128 and the sliding window step is 1, 2D-FFT processing is performed on frames 0-127 in the first pass, and on frames 1-128 in the second pass. While the sliding window step is 1 in this example, the sliding window step may be set to a positive integer greater than or equal to 1, and a preset number of frames may also be set.

[0253] Before performing the 2D-FFT processing, an optional second DC filtering process may be performed on the 1D-FFT data. The second DC filtering process involves calculating a complex average along the frame dimension of the 1D-FFT data of a preset number of frames to obtain the average value of different channels and different ranges as its DC component, and then subtracting the DC component from each channel and each range unit (range bin). A range unit refers to a small range interval divided along the radial direction (i.e., the direction in which a signal is transmitted and an echo is received) by a radar system. The range unit is used to represent the discrete range interval used by the radar system when detecting and tracking targets.

[0254] Alternatively, before the 2D-FFT process, windowing may be performed on the data after the second DC filtering, and then 2D-FFT may be performed along the frame dimension to obtain distance dimension-velocity dimension data. Windowing can reduce spectral leakage and improve the performance of spectral estimation. The windowing operation is to multiply the original signal by a window function, which may be a weighting function with a specific shape, such as 64, 96, or 128.

[0255] For each chirp data of each frame, when the data cached in the SRAM reaches a certain number of frames, for example, 128 frames, the data of the 128 frames may be moved from the SRAM to the BB by DMA, and then the following steps may be performed.

[0256] In step 4, noncoherent integration (NCI) is performed on the multi-channel 2D-FFT data to obtain accumulated data.

[0257] Any one of the following equations may be employed to perform noncoherent integration: JPEG2025530621000037.jpg3777 TIFF2025530621000038.tif6150 TIFF2025530621000039.tif6150th distance unit and TIFF2025530621000040.tif6 is the power in the 150th Doppler unit, TIFF2025530621000041.tif6150 TIFF2025530621000042.tif6150 distance unit and TIFF2025530621000043.tif6 is the amplitude at the 170th Doppler unit, TIFF2025530621000044.tif6150 TIFF2025530621000045.tif6150th distance unit TIFF2025530621000046.tif6150th Doppler unit, TIFF2025530621000047.tif6150th transmission channel and TIFF2025530621000048.tif6 is a complex value obtained after the echo signal in the 150th receiving channel has undergone the above 2D-FFT processing.

[0258] After non-coherent integration, we obtain the result shown in Figure 33, where the abscissa in the figure represents Doppler, i.e. velocity, and the ordinate represents distance, with the 0th distance unit including all Doppler values ​​in the figure with an ordinate of 0.

[0259] Step 5 estimates the noise floor, or background noise, for each distance unit in the accumulated data.

[0260] In this example, TIFF2025530621000049.tif6150, a noise floor estimation method is used. TIFF2025530621000050.tif6150 TIFF2025530621000051.tif6 is the original noise floor estimate for the 150th distance unit, TIFF2025530621000052.tif6150 is the average of the noise floor estimates of multiple distance units, or the noise floor estimate of the last distance unit of interest, TIFF2025530621000053.tif6150 TIFF2025530621000054.tif6 is the adjusted noise floor estimate for the 150th distance unit, TIFF2025530621000055.tif6160. As shown in FIG. 34, each square in the figure represents the original noise floor estimate for one distance unit. For example, the median or average of each distance unit may be calculated as the original noise floor estimate. For noise floor estimates for multiple distance units, for example, the median of the multiple original noise floor estimates shown in the dashed box (1) in the figure may be calculated. The noise floor estimate for the multiple distance units can be used as a global noise floor estimate to help adjust the noise floor (also called noise floor saturation processing). If a target (e.g., an adult) moves significantly, the overall Doppler spectrum value will be high, which will increase the original noise floor estimate, leading to the target not being detected during subsequent target selection. However, adjusting the noise floor using the global noise floor estimate can make subsequent target detection more accurate. The number and positions of distance units used to calculate the global noise floor estimate can both be configured; for example, the noise floor estimates of multiple distance units shown in the dashed box (2) in the figure may be used as the global noise floor estimate.

[0261] In step 6, Non-Coherent Constant False Alarm Rate (NR-CFAR) is performed to obtain candidate target detection points.

[0262] In step 7, the first target selection is performed on the candidate target detection points.

[0263] Each candidate target detection point The following operations are performed on TIFF2025530621000056.tif6150: extract 2D-FFT data corresponding to the detection point, select an azimuth dimension antenna, and perform azimuth dimension DBF (digital beam synthesis) based on the azimuth dimension guide vector to obtain a DBF spectrum, which indicates that the signal power or intensity in different directions varies with frequency, estimate the noise floor for the DBF spectrum, perform azimuth dimension CFAR (also known as Az-CFAR (Azimuth CFAR)), and perform a judgment on the azimuth dimension CFAR result according to a preset condition, and form a first candidate target point set with candidate target points that meet the preset condition as the first target selection result, among which are all candidate target points that meet the preset condition. TIFF2025530621000057.tif6150 and its corresponding azimuth angle Contains TIFF2025530621000058.tif6150.

[0264] The azimuth DBF spectrum is statistically analyzed to obtain the azimuth dimension CFAR result (similar to the elevation dimension CFAR) shown in Figure 3. In the figure, the x-axis indicates the azimuth and the y-axis indicates the amplitude of the DBF spectrum. If only the DBF amplitude peak is used as the target point, both the global max and local max in the figure will be considered as target points, which may lead to false alarms. Therefore, in order to suppress false targets, the following operation is performed.

[0265] The azimuth dimension DBF spectrum is statistically analyzed, and a quantile (e.g., median or mean) is selected as the noise floor estimate of the azimuth DBF, i.e., Az_HIST as shown in the figure. TIFF2025530621000059.tif6150 and find the global maximum in the DBF spectrum. TIFF2025530621000060.tif6150, if TIFF2025530621000061.tif6150, If it is determined that the target point is TIFF2025530621000062.tif6150, it is determined that the candidate target point corresponding to the global maximum value in the DBF spectrum is a false target point, and the processing for the target point is terminated. TIFF2025530621000063.tif6150 is one of the thresholds of the azimuth dimension CFAR. The above formula can be used to determine whether the global maximum value is the target detection point. If If the file is TIFF2025530621000064.tif6150, perform the following process. All points in the DBF spectrum are cycled through, and if any point satisfies the following conditions, it is determined to be a candidate target point that satisfies the preset conditions. In the JPEG2025530621000065.jpg28160 format, TIFF2025530621000066.tif7160 is the number of the candidate target point in the orientation dimension, TIFF2025530621000067.tif6150, TIFF2025530621000068.tif6150 is the number of points in the azimuth dimension DBF spectrum, TIFF2025530621000069.tif6150 is the power of the candidate target point, TIFF2025530621000070.tif6150 is a configurable parameter, TIFF2025530621000071.tif6150, TIFF2025530621000072.tif6150. The above formula 1 indicates that the ratio of the power of the candidate target point to the noise floor is less than the preset threshold TIFF2025530621000073.tif6150, which corresponds to setting a first limit, which is the noise floor estimate plus a first threshold. By setting the first limit, false target points can be filtered out. Equation 2 indicates that the ratio of the power of the candidate target point to the global maximum power is greater than or equal to the preset threshold. TIFF2025530621000074.tif7160 or more, which corresponds to setting a second limit, which is the global maximum power minus the second threshold. Setting the second limit enables multi-target detection, which is advantageous for recognizing targets such as children and babies. TIFF2025530621000075.tif6150, then equations 3 and 4 above indicate that the point is a local maximum.

[0266] Step 8 involves a second round of target selection.

[0267] Each candidate target point The azimuth angle for TIFF2025530621000076.tif6150 TIFF2025530621000077.tif6150 and its elevation and depression guide vectors based on the array configuration Generate TIFF2025530621000078.tif6150 and extract each candidate target point. TIFF2025530621000079.tif6150 is subjected to elevation and depression dimension DBF to obtain its elevation and depression dimension DBF spectrum, and a noise floor is estimated for the elevation and depression dimension DBF spectrum. A second CFAR (referred to as El-CFAR) is then performed, and the elevation and depression dimension CFAR results are judged according to pre-set conditions. Candidate target points that meet the pre-set conditions are selected as the second target selection results to form a second candidate target point set, among which are all candidate target points that meet the conditions. TIFF2025530621000080.tif6150 and its corresponding elevation and depression angles Contains TIFF2025530621000081.tif6150. The second target selection process is similar to the first target selection process. First, the elevation and depression DBF is statistically obtained to obtain the elevation and depression dimension CFAR result. Then, the result is evaluated using the formula TIFF2025530621000082.tif6150 determines whether the maximum value in the entire range is the target detection point, and if so, TIFF2025530621000083.tif6150, all points in the DBF spectrum are cycled through, and if any point satisfies the above formulas 1 to 4, it is determined to be a candidate target point that meets the preset conditions.

[0268] In the embodiment of the present disclosure, after performing NR-CFAR, a second CFAR is performed on the DBFs of azimuth and elevation, thereby suppressing false targets and realizing, to some extent, multi-target discrimination at the same distance and speed.

[0269] When generating the DBF spectrum of the elevation and depression dimensions in the above-mentioned El-CFAR, the elevation and depression dimension guide vectors are newly generated based on the azimuth angle information acquired by Az-CFAR, and in an exemplary embodiment, the elevation and depression dimension guide vectors of a preset azimuth angle (e.g., 0° azimuth angle) may be directly adopted.

[0270] The order of the first target selection in step 7 and the second target selection in step 8 above can be interchanged. In some embodiments, only step 7 or only step 8 may be performed.

[0271] In step 9, target information is extracted.

[0272] All eligible target points Information extracted from TIFF2025530621000084.tif6150 includes, but is not limited to, the range bin index (Distance Unit Index) and Doppler bin index (Doppler Unit Index) of the target point. It may also include the SNR (Signal-to-Noise Ratio) of the Range-Doppler CFAR (RD-CFAR), the SNR of the Az-CFAR, the SNR of the Elevation CFAR (El-CFAR), etc. The SNRs can be used for subsequent target person detection, for example, by weighting the corresponding target detection points based on the SNR value, where the weight is related to the SNR value.

[0273] In step 10, all the detected target points in each frame are counted using the preset area design, and the counting result is judged using the preset rule to obtain whether there is a person in that processing, and then the location of the person is determined, i.e., the target information is obtained.

[0274] In one embodiment of the preset regions, as shown in Fig. 35, the horizontal coordinate in the figure is the X direction (vehicle width direction), and the Z direction is the tail-to-head direction. The interior space of the vehicle is divided into regions that need to be determined along all or some of the XYZ dimensions, or along all or some of the distance-azimuth-elevation dimensions of a polar coordinate system. As in the example shown in Fig. 35, the space is divided into a total of six regions: three seats in the rear and three aisles in front of the three seats. These regions may or may not overlap with each other, and some regions in the vehicle may belong to multiple preset regions at the same time, or may not belong to any region.

[0275] Assume that a total of 11 target points are detected in a certain processing run, and the number of these target points in these preset areas is counted, and in this example, the count numbers of these target points in the areas Seat A, Seat B, Seat C, Aisle A, Aisle B, Aisle C are 0, 2, 6, 0, 1, 0.

[0276] Taking the above region division and counting results as an example, this process determines whether there is a person and determines the location of the person, which includes the following steps: In step 10.1, the flag bits flag_region_i in the six regions indicating whether or not there is a person are all initialized to 0, i.e., there is no person in any of them (i=0, 1, ..., 5). In step 10.2, it is determined whether the total valid target number total_valid_tgt_num is 0. If it is 0, it is determined that there is no person in the vehicle and the process ends. If it is not 0, step 10.3 is executed. In step 10.3, all regions are traversed. For region i, if it is determined that the proportion of the statistical value of the number of valid targets belonging to the region, tgt_num_region_i, to the total number of valid targets, total_valid_tgt_num, exceeds a preset proportion value (for example, 25%, which can be set based on experience), the flag bit flag_region_i of the region i is set to 1; In step 10.4, if there is a region where flag_region_i is 1, set flag_region_empty to 0, i.e., there is a person in the vehicle; In step 10.5, the seating areas are patrolled, and if any of the seating areas flag_region_i is 1, it is determined that there is no person in the corresponding aisle area (for example, if it is determined that there is a person in seat A, it is determined that there is no person in aisle A), Step 10.6 outputs the results.

[0277] By dividing the area and setting a preset proportional value, the target number of personnel can be determined more accurately and erroneous determination can be prevented.

[0278] In an exemplary embodiment, a 2D DBF of azimuth and elevation may be performed directly on the first CFAR result (i.e., the result of step 6), and a 2D DBF of azimuth and elevation may be performed directly on the 2D DBF spectrum of azimuth and elevation, and the main steps of the 2D DBF of step 6 may include the following:

[0279] Extract the 2D-FFT data, select an antenna, and perform 2D DBF based on the azimuth and elevation two-dimensional guide vector to obtain the DBF spectrum. Estimate the noise floor for the DBF spectrum. Refer to step 5 above for the noise floor estimation method. Perform a second CFAR (called AE-CFAR) to find all candidate target points that meet the conditions. TIFF2025530621000085.tif6150 and its corresponding azimuth angle TIFF2025530621000086.tif6150 and elevation angle Taking TIFF2025530621000087.tif6150, one possible approach is to cycle through all points in the DBF spectrum and determine that any point is a candidate target point if it satisfies the following condition: In the JPEG2025530621000088.jpg45160 format, TIFF2025530621000089.tif7160 is the number of the point in the orientation dimension, TIFF2025530621000090.tif6150, TIFF2025530621000091.tif6150 is the number of points in the azimuthal dimension DBF spectrum, TIFF2025530621000092.tif7160 is the number of the point in the elevation dimension, TIFF2025530621000093.tif6150 and TIFF2025530621000094.tif6150 is the number of points in the elevation dimension DBF spectrum, TIFF2025530621000095.tif6150 is the power of the point, TIFF2025530621000096.tif6150 is a configurable parameter, TIFF2025530621000097.tif6150, TIFF2025530621000098.tif6150. The above formula 5 indicates that the ratio of the power of the candidate target point to the noise floor is less than the preset threshold TIFF2025530621000099.tif6150, which corresponds to setting a first limit, which is the noise floor estimate plus a first threshold. By setting the first limit, false target points can be filtered out. Equation 6 shows that the ratio of the power of the candidate target point to the global maximum power is equal to or greater than the preset threshold. TIFF2025530621000100.tif7160 or more, which corresponds to setting a second limit, which is the global maximum power minus the second threshold. Setting the second limit enables multi-target detection, which is advantageous for recognizing targets such as children and babies. If the point is TIFF2025530621000101.tif11170, then equations 7 to 10 above indicate that the point is a local maximum.

[0280] Figures 36A to 36D show the processing results for a single-person scene (a baby in aisle C). A total of 500 frames of data were used, and inter-frame FFT was performed on 128 frames. The sliding window length for sliding window processing was 1, resulting in a total of 373 processing iterations. Figures 36A and 36C show the results when only NR-CFAR in the RD dimension was used, followed by azimuth and elevation DoA (wave arrival angle estimation). Figures 36B and 36D show the results when NR-CFAR in the RD dimension was used, followed by azimuth and elevation DBF, followed by a second CFAR processing. Figures 36A and 36B show the elevation-azimuth results, and Figures 36C and 36D show the azimuth-processing number results. As can be seen, the method of this embodiment (using NR-CFAR in the RD dimension, followed by azimuth-elevation DBF, and then a second CFAR) can effectively reduce the number of invalid false target points, make the target points more concentrated, effectively reduce the difficulty of post-processing, and improve the detection effect.

[0281] Figures 37A to 37D show the processing results for a two-person scene (the baby is in seat B, and the adult is in seat A). Figures 37A and 37C show the results of using only NR-CFAR in the RD dimension, followed by orientation-pitch DoA. Figures 37B and 37D show the results of using NR-CFAR in the RD dimension, followed by orientation-pitch DBF, followed by a second CFAR processing. Figures 37A and 37B show the pitch-orientation results, and Figures 37C and 37D show the orientation-processing number results. As can be seen, the method of this embodiment can effectively reduce the number of invalid false target points and make the target points more concentrated, effectively reducing the difficulty of post-processing and improving the detection effect.

[0282] The embodiments of the present disclosure can be applied not only to the scenario of human target detection and positioning in a vehicle, but also to other similar application scenarios such as personnel detection in a room, personnel detection in a factory building, etc.

[0283] The above operations may be performed by the MCU, or may be performed by the baseband accelerator, or some may be performed by the MCU and some may be performed by the baseband accelerator, for example, 1D-FFT, 2D-FFT, CFAR, DBF are performed in the baseband accelerator, and target detection and localization are performed in the MCU.

[0284] Compared with the prior art, the CPD processing method according to the embodiment of the present disclosure mainly adopts a brand new inter-frame accumulation method, which performs coherent accumulation for the frequency of human breathing, thereby improving the signal-to-noise ratio between human targets and strong static noise, thereby improving detection performance. CFAR second detection is then applied to the DBF spectrum generated in the azimuth / elevation dimensions of the CFAR result, thereby effectively eliminating false alarms and effectively improving detection and measurement performance. This allows special or weak targets, such as infants, in the cabin to be accurately detected, thereby realizing applications such as child detection (CPD) and seat belt reminder (SBR).

[0285] Performance evaluation through a large number of actual experiments showed that the method disclosed herein can achieve a detection rate of over 99%, with a false alarm rate and a false positive rate of less than 0.5% and 1%, respectively, demonstrating clear advantages over conventional techniques.

[0286] An embodiment of the present disclosure further provides a target detection method applicable to target detection in a specific target area, the method including: performing a first constant false alarm probability process on range-Doppler data, and then performing a second constant false alarm probability process in an angle dimension to obtain target data. The specific target area is, for example, an enclosed and / or semi-enclosed area such as a cabin or a room. The first constant false alarm probability process and the second constant false alarm probability process may refer to the description in the above embodiment.

[0287] FIG. 38 is a schematic flowchart illustrating post-processing of targets in an embodiment of the present application. As shown in FIG. 38, after azimuth / elevation / depression angle estimation is performed in the flow structure shown in FIG. 1, the output target point cloud data may be processed in combination with a machine learning model, thereby achieving accurate target detection in the region of interest. For example, a single target point cloud data acquired after azimuth / elevation / depression angle estimation may be combined with ML (machine learning, e.g., SVM or RF algorithm) false alarm suppression technology to suppress non-ideal data such as false alarms due to noise, static noise, or target multipath. That is, for example, the target point cloud data output by elevation / depression DBF & DoA may then be subjected to operations such as ML-based false alarm suppression, clustering, and / or ML-based target classification. The clustering may be an algorithm such as agglomerative clustering or DBSCAN. At the same time, the point cloud data after the clustering process can be input into a trained machine learning model (e.g., SVM or RF) to determine whether there is a person in the current process, and if there is a person, further determine their location, and realize operations such as distinguishing and identifying physical targets such as adults, children, and infants.

[0288] The embodiment shown in Figure 38 can effectively reduce the effects of non-ideal factors such as noise, static interference, and target multipath, and can effectively reduce the difficulty of selecting region parameters and the difficulty of designing decision logic for complex regions, thereby making more effective use of point cloud information and achieving better target detection and classification performance, especially for applications in complex scenes.

[0289] Fig. 39 is a schematic flowchart of post-processing of targets in combination with a DL algorithm in an embodiment of the present application. As shown in Fig. 39, after extraction is performed on data acquired in at least one of the operational steps of storing multiple frames, Frame FFT, non-coherent integration, constant false alarm probability detection (CFAR), etc. in the flow shown in Fig. 1, a deep learning algorithm (abbreviated as DL) may be used to determine, locate, and recognize targets, such as recognizing whether a physical target is an adult or a child (Adult / Child Classification).

[0290] For example, the original data of 1D-FFT may be input to the DL (Access 1D-FFT data as raw input of DL), the original data of inter-frame 2D-FFT may be input to the DL (Access 2D-FFT data as raw input of DL), the non-coherent integrated 2D-FFT data may be input to the DL (Access non-coherent integrated 2D-FFT data as raw input of DL), and / or the SNR data may be input to the DL (Access SNR data as raw input of DL), etc. The original data here may be complex data, modulo values ​​of complex data, modulo squares of complex data, or other transformation formats, and may be values ​​in the linear domain, values ​​in the dB(log) domain, or other calculation formats.

[0291] Based on the flow shown in Figure 39, by adopting at least one of the above methods, multi-frame 1D-FFT data, multi-channel data after multi-frame FFT, energy (power) or SNR data after multi-frame FFT and SISO-combine are extracted, and then the extracted data is input into a constructed deep learning model (e.g., CNN or Transformer, etc.) to determine whether a target exists in the current processing and determine the type of the current target (e.g., determine whether the target is an adult or a child, etc.).

[0292] Note that the flow steps and related descriptions shown in FIG. 39 are compatible with the flow steps and related descriptions shown in FIG. 38. For example, Region HIST and subsequent modules in the flow shown in FIG. 39 can be compatible with some or all of the ML-based false alarm suppression and subsequent modules in the flow shown in FIG. 38.

[0293] FIG. 40 is a schematic flowchart of a target detection method combined with a DL algorithm in an embodiment of the present application. As shown in FIG. 40, in the flow shown in FIG. 1, two-dimensional digital beamforming (2D-DBF) processing and DL-based target classification processing may be performed directly after frame FFT processing. That is, instead of performing CFAR processing after frame FFT, azimuth and elevation dimensional 2D-DBF processing is performed for each region of interest (or unit section), thereby obtaining a number of RD spectra corresponding to the region of interest. These RD spectra are then input into the constructed deep learning model (DL) for target detection. In some alternative embodiments, the relationship between the regions of interest and the RD spectra may be one-to-many, i.e., at least two RD spectra can be obtained based on one region of interest, and the specific number may be adjusted according to actual needs.

[0294] The DL input may be amplitude or power in the linear or dB domain, and may be subjected to normalization or other operations. The constructed deep learning model may then classify the input, and the distinguished types may be whether there is a target, the attributes of the target, the specific unit interval location (i.e., region location) where each target is located, etc.

[0295] For example, in an application scene where the rear area of ​​a car interior is referred to as a region of interest or target area and is divided into three seating areas (section units) and three corresponding aisle areas, after frame-to-frame FFT, azimuth-elevation 2D-DBF can be performed on the centers of the six regions of interest (the regions of interest can also be set to three seating areas) to obtain six RD spectra (or three RD spectra) corresponding to the regions of interest, and at least a portion of the six RD spectra (or three RD spectra) can be input to a constructed deep learning model to perform operations such as target identification and differentiation. For example, the constructed deep learning model can perform classification based on the input, thereby determining whether a living target is present in the region of interest, and if so, further determining whether the living target is an adult, child, infant, pet, etc., and further determining specific location information such as which seat or aisle area the living target is located in.

[0296] In the embodiment shown in Figure 40, the target detection method is a processing flow based on model and data dual driving, which has relatively low requirements for signal processing and can effectively avoid steps such as selecting signal processing parameters, region parameters, designing CFAR logic and region judgment logic, thereby greatly reducing the difficulty of implementing and designing the solution.

[0297] In some alternative embodiments, all FFT processes in all the above target detection methods may be windowed FFTs, and may also be realized by a combination of SVA and FFT.

[0298] Figure 41 is a schematic flowchart of another target detection method combined with a DL algorithm in an embodiment of the present application. As shown in Figure 41, in the flow shown in Figure 40, after the range-dimensional Fourier transform (Range FFT, which may also be referred to as 1D-FFT), 2D-DBF may be performed first, and then operations such as frame data accumulation and inter-frame FFT processing may be performed. That is, before the inter-frame FFT, 2D-DBF of azimuth and elevation / depression angles is first performed for the center position of the region of interest (e.g., the area of ​​three seats). This embodiment realizes a method for parallel processing of data processing and transmission time, thereby effectively reducing processing time.

[0299] In some alternative embodiments, in all the above target detection methods, the multi-frame sliding window FFT may be further replaced with other time-frequency transform processes, such as short-time Fourier transform, fractional Fourier transform, etc., to similarly achieve effective detection of targets of interest.

[0300] In some alternative embodiments, in all of the above target detection methods, the multi-frame sliding window FFT may be replaced with a filter such as a high-pass FIR, a comb FIR, or a filter optimized and integrated by an optimization function, thereby achieving performance similar to or better than that of frame FFT processing with fewer hardware resources.

[0301] As shown in Figures 40 to 41, the method of combining 2D-FFT and DL recognition (DL-based classification) can effectively improve the accuracy of target recognition, and the 2D-FFT step and the DL recognition step can be flexibly set between each step in signal processing according to actual needs, for example, the 2D-FFT step can be set after the Range FFT or after the Frame FFT, etc.

[0302] FIG. 42 is a schematic flowchart of another target detection method combined with a DL algorithm in an embodiment of the present application. As shown in FIG. 42, after the flow shown in FIG. 40 or FIG. 41, i.e., after a range-dimensional Fourier transform (Range FFT, also referred to as 1D-FFT), a 2D-DBF may be subsequently performed, and then the frame data may be accumulated and a DL-based target classification operation (Complex DL-based Classification) may be directly performed. That is, in the entire target detection flow, the inter-frame FFT processing operation is not performed, and after the 1D-FFT, 2D-DBF is directly performed on the center position of the region of interest (e.g., three seating areas) to obtain a corresponding number of range-frame spectra (e.g., three range-frames). The range-frame spectra are then input into a constructed (e.g., complex) deep learning model to perform operations such as target differentiation and determination.

[0303] In the embodiment shown in FIG. 42, the FFT processing step between frames is avoided, which effectively reduces the amount of calculation. Furthermore, by adopting a complex deep learning model, a higher processing gain than FFT can be obtained, thereby achieving the goal of improving target detection performance. Furthermore, this embodiment effectively shortens the processing hierarchy, which is convenient for efficient scheduling operations, etc.

[0304] In some alternative embodiments, the DBF in the target detection methods shown in Figures 40 to 42 may be replaced with algorithms such as Capon, MUSIC, ESPRINT, and their derivatives, and optimization integration may be performed on the beam synthesis at the center position of the region of interest and / or on the antenna arrangement, thereby further improving the target detection performance of the system.

[0305] Furthermore, the embodiments of the present application can be mutually referenced and interchangeable unless they conflict, and the order and configuration of each functional module can be adjusted according to needs. Furthermore, for a system having a BB module and an MCU module, when the system detects targets by transmitting electromagnetic waves and receiving corresponding echo signals, each step in each target detection method of the embodiments of the present application can be arranged and executed corresponding to the BB module and / or the MCU module, taking into consideration the actual needs, the data processing capacity and time efficiency of the system execution, etc., and the related examples in the drawings can be used as some options.

[0306] In some alternative embodiments, all of the target detection methods described above can be applied to TDM-MIMO systems, but for systems that do not employ TDM-MIMO, different phase shifts may be preset for different Tx transmissions, and then digitally synthesized transmit waveforms (phase-controlled array) may be performed directly on the region of interest, followed by target detection and determination operations in combination with the processing flows shown in Figures 40 to 42.

[0307] In some other embodiments, for systems employing a pulse method, a pulse compression method, or an ultra-wideband method, target detection may be performed directly using the method flows shown in Figures 40 to 42, and target detection performance may be further improved by further employing a transmit beam digital synthesis technique. For systems employing a pulse compression method, 1D-FFT processing may not be required, but an additional pulse compression step is required.

[0308] In addition, the above-mentioned processing method corresponding to systems of different types can also be applied to a hybrid type system. For example, for a system having six transmitting antennas (i.e., six Tx), three of the transmitting antennas (three Tx) may be TDM type, and the other three transmitting antennas may be other types, and the subsequent processing only needs to correspond to the processing method of the above type.

[0309] After inter-frame FFT processing, two-dimensional digital beam synthesis (2D-DBF) processing and DL-based target classification processing are directly performed. That is, after inter-frame FFT, CFAR processing is not performed, and azimuth and elevation 2D-DBF operations are performed for each region of interest (or unit section), thereby obtaining a number of RD spectra corresponding to the region of interest. These RD spectra are then input into the constructed deep learning model (DL) for target identification. In some alternative embodiments, if processing is performed using methods such as FIR or STFT (Short-Time Fourier Transform) instead of the inter-frame FFT method, the above-mentioned RD spectrum is a one-dimensional spectrum output corresponding to FIR, while if time-frequency transformation corresponding to STFT is used, it is a three-dimensional spectrum.

[0310] In addition, the electromagnetic waves in this application may include radio waves and light waves, and radio waves include short waves, medium waves, long waves, microwaves, etc., and microwaves include centimeter waves (i.e., electromagnetic waves from 3 GHz to 30 GHz, for example, electromagnetic waves from 3.1 GHz to 10.6 GHz, electromagnetic waves in the 24 GHz band, etc.) and millimeter waves (i.e., electromagnetic waves from 30 GHz to 300 GHz, for example, electromagnetic waves in the 60 GHz band, electromagnetic waves in the 77 GHz band (for example, 77 GHz to 81 GHz, etc.)), and light waves include ultraviolet rays, visible light, infrared rays, laser light, etc., and the electromagnetic wave band of laser light is (3.846 to 7.895)*10^5 GHz, that is, laser light falls within the frequency band of ultraviolet rays and some of the frequency bands of visible light.

[0311] An embodiment of the present disclosure further provides an integrated circuit, which may include a signal transmitting module configured to transmit electromagnetic waves for target detection, a signal receiving module configured to receive echoes formed by the reflected and / or scattered electromagnetic waves, and a processing module configured to perform signal and data processing on the echoes, thereby achieving target detection.

[0312] Optionally, the target detection is, for example, at least one of determining, locating and / or recognizing a target in a region of interest.

[0313] In an exemplary embodiment, the processing module includes at least a baseband unit, and the baseband unit may be configured to be used to implement distance dimension FFT processing, velocity dimension FFT, first constant false alarm probability processing, and second constant false alarm probability processing in the method according to any embodiment of the present disclosure.

[0314] Optionally, in an exemplary embodiment, the processing module may further include an MCU unit, and when the method includes digital beam synthesis and frame data accumulation, the baseband unit is configured to be used to realize the digital beam synthesis and the MCU unit is configured to be used to realize the frame data accumulation.

[0315] In an exemplary embodiment, the integrated circuit may be a millimeter wave radar chip or die.

[0316] The present embodiment further provides an integrated circuit, which may include a radio frequency module, an analog signal processing module, and a digital signal processing module connected in series, where the radio frequency module is used to generate a radio frequency transmission signal and receive an echo signal, the analog signal processing module is used to down-convert the echo signal to obtain an intermediate frequency signal, and the digital processing module is used to analog-to-digital convert the intermediate frequency signal to obtain a digital signal and process the digital signal according to the target detection method of the present embodiment to achieve target detection, for example, the integrated circuit may be a millimeter-wave radar chip (chip or die). The digital processing module may include sub-units such as a BB unit and an MCU unit, and each sub-unit may be configured to perform a corresponding step of the target detection method of the above embodiment.

[0317] In some alternative embodiments, the integrated circuit may be an AiP (Antenna-In-Package) chip structure, an AoP (Antenna-On-Package) chip structure, or an AoC (Antenna-On-Chip) chip structure.

[0318] According to some other embodiments of the present application, there is further provided an electromagnetic wave sensor. The electromagnetic wave sensor may include an antenna and an integrated circuit as described above. The integrated circuit is electrically connected to the antenna and used to transmit and receive electromagnetic wave signals. For example, the electromagnetic wave sensor may include a carrier, an integrated circuit as described in any of the above embodiments, and an antenna, etc. The integrated circuit may be mounted on the carrier, the antenna may be mounted on the carrier, or may be integrated with the integrated circuit as an integrated device and mounted on the carrier (i.e., in this case, the antenna may be mounted in an AiP, AoP, or AoC structure), and the integrated circuit may be connected to the antenna (i.e., in this case, the antenna is not integrated on the sensor chip or integrated circuit, such as a typical SoC), and used to transmit and receive electromagnetic wave signals. The carrier may be a printed circuit board (PCB), and the corresponding transmission line may be PCB wiring.

[0319] An embodiment of the present application provides a terminal device, which may include a device main body and an electromagnetic wave sensor as described above installed on the device main body, where the electromagnetic wave sensor is used for target detection and / or communication, thereby providing reference information for the operation of the device main body.

[0320]

[0013] Embodiments of the present application further provide an electronic device (which may be understood as a terminal device), which may appear in the form of a general-purpose computing device. Components of the electronic device may include, but are not limited to, at least one processing unit, at least one storage unit, a bus connecting different system components (including the storage unit and the processing unit), a display unit, etc. The storage unit stores program code, which can be executed by the processing unit, thereby causing the processing unit to perform the methods according to various exemplary embodiments of the present application described herein. The storage unit may include a readable medium in the form of a volatile storage unit, for example, a random access storage unit (RAM) and / or a cache storage unit, and may also include a read-only storage unit (ROM).

[0321] The storage unit may further include a program / utility having a set (at least one) program module, including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which, or any combination thereof, may include implementing a network environment.

[0322] The bus may represent one or more of several types of bus structures, including a storage unit bus or storage unit controller, a peripheral bus, a graphics accelerator port, a processing unit, or a local bus using any of a number of bus structures.

[0323] The electronic device may also communicate with one or more external devices (e.g., a keyboard, a pointing device, a Bluetooth device, etc.), one or more devices that allow a user to interact with the electronic device, and / or any device (e.g., a router, a modem, etc.) that allows the electronic device to communicate with one or more other computing devices. Such communication may occur via an input / output (I / O) interface. The electronic device may then further communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network such as the Internet) via a network adapter. The network adapter may communicate with other modules of the electronic device via a bus. It will be appreciated that, although not shown, other hardware and / or software modules may be used in conjunction with the electronic device, including, but not limited to, microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0324] For example, an electronic device in an embodiment of the present application may further include a device main body and an electromagnetic wave sensor described in any of the above embodiments installed on the device main body, and the electromagnetic wave sensor can be used to realize functions such as target detection and / or wireless communication.

[0325] Specifically, based on the above embodiment, in one optional embodiment of the present application, the electromagnetic wave sensor may be installed outside the device body or inside the device body, and in another optional embodiment of the present application, the electromagnetic wave sensor may further be partially installed inside the device body and partially installed outside the device body. The embodiments of the present application are not limited thereto, and specific details may be determined according to circumstances.

[0326] In one alternative embodiment, the device body may be a component or product applied in the fields of smart cities, smart houses, transportation, smart homes, consumer electronics, security monitoring, industrial automation, onboard detection (e.g., smart cabins), medical equipment, and health care, etc. For example, the device body may be a smart transportation device (e.g., automobile, bicycle, motorcycle, ship, subway, train, etc.), a security device (e.g., camera), a liquid level / flow rate detection device, a smart wearable device (e.g., bracelet, glasses, etc.), a smart home device (e.g., cleaning robot, door lock, television, air conditioner, smart lamp, etc.), various communication devices (e.g., mobile phone, tablet, etc.), and, for example, a barrier gate, a smart traffic indicator lamp, a smart sign, a traffic camera, various industrial mechanical arms (or robots), etc. It may also be various devices for detecting vital characteristic parameters and various devices equipped with such devices, such as vital characteristic detection in a car cabin, indoor occupant monitoring, a smart medical device, a consumer electronic device, etc.

[0327] An embodiment of the present application further provides a non-transitory computer-readable storage medium having computer-readable instructions stored thereon, which, when executed by a processor, cause the processor to perform the above-described method for compensating for unequal lengths of power feeders.

[0328] As can be understood by those skilled in the art from the above description of the embodiments, the embodiments described herein may be realized by software, or by a combination of software and necessary hardware. The technical solutions according to the embodiments of the present application may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a USB disk, a removable hard disk, etc.) or on a network, and includes some instructions for causing a computing device (which may be a personal computer, a server, a network device, etc.) to execute the above method according to the embodiments of the present application.

[0329] A software product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include an electrical connection having one or more leads, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0330] A computer-readable storage medium may include a data signal propagated in baseband or as part of a carrier, carrying readable program code. The propagated data signal may take multiple forms, including, but not limited to, an electromagnetic signal, an optical signal, or any suitable combination of the above. A computer-readable storage medium may also be any readable medium other than a computer-readable storage medium, which is capable of transmitting, propagating, or transmitting a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained in the computer-readable storage medium may be transmitted over any suitable medium, including, but not limited to, wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0331] Program code for carrying out the operations of the present application may be written in any combination of one or more program design languages, including object-oriented program design languages ​​such as Java, C++, etc., as well as traditional process program design languages ​​such as "C" or similar. The program code may execute entirely on the user's computing device, partially on the user's device, as a separate software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. When a remote computing device is involved, the remote computing device may be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., via the Internet using an Internet Service Provider).

[0332] The computer-readable medium carries one or more programs that, when executed by the device, implement the functionality described above.

[0333] Those skilled in the art will understand that each of the above-mentioned modules may be arranged in the device as described in the embodiment, or may be located in one or more devices different from the embodiment with appropriate modifications. The modules in the above embodiment may be integrated into one module or further divided into multiple sub-modules.

[0334] According to an embodiment of the present application, a computer program is provided, which includes computer programs or instructions, and when the computer programs or instructions are executed by a processor, the above-mentioned method can be performed. In one alternative embodiment, the integrated circuit may be a millimeter-wave radar chip. The types of digital functional modules in the integrated circuit may be determined according to actual needs. For example, in a millimeter-wave radar chip, the data processing module may be used for, for example, distance-dimensional Doppler transform, velocity-dimensional Doppler transform, constant false alarm probability detection, wave direction detection, point cloud processing, etc., to obtain information such as the distance, angle, velocity, height, micro-Doppler motion characteristics, shape, size, surface roughness, and dielectric properties of the target.

[0335] Furthermore, the wireless device can realize functions such as target detection and / or communication by transmitting and receiving wireless signals, thereby providing detected target information and / or communication information to the device main body, thereby supporting or controlling the operation of the device main body.

[0336] For example, when the above-mentioned device main body is applied to an advanced driver assistance system (i.e., ADAS), the wireless device (e.g., millimeter-wave radar) as an on-board sensor can support the ADAS system to realize application scenarios such as adaptive cruise control, automatic brake assist (i.e., AEB), blind spot monitoring (i.e., BSD), lane change assist (i.e., LCA), reverse vehicle detection alert (i.e., RCTA), parking assistance, rear vehicle alert, collision prevention, pedestrian detection, and in-cabin life detection (i.e., CPD).

[0337] As will be understood by those skilled in the art, all or some steps of the methods, systems, and functional modules / units in the devices disclosed above can be implemented as software, firmware, hardware, or any suitable combination thereof. In hardware embodiments, the division between the functional modules / units mentioned above does not necessarily correspond to the division of physical components; for example, one physical component may have multiple functions, or one function or step may be performed by multiple physical components in cooperation. Some or all of the components may be implemented as software executed by a processor, such as a digital signal processor or microprocessor, or as hardware, or as an integrated circuit, such as a dedicated integrated circuit. Such software may be located on a computer-readable medium, which may include computer storage media (or non-transitory media) and communication media (or transitory media). As is well known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (e.g., computer-readable instructions, data structures, program modules, or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium used to store the desired information and accessible by a computer. As known to those skilled in the art, communication media typically include computer-readable instructions, data structures, program modules, or other data in a modulated data signal, such as a carrier or other transport mechanism, and may include any information delivery media.

[0338] The technical features of the above embodiments may be combined in any manner, and for the sake of simplicity, not all possible combinations of the technical features in the above embodiments have been described. However, as long as there is no contradiction in the combination of these technical features, any combination should be considered within the scope of the present specification.

[0339] The above embodiments only represent preferred embodiments of the present invention and the technical principles used. The descriptions are relatively specific and detailed, but should not be understood as limitations on the scope of the patent. Those skilled in the art can make various obvious modifications, adjustments, and substitutions without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in relatively detail through the above embodiments, the present invention is not limited to the above embodiments and may include many other equivalent embodiments without departing from the concept of the present invention. The patent protection scope of the present invention is determined by the appended claims.

Claims

1. 1. A target detection method, comprising: Frame data is accumulated for 1D-FFT data, and the 1D-FFT data is acquired by performing distance-dimensional FFT processing on the echo signal; performing FFT processing between frames based on the accumulated frame data to obtain an RD spectrum; and detecting a target in a region of interest based on the RD spectrum.

2. performing inter-frame FFT processing based on the accumulated frame data to obtain an RD spectrum; and detecting a target in a region of interest based on the RD spectrum; 2. The target detection method according to claim 1, further comprising: extracting target data from accumulated frame data; performing sliding window processing on the target data; and performing digital signal processing on the windowed data to obtain target information, wherein the length of the sliding window has a time length equivalent to the period of the periodic motion of the target.

3. Extracting target data from the stored frame data includes: extracting at least a portion of data or at least a portion of data after digital processing from each frame data acquired by distance dimension FFT processing to set as the target data, or determining parameters corresponding to each distance unit in each frame data based on each frame data acquired by distance dimension FFT processing, thereby generating the target data; performing digital signal processing on the windowed data to obtain target information, 3. The target detection method of claim 2, further comprising: performing noise floor estimation within a preset range, the upper limit of which is determined based on the noise floor estimation result of the farthest distance unit; and performing constant false alarm probability detection based on the estimated noise floor, thereby obtaining target information.

4. Extracting target data from frame data acquired by the distance dimension FFT processing includes: extracting data within a preset distance range as the target data from the frame data acquired by the distance dimension FFT processing; and / or The step of performing digital signal processing on the windowed data includes:

3. The method of claim 2, further comprising performing digital signal processing on data within a preset Doppler range of the windowed data.

5. 4. The target detection method according to claim 2, wherein when the data is cached using a Ring FIFO, the method further comprises: reading the data from the Ring FIFO; and rearranging the read data.

6. performing sliding window processing on the target data and performing digital signal processing on the windowed data to obtain target information; performing window processing on the target data based on sliding windows corresponding to periods of different biometric characteristic parameters of a living body, and performing digital signal processing on the windowed data corresponding to the sliding windows of different lengths; 3. The target detection method according to claim 2, wherein the periods of the biometric characteristic parameters corresponding to the different sliding windows are different, and the length of each sliding window has a time length of the same order of magnitude as the period of the biometric characteristic parameters.

7. Implementing target detection in a region of interest based on the RD spectrum includes: Performing a constant false alarm probability process independently on at least two transmitting and receiving channels based on the RD spectrum, thereby obtaining candidate target data of the at least two transmitting and receiving channels; 3. The target detection method according to claim 1, further comprising: performing processing based on the candidate target data of at least two transmitting and receiving channels to obtain final target data.

8. performing a constant false alarm probability process independently for at least two transmitting and receiving channels based on the RD spectrum, performing a constant false alarm probability process for at least two transmitting and receiving channels respectively based on the RD spectrum; or 8. The target detection method of claim 7, further comprising: performing noise floor estimation independently for a transmitting / receiving channel that performs a constant false alarm probability process independently based on the RD spectrum, obtaining a noise floor estimation result for the single transmitting / receiving channel; and performing constant false alarm probability detection independently for the corresponding transmitting / receiving channel based on the noise floor estimation result for the single transmitting / receiving channel.

9. The noise floor estimation result of the single transmitting and receiving channel is composed of the noise floor estimation result of each distance unit in the single transmitting and receiving channel, and the constant false alarm probability detection result of the single transmitting and receiving channel is composed of the constant false alarm probability detection result of each Doppler unit in each distance unit in the single transmitting and receiving channel; Based on the noise floor estimation result of the single transmitting / receiving channel, the constant false alarm probability detection for the corresponding transmitting / receiving channel is realized by the following formula: where: is the constant false alarm probability detection result of the vth Doppler unit of the rth distance unit of the cth channel, is the echo energy at the vth Doppler unit of the rth range unit of the cth channel, is the noise floor estimate of the r-th distance unit of the c-th channel, are preset parameters, 9. The target detection method according to claim 8, wherein:

10. The noise floor estimation for the transmit and receive channels independently performing constant false alarm probability processing based on the RD spectrum is 9. The target detection method of claim 8, further comprising: performing noise floor estimation independently for a transmitting / receiving channel that performs a constant false alarm probability process independently within a preset range based on the RD spectrum; and determining an upper limit of the preset range based on the noise floor estimation result of the farthest distance unit.

11. performing processing based on the candidate target data of the at least two transmit and receive channels; 8. The target detection method according to claim 7, further comprising: performing a constant false alarm probability detection based on the currently acquired candidate target data and a preset threshold to obtain the final target data.

12. The constant false alarm probability detection based on the currently acquired candidate target data and the preset threshold is realized by the following formula: where: is the candidate target data of the v-th Doppler unit of the r-th range unit of the c-th channel acquired, is the total number of channels, is the preset threshold, 12. The target detection method according to claim 11, wherein: is the final target data of the v-th Doppler unit of the r-th range unit.

13. After obtaining the target information, the method further includes: Verifying the target in each preset region based on the target entering within each preset region, and the preset regions are obtained by dividing a detection space; and outputting a detection result of each of the preset regions based on the target that has passed verification.

14. Validating the target in each of the preset regions based on the target that falls within each of the preset regions includes: Verifying the targets in each preset region based on the occupancy rate of the detected targets of the targets that fall within each preset region; or 14. The target detection method according to claim 13, further comprising verifying the targets in each preset region based on a signal-to-noise ratio of the targets falling within the preset region.

15. Implementing target detection in a region of interest based on the RD spectrum includes: performing a first constant false alarm probability processing on the RD spectrum to obtain candidate target detection points; 2. The target detection method according to claim 1, further comprising: performing a second constant false alarm probability process on the candidate target detection points; and performing target detection based on a result of the second constant false alarm probability process.

16. performing a first constant false alarm probability processing on the RD spectrum to obtain candidate target detection points; 16. The target detection method of claim 15, further comprising: performing noncoherent integration on multi-channel range-velocity dimension data; and performing a first constant false alarm probability process based on noise estimation based on the noncoherent integration result to obtain candidate target detection points.

17. performing a second constant false alarm probability processing on the candidate target detection points; performing azimuth-dimensional DBF on the candidate target detection points, and performing constant azimuth-angle false alarm probability processing with reference to the noise floor estimation result to obtain azimuth-dimensional target selection results; and / or performing elevation / depression-angle-dimensional DBF on the candidate target detection points, and performing constant elevation / depression-angle-dimensional false alarm probability processing with reference to the noise floor estimation result to obtain elevation / depression-angle-dimensional target selection results; 16. The target detection method according to claim 15, further comprising: performing two-dimensional DBF of azimuth and elevation for the candidate target detection points; and performing constant false alarm probability processing of azimuth and elevation angles with reference to a noise floor estimation result, thereby obtaining two-dimensional target selection results in azimuth and elevation.

18. 18. The target detection method of claim 17, wherein, during a constant false alarm probability process, one or more operations of global maximum filtering, first threshold filtering, and second threshold filtering are performed on the candidate target detection points to obtain target detection results, wherein the global maximum filtering is used to filter whether the global maximum is a target detection point, the first threshold filtering is used to determine whether the current candidate filtering result is a target detection point based on a relationship between the candidate filtering result and a noise floor estimate, and the second threshold filtering is used to determine whether the current candidate filtering result is a target detection point based on the relationship between the candidate filtering result and the global maximum.

19. The global maximum filtering includes determining whether a difference between a digital beamforming spectrum amplitude value corresponding to a current global maximum and a noise floor estimate is within a first preset range, and if it is within the first preset range, the candidate filtering result corresponding to the global maximum is a target detection point; The first threshold filtering includes determining whether a difference between the current candidate filtering result and the noise floor estimate is within a second preset range, and if the difference is within the second preset range, the current candidate filtering result is a target detection point; 19. The target detection method of claim 18, wherein the second threshold filtering includes determining whether a difference between a power value of a current candidate filtering result and a power of a global maximum value is within a third preset range, and if the difference is within the third preset range, the current candidate filtering result is a target detection point.

20. performing noncoherent integration on the multi-channel distance / velocity dimension data, and performing a first constant false alarm probability process based on noise estimation based on the noncoherent integration result; The target detection method of claim 15, further comprising: performing noncoherent integration on multi-channel range-velocity dimension data; estimating the noise floor of each range unit after noncoherent integration to obtain a noise floor estimate for each range unit; and performing noncoherent constant false alarm probability processing using the noise floor estimate; and adjusting the noise floor of each range unit using a global noise floor when estimating the noise floor of each range unit after noncoherent integration.

21. performing target detection based on the second constant false alarm probability processing result; 16. The target detection method of claim 15, comprising: dividing a region of interest into a plurality of target sub-regions in advance; determining the number and positions of target points in each sub-region based on target detection point positions obtained after target detection; and determining whether there is a target to be detected in the current sub-region based on whether the ratio of the number of target points in each sub-region to the total number of targets is greater than a preset proportional value.

22. For a multi-channel application scene, detecting a target in a region of interest based on the RD spectrum includes: Performing non-coherent integration on the multi-channel RD spectrum, followed by constant false alarm probability processing based on noise estimation and wave direction estimation, thereby realizing target detection; or 2. The target detection method according to claim 1, further comprising: performing a constant false alarm probability process on each of the multi-channel RD spectra, followed by a channel domain binary integration process and wave arrival direction estimation, and then performing one of the following operations on the target: judgment, location, and recognition.

23. 23. The method of claim 22, wherein the constant false alarm probability processing is a DAE CFAR processing for each azimuth angle and elevation / depression angle.

24. Implementing target detection in a region of interest based on the RD spectrum includes:

10. The method of claim 1, further comprising: performing target recognition based at least in part on extracting multiple frames of accumulated data, frame-to-frame Fourier transformed data, non-coherent integrated data, and / or constant false alarm probability detection data.

25. 1. An integrated circuit comprising: a signal transmission module configured to be used for electromagnetic waves for target detection; a signal receiving module configured to receive echoes formed by the reflected and / or scattered electromagnetic waves; a processing module configured to be used for processing the echoes according to the target detection method of any one of claims 1 to 24 to realize target detection.

26. The integrated circuit of claim 25, wherein the processing module comprises a baseband unit and an MCU unit, the baseband unit being configured to be used to realize distance dimension FFT processing and inter-frame FFT processing in the method of any one of claims 1 to 24, and the MCU unit being configured to be used to realize frame data accumulation and target detection in a region of interest in the method of any one of claims 1 to 24.

27. 27. The integrated circuit of claim 26, wherein when the method includes digital beam combining, the baseband unit is configured for use in implementing the digital beam combining.

28. 26. The integrated circuit of claim 25, wherein the integrated circuit is a millimeter-wave chip or a sensor chip.

29. An electromagnetic wave sensor, Career and an integrated circuit according to any one of claims 25 to 28 mounted on the carrier; an antenna, the antenna is mounted on the carrier, or the antenna is integrated with the integrated circuit as an integrated device and mounted on the carrier; The electromagnetic wave sensor, wherein the integrated circuit is connected to the antenna and is used to transmit the electromagnetic wave signal and / or receive the echo signal.

30. A terminal device, A device body, The electromagnetic wave sensor according to claim 29, which is installed in the device body, The terminal device, wherein the electromagnetic wave sensor is used for target detection and / or communication, thereby providing reference information for the operation of the device body.

31. A non-transitory computer-readable storage medium having computer-readable instructions stored thereon, the computer-readable instructions, when executed by a processor, causing the processor to perform the target detection method of any one of claims 1 to 24.

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