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

Abstract

The embodiment of the invention provides a target detection method, an integrated circuit, an electromagnetic wave sensor, equipment and a computer readable storage medium, which can improve the accuracy of target detection. The method comprises the following steps: performing intra-chirp distance dimension FFT (Fast Fourier Transform) and inter-frame speed dimension FFT on an echo signal to obtain distance and speed dimension data; performing first constant false alarm processing on the distance velocity dimension data to obtain candidate target detection points; and performing second constant false alarm processing on the candidate target detection point, and performing target detection based on a second constant false alarm processing result.

Description

Technical Field

[0001] The embodiments of the present disclosure relate to, but are not limited to, the field of target detection technology, and in particular, to a target detection method, an integrated circuit, an electromagnetic wave sensor, a device, and a computer-readable storage medium. Background Art

[0002] The echo signal processing of the target detection system generally includes the following processes: frequency mixing, analog-to-digital conversion, sampling, distance dimension Fourier transform, velocity dimension Fourier transform, two-dimensional constant false alarm detection, etc. However, when performing target detection in a short-distance sealed or relatively sealed space area, such as echo signal processing in a car cabin, due to the influence of strong static clutter and multipath, there will be problems such as low detection rate, more false alarm targets, inaccurate angle estimation, etc., resulting in low accuracy of target detection. Summary of the invention

[0003] The embodiments of the present disclosure provide a method, an integrated circuit, an electromagnetic wave sensor, a device, and a computer-readable storage medium for target detection, which can improve the accuracy of target detection.

[0004] On the one hand, an embodiment of the present disclosure provides a target detection method, including: performing distance-dimensional FFT processing based on the echo signal to obtain 1D-FFT data, performing multi-frame joint processing on the 1D-FFT data to obtain an RD spectrum; and realizing detection of targets in a region of interest based on the RD spectrum.

[0005] In an exemplary embodiment, the distance dimension FFT processing is performed based on the echo signal to obtain 1D-FFT data, and the 1D-FFT data is subjected to multi-frame joint processing to obtain an RD spectrum, including: performing distance dimension FFT processing within the chirp on the echo signal to obtain 1D-FFT data; accumulating frame data on the 1D-FFT data until a preset amount of data is accumulated, and then performing inter-frame FFT processing to obtain an RD spectrum, i.e., a distance Doppler spectrum or data. The period length of the preset amount can be set based on the period length of the characteristic parameter of the detected target. For example, when performing breathing detection on a person, the number of frames can be set based on the breathing frequency range of humans (specific groups such as adults or infants can also be selected), and then inter-frame FFT processing can be performed based on the frame data of the predetermined number of frames to achieve operations such as detection and identification of the human body in the target area.

[0006] In an exemplary embodiment, the detecting of a target in a region of interest based on the RD map includes:

[0007] The RD spectra of each channel are incoherently accumulated, and a first constant false alarm processing based on noise estimation is performed based on the incoherent accumulation result to obtain a candidate target detection point; a second constant false alarm processing is performed on the candidate target detection point, and target detection is performed based on the second constant false alarm processing result.

[0008] On the other hand, an embodiment of the present disclosure provides a target detection method, including: performing range dimension FFT within the chirp and speed dimension FFT between frames on the echo signal to obtain range and speed dimension data; performing a first constant false alarm processing on the range and speed dimension data to obtain a candidate target detection point; performing a second constant false alarm processing on the candidate target detection point, and performing target detection based on the second constant false alarm processing result.

[0009] In an exemplary embodiment, the method of performing distance-dimensional FFT within the chirp and speed-dimensional FFT between frames on the echo signal to obtain distance-speed dimension data includes: performing distance-dimensional FFT processing on the echo signal within the chirp to obtain 1D-FFT data; accumulating frame data of the 1D-FFT data until a preset amount of data is accumulated, and then performing speed-dimensional FFT processing between frames to obtain distance-speed dimension data.

[0010] In an exemplary embodiment, performing a first constant false alarm processing on the distance and speed dimension data to obtain a candidate target detection point includes: performing incoherent accumulation on the distance and speed dimension data of multiple channels, and performing a first constant false alarm processing based on noise estimation based on the incoherent accumulation results to obtain a candidate target detection point.

[0011] In an exemplary embodiment, the second constant false alarm processing includes: performing DAECFAR processing on the azimuth angle and the elevation angle respectively.

[0012] In an exemplary embodiment, the performing a second constant false alarm processing on the candidate target detection points includes: performing a digital beamforming processing on the candidate target detection points, and then performing a second constant false alarm processing based on noise estimation to obtain a screening result in which false target points are filtered out.

[0013] In an exemplary embodiment, the second constant false alarm processing on the candidate target detection point includes: performing DBF in the azimuth dimension on the candidate target detection point, performing constant false alarm processing in the azimuth dimension in combination with the noise floor estimation result, to obtain a target screening result in the azimuth dimension; and / or performing DBF in the elevation dimension on the candidate target detection point, performing constant false alarm processing in the elevation dimension in combination with the noise floor estimation result, to obtain a target screening result in the elevation dimension.

[0014] In an exemplary embodiment, the performing a second constant false alarm processing on the candidate target detection point includes: performing azimuth and elevation two-dimensional DBF on the candidate target detection point, performing azimuth and elevation constant false alarm processing in combination with a noise floor estimation result, and obtaining an azimuth and elevation two-dimensional target screening result.

[0015] In an exemplary embodiment, the noise floor estimation result is obtained in the following manner: statistics are performed on the DBF spectrum of the current dimension, and the quantile value is selected as the noise floor estimation result of the current dimension.

[0016] In an exemplary embodiment, when performing DBF, the amplitudes of all peaks in the DBF spectrum are obtained. For any peak, if its signal-to-noise ratio exceeds a predetermined threshold, and the difference between its amplitude and the global maximum amplitude is within a preset range, and the number of peaks that have been output is less than a preset number, then the peak is output as a target screening result.

[0017] In an exemplary embodiment, the second constant false alarm processing is performed on the candidate target detection point, and target detection is performed based on the second constant false alarm processing result, including: during the second constant false alarm processing, one or more of the following operations are performed on the candidate target detection point to obtain a target detection result: global maximum value screening, first threshold value screening, and second threshold value screening, wherein the global maximum value screening is used to screen whether the global maximum value is a target detection point, the first threshold value screening determines whether the current candidate screening result is a target detection point based on the relationship between the candidate screening result and the noise floor estimation value, and the second threshold value screening determines whether the current candidate screening result is a target detection point based on the relationship between the candidate screening result and the global maximum value.

[0018] In an exemplary embodiment, the global maximum screening includes: determining whether the difference between the digital beamforming spectrum amplitude value corresponding to the current global maximum value and the noise floor estimation value is within a first preset range; if so, the candidate screening result corresponding to the global maximum value is a target detection point; the first threshold value screening includes: determining whether the difference between the current candidate screening result and the noise floor estimation value is within a second preset range; if so, the current candidate screening result is a target detection point; the second threshold value screening includes: determining whether the difference between the power value of the current candidate screening result and the power of the global maximum value is within a third preset range; if so, the current candidate screening result is a target detection point.

[0019] In an exemplary embodiment, the incoherent accumulation of the distance-speed dimension data of multiple channels and the first constant false alarm processing based on noise estimation based on the incoherent accumulation results include: incoherent accumulation of the distance-speed dimension data of multiple channels, estimating the noise floor of each distance unit after the incoherent accumulation, obtaining the noise floor estimation value of each distance unit, and performing incoherent constant false alarm processing using the noise floor estimation value, wherein, when estimating the noise floor of each distance unit after the incoherent accumulation, the noise floor of each distance unit is adjusted using the global noise floor.

[0020] In an exemplary embodiment, when estimating the noise floor of each distance unit after the incoherent accumulation, the noise floor of each distance unit is adjusted using the global noise floor, including: adjusting the noise floor of each distance unit in the following manner: i ′=min(n i ,α*n g ), where n i is the original noise floor estimate of the ith distance unit, and n g is the mean of the noise floor estimates of multiple distance cells, n i ′ is the adjusted noise floor estimate of the ith distance unit, α>1.

[0021] In an exemplary embodiment, after accumulating a preset amount of data, performing inter-frame velocity-dimensional FFT processing may include: using a sliding window method to read 1D-FFT data of a preset number of frames each time to perform inter-frame velocity-dimensional FFT processing.

[0022] In an exemplary embodiment, the target detection based on the second constant false alarm processing result includes: pre-dividing the area of ​​interest into multiple target sub-areas, determining the number and position of target points in each sub-area according to the target detection point positions obtained after target detection, and judging whether there is a target to be detected in the current sub-area according to whether the ratio of the number of target points in each sub-area to the total number of targets is greater than a preset ratio value.

[0023] On the other hand, an embodiment of the present disclosure also provides a method for target detection, which is applied to target detection in a specific target area. The method includes: after performing a first constant false alarm processing on the range-Doppler data, continuing to perform a second constant false alarm processing in the angle dimension to obtain target data.

[0024] On the other hand, an embodiment of the present disclosure also provides an integrated circuit, including: a signal transmitting module, configured to transmit electromagnetic waves for target detection; a signal receiving module, configured to receive echoes formed by reflection and / or scattering of the electromagnetic waves; and a processing module, configured to process the echo signals according to the target detection method as described above to achieve target detection.

[0025] In an exemplary embodiment, the processing module includes a baseband unit, and the baseband unit can be configured to implement the distance dimension FFT, velocity dimension FFT, first constant false alarm processing and second constant false alarm processing as described above.

[0026] In an exemplary embodiment, the processing module further includes an MCU unit, and when the method includes digital beamforming and frame data accumulation, the baseband unit is configured to implement the digital beamforming, and the MCU unit is configured to implement the frame data accumulation.

[0027] In an exemplary embodiment, the integrated circuit is a millimeter wave chip or a sensor chip.

[0028] On the other hand, an embodiment of the present disclosure further provides an electromagnetic wave sensor, comprising: a carrier; an integrated circuit as described above, arranged on the carrier; an antenna, arranged on the carrier, or the antenna and the integrated circuit are integrated into a single device and arranged on the carrier; wherein the integrated circuit is connected to the antenna for transmitting the electromagnetic wave signal and / or receiving the echo signal.

[0029] On the other hand, an embodiment of the present disclosure further provides a terminal device, comprising: a device body; and an electromagnetic wave sensor as described above, which is arranged on the device body; wherein the electromagnetic wave sensor is used for target detection and / or communication to provide reference information for the operation of the device body.

[0030] On the other hand, an embodiment of the present disclosure further provides a non-transitory computer-readable storage medium having computer-readable instructions stored thereon, which, when executed by a processor, enables the processor to execute the target detection method as described above.

[0031] Other features and advantages of the embodiments of the present disclosure will be described in the following description, and partly become apparent from the description, or understood by practicing the present disclosure. Other advantages of the present disclosure can be realized and obtained by the schemes described in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The accompanying drawings are used to provide an understanding of the technical solution of the present disclosure and constitute a part of the specification. Together with the embodiments of the present disclosure, they are used to explain the technical solution of the present disclosure and do not constitute a limitation on the technical solution of the present disclosure. The shapes and sizes of the components in the accompanying drawings do not reflect the actual proportions and are only intended to illustrate the contents of the present disclosure.

[0033] Figure 1 This is a flow chart of the target detection method according to an embodiment of the present disclosure;

[0034] Figure 2AIt is a flowchart of realizing target detection based on frame-level FFT combined with DAE CFAR in an embodiment of the present disclosure;

[0035] Figure 2B A schematic diagram of another process of realizing target detection based on frame-level FFT combined with DAE CFAR in an embodiment of the present disclosure

[0036] Figure 3 Based on Figure 2A or Figure 2B A schematic diagram of azimuth or elevation CFAR in the target detection process shown;

[0037] Figure 4 This is a schematic diagram of the result after incoherent accumulation;

[0038] Figure 5 The local graph for the incoherent accumulation results;

[0039] Figure 6 This is a schematic diagram of the target point of the sixth area in the embodiment of the present disclosure;

[0040] Figure 7A-7D The processing results for the single-person scenario (baby in aisle C) are shown;

[0041] Figure 8A-8D The processing results for a two-person scenario (baby in seat B and adult in seat A) are shown. DETAILED DESCRIPTION

[0042] The present disclosure describes multiple embodiments, but the description is exemplary rather than restrictive, and it is apparent to those skilled in the art that there may be more embodiments and implementations within the scope of the embodiments described in the present disclosure. Although many possible feature combinations are shown in the drawings and discussed in the specific embodiments, many other combinations of the disclosed features are possible. Unless specifically limited, any feature or element of any embodiment may be used in combination with any other feature or element in any other embodiment, or may replace any other feature or element in any other embodiment.

[0043] The present disclosure includes and contemplates combinations of features and elements known to those of ordinary skill in the art. The embodiments, features, and elements disclosed in the present disclosure may also be combined with any conventional features or elements to form a unique invention scheme defined by the claims. Any features or elements of any embodiment may also be combined with features or elements from other invention schemes to form another unique invention scheme defined by the claims. Therefore, it should be understood that any feature shown and / or discussed in the present disclosure may be implemented individually or in any appropriate combination. Therefore, except for the limitations made according to the attached claims and their equivalents, the embodiments are not subject to other limitations. In addition, various modifications and changes may be made within the scope of protection of the attached claims.

[0044] In addition, when describing representative embodiments, the specification may have presented the method and / or process as a specific sequence of steps. However, to the extent that the method or process does not rely on the specific order of the steps described herein, the method or process should not be limited to the steps in the specific order described. As will be appreciated by those of ordinary skill in the art, other orders of steps are also possible. Therefore, the specific order of the steps set forth in the specification should not be interpreted as a limitation to the claims. In addition, the claims for the method and / or process should not be limited to the steps performed in the order written, and those skilled in the art can easily understand that these orders can be changed and still remain within the spirit and scope of the disclosed embodiments.

[0045] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which the present disclosure belongs. The terms used in the specification of the present disclosure are only for describing the embodiments and are not intended to limit the present disclosure.

[0046] When the target detection system detects a target within its detectable area, the transmitting antenna in the target detection system can send a frequency modulated continuous wave (FMCW) signal containing a number of chirps. The FMCW signal is refracted and / or reflected by the target to form an echo signal, which can be received by the receiving antenna in the target detection system. For the received echo signal, the target detection system can perform mixing, analog-to-digital conversion (ADC), sampling, fast Fourier transform (FFT), constant false alarm rate (CFAR), direction of arrival (DoA) estimation and other processing on the echo signal, so as to obtain a data spectrum containing dimensions such as the distance (Rang), velocity (Velocity), and angle (Angle) of the target relative to the detection system, wherein the velocity dimension in the data spectrum can also be called the Doppler dimension, and the angle may include the azimuth (Azimuth) and / or the elevation (Elevation).

[0047] When performing target detection in a sealed or relatively sealed space area, in some optional embodiments, target detection can be achieved by performing regional judgment based on single-frame fast and slow time processing, CFAR (constant false alarm) detection, angle measurement obtained point cloud, etc., or by using Capon and other algorithms to perform DoA (direction of arrival estimation) and other operations based on single-frame fast time processing, thereby forming an RA (Range-Azimuth, distance-azimuth) diagram and extracting features such as power in a predetermined area for identification and judgment to achieve target detection, or by detecting human breathing and heartbeat after filtering the phase of the received echo. For example, when target detection is achieved by performing regional judgment based on single-frame fast and slow time processing, CFAR detection, angle measurement obtained point cloud, after performing 1D-FFT (such as range-dimensional FFT) processing on the echo signal, time correlation analysis can be performed based on the echo data to achieve human target discrimination, and then position judgment can be performed by combining point cloud information, etc., to ultimately achieve the purpose of in-cabin target detection.

[0048] The present disclosure provides a target detection method, such as Figure 1 As shown, including:

[0049] Step 10, after performing range-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; the RD spectrum is, for example, a range-doppler two-dimensional graph, and the spectrum contains range Doppler data, that is, it contains range-velocity two-dimensional data.

[0050] Step 20: Detect the target in the region of interest based on the RD map.

[0051] The above detection of the target in the region of interest includes at least one of the following operations: judgment, positioning, identification and the like.

[0052] The target detection method and related devices provided in the embodiments of the present disclosure can realize the solution of target detection in sealed space areas such as cabins, indoors, and factories through multi-frame joint processing technology, that is, through inter-frame accumulation, so as to effectively improve the detection rate, reduce the number of false alarm targets, and greatly improve the accuracy of angle estimation, etc., so that special targets or weak targets such as infants and young children in the cabin can be accurately detected, and applications such as CPD (Child Presence Detection) and SBR (Safety Belt Reminder) can be realized.

[0053] The disclosed embodiments also provide a target detection method, which may include: performing a distance dimension FFT within the chirp and a speed dimension FFT between frames on the echo signal to obtain distance and speed dimension data; performing a first constant false alarm processing on the distance and speed dimension data to obtain a candidate target detection point; performing a second constant false alarm processing on the candidate target detection point, and performing target detection based on the second constant false alarm processing result.

[0054] In the embodiment of the present application, two constant false alarm processes are performed to effectively eliminate false alarms and effectively improve the detection and measurement performance, thereby enabling special targets or weak targets such as infants in the cabin to be accurately detected, thus realizing applications such as CPD and SBR.

[0055] In an exemplary embodiment, the step of obtaining the distance-speed dimension data includes: performing distance-dimensional FFT processing on the echo signal within the chirp to obtain 1D-FFT data; accumulating frame data on the 1D-FFT data until a preset amount of data is accumulated, and then performing inter-frame speed-dimensional FFT processing to obtain an RD spectrum, i.e., the distance-speed dimension data. For example, a sliding window method can be used to read a preset number of frames of 1D-FFT data each time to perform inter-frame FFT processing.

[0056] In the process of implementation, accurate detection of targets in the cabin can be achieved based on multi-frame joint processing technology. The multi-frame joint processing solution can be to obtain the range-Doppler map by sliding window FFT of multi-frame data, or to perform CFAR, DOA and other processing operations on the area of ​​interest after using FIR (Finite Impulse Response) or other complex time-frequency transformation processing, and can be combined with the set area judgment logic and area parameters to achieve detection and positioning operations for living targets such as adults, children, pets, or other non-living targets. CFAR can be NR-CFAR in the Doppler dimension, or RD-CFAR, or DAE (Doppler-Azimuth-Elevation)-CFAR, etc. After CFAR and DoA, post-processing such as clustering, false alarm suppression, and multiple processing point cloud association can be used. The regional parameters can be determined by adopting at least one of the following schemes, or a combination of at least two schemes, including clustering operation on the point cloud detected after a certain sliding window multi-frame processing, outlier detection and elimination operation on the point cloud detected after a certain sliding window multi-frame processing, etc.

[0057] In an exemplary embodiment, the step of performing the first constant false alarm processing includes: performing incoherent accumulation on RD spectra of multiple channels, performing the first constant false alarm processing based on noise estimation based on the incoherent accumulation results, and obtaining candidate target detection points.

[0058] Optionally, the first constant false alarm processing includes: performing incoherent accumulation on the RD spectra of multiple channels, estimating the noise floor of each distance unit after the incoherent accumulation, obtaining a noise floor estimation value of each distance unit, and performing incoherent constant false alarm processing using the noise floor estimation value, wherein, when estimating the noise floor of each distance unit after the incoherent accumulation, the noise floor of each distance unit is adjusted using the global noise floor.

[0059] An optional adjustment method includes: adjusting the noise floor of each distance unit in the following manner: i ′=min(n i ,α*n g ), where n i is the original noise floor estimate of the ith distance unit, and n g is the mean of the noise floor estimates of multiple distance cells, n i ′ is the adjusted noise floor estimate of the ith range unit, and α> 1. By adjusting the noise floor, it is possible to avoid the failure to detect the target due to the high noise floor.

[0060] In an exemplary embodiment, the second constant false alarm processing includes: performing DAE (Doppler-Azimuth-Elevation) CFAR processing on the azimuth angle and the elevation angle respectively.

[0061] In an exemplary embodiment, the step of performing the second constant false alarm processing includes: performing digital beamforming processing on the candidate target detection points, and then performing the second constant false alarm processing based on noise estimation to obtain a screening result in which false target points are filtered out.

[0062] One feasible method is: performing DBF in the azimuth dimension on the candidate target detection point, performing constant false alarm processing in the azimuth dimension in combination with the noise floor estimation result, and obtaining a target screening result in the azimuth dimension; and / or performing DBF in the elevation dimension on the candidate target detection point, performing constant false alarm processing in the elevation dimension in combination with the noise floor estimation result, and obtaining a target screening result in the elevation dimension.

[0063] Another achievable method is: performing azimuth and elevation two-dimensional DBF on the candidate target detection points, performing azimuth and elevation constant false alarm processing in combination with the noise floor estimation result, and obtaining azimuth and elevation two-dimensional target screening results.

[0064] The above noise floor estimation result may be obtained by performing statistics on the DBF spectrum of the current dimension, and selecting a quantile value (such as a median or a mean) as the noise floor estimation result of the current dimension.

[0065] Exemplarily, the azimuth angle constant false alarm processing, the elevation angle constant false alarm processing, or the azimuth angle and elevation angle constant false alarm processing can all be performed in the following manner:

[0066] During constant false alarm processing, one or more of the following operations are performed on the candidate target detection point to obtain a target detection result: global maximum value screening, first threshold value screening, and second threshold value screening, wherein the global maximum value screening is used to screen whether the global maximum value is a target detection point, the first threshold value screening determines whether the current candidate screening result is a target detection point according to the relationship between the candidate screening result and the noise floor estimation value, and is used to filter out false targets, and the second threshold value screening determines whether the current candidate screening result is a target detection point according to the relationship between the candidate screening result and the global maximum value, and is used to achieve multi-target detection.

[0067] Among them, the global maximum value screening includes: judging whether the difference between the digital beamforming spectrum amplitude value corresponding to the current global maximum value and the noise floor estimation value is within the first preset range, if it is within the first preset range, the candidate screening result corresponding to the global maximum value is the target detection point; the first threshold value screening includes: judging whether the difference between the current candidate screening result and the noise floor estimation value is within the second preset range, if it is within the second preset range, the current candidate screening result is the target detection point; the second threshold value screening includes: judging whether the difference between the power value of the current candidate screening result and the power of the global maximum value is within the third preset range, if it is within the third preset range, the current candidate screening result is the target detection point.

[0068] In an exemplary embodiment, when detecting a target in a region of interest based on the RD map, the region of interest is pre-divided into multiple target sub-regions, and the number and position of target points in each sub-region are determined based on the target detection point positions obtained after target detection. It is determined 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 ratio.

[0069] Among them, when applied to a closed space area or a relatively closed space area, the space area can be pre-divided to achieve the definition and detection of different areas of interest (divided areas). For example, when detecting targets in the cabin of a car, the radar monitored area can be divided into seat intervals and aisle intervals, etc., and then corresponding parameter types and thresholds can be preset for different types of areas, and the corresponding processing steps can be combined to achieve accurate detection of targets of interest in specific areas.

[0070] In some optional embodiments, for family cars, the space area in the car can generally be simply divided into head space, rear space and luggage space. If the rear space is the key monitoring area, the rear space can be further divided into seat intervals and aisle intervals, etc. The seat interval can also be divided into a corresponding number of seat interval units based on the seats. Similarly, the aisle interval can also be divided into a corresponding number of aisle interval units corresponding to the above-mentioned seat interval units. For example, for a five-seat family car, for the three seats in the rear space, three seat interval units and corresponding three aisle interval units can be divided. For different types of areas (or intervals or interval units), corresponding parameter types and thresholds can be preset, and adaptive signal data processing methods and steps can be used to achieve accurate detection of the target of interest (specific target) in specific intervals or interval units. Among them, adjacent interval units may have partial overlapping areas, or they may be adjacent or separated by gaps of preset widths.

[0071] The disclosed embodiment also provides a target detection method, which is applied to target detection in a specific target area. The method comprises: performing a first constant false alarm processing on the range-Doppler data, and then continuing to perform a second constant false alarm processing in the angle dimension to acquire target data.

[0072] The following is a detailed description of the technical solution of the present invention based on the technical idea of ​​frame-level Fourier transform with reference to the accompanying drawings, that is, after performing Fourier transform of the chirp in the echo signal in the fast time dimension, accumulating at least two frames of Fourier transform data in the fast time dimension, performing frame-dimensional Fourier transform of the at least two frames of data, and obtaining a range-Doppler (Range-Doppler, RD for short) spectrum, so as to estimate the target distance and / or speed based on the RD spectrum.

[0073] like Figure 2A As shown, after the echo signal is subjected to operations such as ADC (analog-to-digital) conversion and sampling processing, range DC removal (Range DC removal) and range Fourier transform (Range FFT, which can also be expressed as 1D-FFT or fast time FFT) are sequentially performed, and then multi-frame accumulation (such as storing 128 frames of data, store 128 frames) is performed, and Doppler DC removal (Doppler DC removal), frame Fourier transform (Frame FFT), non-coherent integration (Non-coherent integration) are continued, and then constant false alarm detection (such as peak-based CFAR (detection with peak selection)) is performed based on the noise obtained by noise estimation (such as using a variance-based noise estimator (noise variance estimator)); finally, angle detection and target classification and other processing are performed to obtain the target's distance, speed and / or angle information; for example, azimuth estimation and elevation estimation can be first performed based on the CFAR result, for example, DBF (digital beam It is implemented by technologies such as digital beamforming (forming) and DoA (direction of arrival estimation).

[0074] In some optional embodiments, for an electromagnetic wave sensor having a BB (Baseband) unit and an MCU (Microcontroller Unit) unit, it can be based on Figure 2BAs shown in the flowchart of target detection, frame data accumulation (store 128 frames) and target classification (Region HIST, classification) can be performed in the MCU module, while range DC removal (Range DC removal) and Doppler DC removal (Doppler DC removal) can be performed in the BB module or the MCU module. Figure 2B In the process shown, DC filtering of downlink ADC data in the BB module is taken as an example for explanation.

[0075] like Figure 2B As shown, after performing 1D-FFT processing in the BB module or obtaining distance dimension information, the 1D-FFT data can be cached in the MCU module, and when the preset data amount (such as 128, 56, or 32 frames) is cached, the data cached to the preset number of frames can be DC removed and frame-level FFT (frame-level FFT) can be performed in the frame dimension to obtain an RD (Range-Doppler) diagram.

[0076] When the system contains multiple channels, non-coherent integration can be performed on the RD spectra of the multiple channels, and the noise floor of multiple range bins in the accumulated data can be estimated using the NVE (noise variance estimator) module. For example, the noise floor estimation can be performed based on a preset formula, and subsequent NR (Noise Reference)-CFAR processing can be performed based on the value of the noise floor estimation. The above preset formula can be: i =min(n i ,α*n g ), n i is the noise floor estimate of the ith range bin, n g is the noise floor estimate of the last range bin of interest or multiple range bins of interest, α is a coefficient, which is a number greater than 1 and can be set and updated based on demand or engineering data, experience, etc. Taking the interior space of a vehicle as an example, the distance unit of interest refers to the distance unit determined based on prior knowledge (such as radar installation, vehicle geometry information, etc.) and considering a certain redundancy of the interior space range of the vehicle.

[0077] For angle estimation, DAE CFAR (Constant False Alarm Rate) can be used to estimate the azimuth and elevation angles for the CFAR detection point data. For example, after performing Doppler dimension CFAR detection on the inter-frame FFT data, perform azimuth dimension DBF (Azimuth DBF), and combine the NVE (noise variance estimator) noise floor estimation to continue azimuth CFAR processing (Azimuth CFAR); similarly, perform elevation dimension DBF (Elevation DBF) on the azimuth CFAR data, and combine the NVE noise floor estimation to continue elevation CFAR processing (Elevation CFAR), thereby obtaining the azimuth and elevation information of the target.

[0078] Figure 3 Based on Figure 2A or Figure 2B The schematic diagram of the azimuth or elevation CFAR in the target detection process is shown in the figure. Figure 3 As shown, the NVE engine can calculate the azimuth / elevation power data in the form of HIST (histogram) to obtain the quantile (rank) of the DBF spectrum energy, for example, the DBF spectrum of the current dimension is counted, and the azimuth / elevation DBF noise floor estimation can be performed by selecting a rank value (such as mean, median, etc.). When performing DBF, all peaks in the DBF spectrum can be obtained, and the SNR of all peak points and the estimated noise floor can be used to determine whether to output as a peak.

[0079] For example, when performing DBF, the amplitude values ​​of all peak points in the DBF spectrum, including the peak of the global maximum (Global max) and the peak of the local maximum (Local max), can be obtained; for any peak, if its SNR exceeds a predetermined 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 that have been output is less than the preset number, then the peak is output, that is, output as a real target point. Compared with the solution of directly using the peak in the Doppler spectrum to find the target, since the Doppler spectrum may contain clutter, the peak cannot reflect the real target, so it is more accurate to find the real target point through the above solution; secondly, there may be multiple targets in the Doppler spectrum. If only the peak is used to find the target, it will lead to the loss of target recognition. The target detection is performed by using this implementation method, taking into account the relationship between the candidate target and the global maximum amplitude, so accurate multi-target detection can be achieved.

[0080] In addition, the target points detected in each frame (or a preset amount of data, i.e. each time) can be counted according to a preset (or divided) area unit design, and the counting results can be judged according to preset rules to obtain whether there is a physical target of interest (such as an adult, child, infant or pet, etc.) in this processing and determine its location area.

[0081] In some optional embodiments, in the above target detection method, all FFT processing may be windowed FFT, or may be implemented by using SVA combined with FFT.

[0082] In some optional embodiments, in the above target detection method, the multi-frame sliding window FFT can be replaced by other time-frequency transform processing, such as short-time Fourier transform, fractional Fourier transform, etc., which can also achieve effective detection of the target of interest.

[0083] In some optional embodiments, in the above-mentioned target detection method, the multi-frame sliding window FFT can be replaced by a high-pass FIR, a Comb FIR, or a filter optimized and synthesized according to an optimization function, so as to achieve the purpose of obtaining similar or even higher performance of multi-frame FFT processing with fewer hardware resources.

[0084] In some optional embodiments, after the inter-frame FFT (Frame FFT) processing, two-dimensional digital beamforming (2D-DBF) processing can be directly performed, that is, after the inter-frame FFT, CFAR processing is not performed, but 2D-DBF operation is performed on the azimuth and elevation angles of multiple regions of interest (or unit intervals) to obtain RD spectra corresponding to the number of regions of interest; the above-mentioned RD spectra can be subsequently input into the constructed deep learning model (DL) for target judgment. In some optional embodiments, if the inter-frame FFT method is not used, but FIR, STFT (Short-Time Fourier Transform) and other methods are used for processing, the above-mentioned RD spectrum corresponds to FIR as a one-dimensional spectrum output, and the corresponding STFT is a three-dimensional spectrum for time-frequency transformation.

[0085] In some optional embodiments, the above target detection method is applicable to a TDM-MIMO system.

[0086] It should be noted that the embodiments of the present disclosure can refer to each other and be compatible without conflict, and the order and whether to set each functional module can be adjusted according to needs. For a system with a BB module and an MCU module, when the system detects a target by emitting electromagnetic waves and receiving corresponding echo signals, each step in each target detection method of the embodiments of the present disclosure can be configured to run in the BB module and / or the MCU module according to actual needs and the data processing capability and timeliness of the system operation. The relevant examples in the diagram can be used as a reference for some of the options.

[0087] In the above-mentioned embodiment, the frame-level FFT is used to replace the traditional chirp-level Doppler FFT to determine the speed of the target so as to achieve accurate detection of the target of interest, and the sliding window FFT processing can be performed on multiple frames to further improve the update frequency of the result, thereby improving the real-time performance of the system. In addition, the amount of data processed can be reduced by processing only the distance and / or Doppler area of ​​interest. In addition, when estimating the background noise, the background noise is obtained by taking the minimum value based on the estimate of the current range bin combined with the estimate of the last range bin of interest, which can be more suitable for application scenarios in relatively sealed environments such as in the cabin of a car. When judging the regional logic, by counting the target points detected in each frame according to the preset area design, and judging the counting results according to the preset rules, it is possible to accurately judge whether there is a target in each preset area.

[0088] Based on the above scheme, after testing with real data, the top-mounted radar installation scenario can effectively achieve a high detection rate, as well as extremely low missed detection rate and false alarm rate. For example, when detecting targets in the cabin, it can achieve a detection rate of more than 99%, a missed detection rate of less than 0.5%, and a false alarm rate of less than 1%.

[0089] The target detection method disclosed in the present invention is described in detail below through an application example, and the processing process is shown in FIG2 .

[0090] Step 1, when the target detection system detects the target within its detectable area, the transmitting antenna in the target detection system can send a frequency modulated continuous wave (FMCW) signal containing a plurality of chirps. The FMCW signal is refracted and / or reflected by the target to form an echo signal, and the echo signal can be received by the receiving antenna in the target detection system.

[0091] The target detection system may be an antenna array system or a radar system having at least one transceiver channel, such as a multiple input multiple output (MIMO) radar system or a single input multiple output (SIMO) system. The target detection system generally has N transmitting antennas (TX) and M receiving antennas (RX), where N and M are both positive integers greater than or equal to 1, and by reasonably arranging the position of each antenna, a virtual antenna array of N×M transceiver channels can be formed.

[0092] Step 2, performing distance dimension FFT (1D-FFT) processing on each chirp data of each frame of echo signal received to obtain distance dimension data, i.e., 1D-FFT data;

[0093] Before performing 1D-FFT processing, optionally, the chirp data can be firstly subjected to analog-to-digital conversion (ADC), and then the obtained ADC data is subjected to a first DC filtering process, wherein the first DC filtering process includes: calculating the average of the data collected by each RX channel of each chirp along the fast time dimension, and subtracting the DC component of all sampling points of each RX channel, wherein the fast time dimension refers to a data sequence formed by sampling the radar echo signal in the distance (or angle) direction, and DC filtering can eliminate the DC offset in the signal and make the center line of the signal zero.

[0094] Optionally, before 1D-FFT processing, the DC filtered data can be windowed, and then 1D-FFT processing can be performed to obtain distance dimension information. Spectral leakage can be reduced and the performance of spectrum estimation can be improved by windowing. The windowing operation is to multiply the original signal by a window function, and the window function can be a weight function with a specific shape, such as 64, 96 or 128. Common window functions include rectangular window, Hamming window, Hanning window, Blackman window, etc.

[0095] The 1D-FFT data can be moved from the BB to the SRAM cache in the CPU or MPU via DMA.

[0096] Step 3: Perform velocity dimension FFT (2D-FFT) processing on the 1D-FFT data of the preset number of frames to obtain distance dimension-velocity dimension data, namely, 2D-FFT data (or RD graph).

[0097] The velocity dimension can also be called the Doppler dimension, and the above-mentioned distance dimension-velocity dimension information can also be called distance dimension-Doppler information.

[0098] This step is different from the slow time processing between chirps in radar signal processing in the related art. By accumulating multiple frames of data and performing inter-frame 2D-FFT processing on the 1D-FFT data of a preset number of frames in a sliding window manner each time, the update frequency of the results can be increased, thereby improving the real-time performance of the system. The 2D-FFT processing in this step can also be called inter-frame FFT processing. Taking the preset number of frames as 128 and the sliding window step as 1 as an example, the 2D-FFT processing is performed on the 0th to 127th frames for the first time, and the 2D-FFT processing is performed on the 1st to 128th frames for the second time. Here, the sliding window step of 1 is used as an example. The sliding window step can be configured as a positive integer greater than or equal to 1, and the preset number of frames can also be configured.

[0099] Before performing 2D-FFT processing, optionally, a second DC filtering process can be performed on the 1D-FFT data. The second DC filtering process includes: calculating the complex average along the frame dimension of the 1D-FFT data of a preset number of frames, obtaining the average values ​​of different channels and different distances as their DC components, and subtracting the DC components from each range bin of each channel. A range bin refers to a small distance interval divided by the radar system along the radial direction (i.e., the direction of transmitting signals and receiving echoes). A range bin is used to represent the discrete distance interval used by the radar system when detecting and tracking targets.

[0100] Optionally, before 2D-FFT processing, the data after the second DC filtering can be windowed, and then 2D-FFT processing is performed along the frame dimension to obtain distance dimension-speed dimension data. Spectral leakage can be reduced and the performance of spectrum estimation can be improved by windowing. The windowing operation is to multiply the original signal by a window function, and the window function can be a weight function with a specific shape, and the window function is, for example, 64, 96 or 128.

[0101] For each chirp data of each frame, when the data cached in the SRAM obtains a certain number of frames, such as 128 frames, the 128 frames of data can be moved from the SRAM to the BB through DMA, and the following steps are continued.

[0102] Step 4, performing non-coherent integration (NCI) on the multi-channel 2D-FFT data to obtain accumulated data;

[0103] Any of the following formulas can be used for incoherent accumulation:

[0104]

[0105] Wherein, P(r,v) is the power at the rth distance unit and the vth Doppler unit, A(r,v) is the amplitude at the rth distance unit and the vth Doppler unit, and s(r,t,a,v) is the complex value of the echo signal at the tth transmitting channel and the ath receiving channel of the rth distance unit and the vth Doppler unit after the above 2D-FFT processing.

[0106] After incoherent accumulation, we get Figure 4 As shown in the results, the horizontal axis describes the Doppler value, i.e., speed, and the vertical axis describes the distance. The 0th distance unit includes all Doppler values ​​whose vertical axis is 0 in the figure.

[0107] Step 5, estimate the noise floor, i.e. background noise, of each distance unit in the accumulated data;

[0108] In this example, the following noise floor estimation method is used: i ′=min(n i ,α*n g ), where n i is the original noise floor estimate of the ith distance unit, and n g is the mean of the noise floor estimates of multiple distance cells, or the noise floor estimate of the last distance cell of interest, n i ′ is the noise floor estimate after adjustment for the ith distance unit, α>1. Figure 5 As shown in the figure, each grid represents the original noise floor estimation value of a distance unit. For example, the median or mean of each distance unit can be calculated as the original noise floor estimation value. The noise floor estimation value of multiple distance units can, for example, calculate the median of multiple original noise floor estimation values ​​shown in the dashed box ① in the figure. The noise floor estimation values ​​of multiple distance units as the global noise floor estimation value can help adjust the noise floor (or noise floor saturation processing). When the target (such as an adult) moves significantly, the entire Doppler spectrum value will be higher as a whole, resulting in the original noise floor estimation value being higher, which will cause the target to be undetectable during subsequent target screening. Adjusting the noise floor through the global noise floor estimation value can make subsequent target detection more accurate. The number and position of the distance units used to calculate the global noise floor estimation value can be configured. For example, the noise floor estimation values ​​of multiple distance units shown in the dashed box ② in the figure can be used as the global noise floor estimation value.

[0109] Step 6: Perform NR-CFAR (Non-Coherent Constant False Alarm Rate) to obtain candidate target detection points;

[0110] Step 7, perform the first target screening on the candidate target detection points;

[0111] For each candidate target detection point T iThe following operations are performed: extract the 2D-FFT data corresponding to the detection point, select the azimuth antenna and perform azimuth DBF (digital beamforming) according to the azimuth steering vector, obtain the DBF spectrum, the DBF spectrum shows the signal power or intensity in different directions changing with frequency, estimate the noise floor of the DBF spectrum and perform azimuth CFAR (which can be called Az-CFAR (Azimuth CFAR)), perform preset condition judgment on the result of azimuth CFAR, and use the candidate target points that meet the preset conditions as the first target screening results to form the first candidate target point set, which includes all candidate target points T that meet the preset conditions. i,j and its corresponding azimuth angle θ i,j .

[0112] The azimuth DBF spectrum is statistically obtained as follows Figure 3 The result of azimuth CFAR is shown in the figure (the result of elevation CFAR is similar). In the figure, the x-axis represents the azimuth, and the y-axis represents the DBF spectrum amplitude. If only the DBF amplitude peak is used as the target point, then both Globalmax and Localmax in the figure are used as target points, which may cause false alarms. Therefore, in order to suppress false targets, the following operations are performed:

[0113] The azimuth DBF spectrum is statistically analyzed, and a quantile value (such as the median or mean) is selected as the noise floor estimate of the azimuth DBF, which is Az_HIST as shown in the figure, denoted by n a ; Find the global maximum in the DBF spectrum, whose value is recorded as P max , judge if P max <β1·n a , β1>1, then the candidate target point corresponding to the global maximum value in the DBF spectrum is determined to be a false target point, and the processing of the target point is terminated. In the formula, β1 is one of the thresholds of the azimuth dimension CFAR. The above formula can be used to determine whether the global maximum value is a target detection point; if P max ≥β1·n a , then the following processing is performed:

[0114] Traverse all points on the DBF spectrum. If any point meets the following conditions, it is determined to be a candidate target point that meets the preset conditions:

[0115] P i ≥β2·n a Formula 1

[0116] P i ≥γ·P max Formula 2

[0117] P i ≥ρ1·P i-1 Formula 3

[0118] P i ≥ρ2·P i+1 Formula 4

[0119] Where i is the serial number of the candidate target point in the azimuth dimension, 0 <i<N a -1,N a is the number of points in the azimuthal DBF spectrum, P i is the power of the candidate target point, β2, γ, ρ1, ρ2 are settable parameters, 0<γ<1, β2>1. The above formula 1 indicates that the ratio of the power of the candidate target point to the noise floor needs to be above the preset threshold β2, which is equivalent to setting the first threshold, and the first threshold is the noise floor estimation value + the first threshold. By setting the first threshold, false target points can be filtered out; Formula 2 indicates that the ratio of the power of the candidate target point to the global maximum power needs to be above the preset threshold γ, which is equivalent to setting the second threshold, and the second threshold is the global maximum power value - the second threshold. By setting the second threshold, multi-target detection can be achieved, which is conducive to identifying targets such as children or infants; when ρ1=ρ2=1, the above formulas 3 and 4 indicate that the point is a local maximum.

[0120] Step 8, conduct a second target screening;

[0121] For each candidate target point T i,j According to its azimuth θ i,j and array arrangement to generate the pitch direction steering vector sv(i,j) for each candidate target point T i,j Perform pitch DBF to obtain its pitch DBF spectrum, estimate the noise floor of its pitch DBF spectrum and perform secondary CFAR (called El-CFAR), and perform preset condition judgment on the result of pitch CFAR. The candidate target points that meet the preset conditions are used as the second target screening results to form the second candidate target point set, which includes all candidate target points that meet the conditions T i,j,k and its corresponding pitch angle

[0122] The second target screening process is similar to the first target screening process. First, the pitch DBF is statistically analyzed to obtain the pitch CFAR result. The result is first judged and the formula P is used to calculate the pitch DBF result. max <β1·n a Determine whether the global maximum is the target detection point. If P max ≥β1·n a , then all points on the DBF spectrum are traversed, and any point that satisfies the above equations 1-4 is determined to be a candidate target point that meets the preset conditions.

[0123] The embodiment of the present disclosure performs secondary CFAR on the azimuth and elevation DBFs after performing NR-CFAR, thereby suppressing false targets and achieving, to a certain extent, resolution of multiple targets at the same distance and speed.

[0124] When generating the DBF spectrum of the pitch dimension in the above-mentioned El-CFAR, the pitch dimension steering vector is regenerated according to the azimuth information obtained by Az-CFAR. In an exemplary embodiment, the pitch dimension steering vector of a preset azimuth angle (such as 0° azimuth angle) can also be directly used.

[0125] The order of the first target screening in step 7 and the second target screening in step 8 can be exchanged. In some embodiments, only step 7 or only step 8 can be performed.

[0126] Step 9, target information extraction;

[0127] For all candidate target points T that meet the conditions i,j,k Extract information, including but not limited to the range bin index (range unit index) and Doppler bin index (Doppler unit index) of the target point. In addition, it can also include the SNR (signal-to-noise ratio) of RD-CFAR (Range-Doppler CFAR, range velocity domain CFAR), the SNR of Az-CFAR, the SNR of El-CFAR (Elevation CFAR, elevation angle CFAR), etc. The above SNR can be used for subsequent target personnel determination. For example, the corresponding target detection point can be given a weight according to the SNR value, and the weight is related to the SNR value.

[0128] Step 10, count all target points detected in each frame according to the preset area design, and judge the counting results according to the preset rules to obtain whether there is a person in this processing, and determine the location of the person, that is, obtain the target information.

[0129] One implementation of the preset area is shown in the following figure, where the horizontal axis is the X direction (vehicle width direction) and the Z direction (vehicle rear-to-vehicle front direction). The interior space of the vehicle is divided into areas to be determined along all or some dimensions of XYZ, or along all or some dimensions of the distance-azimuth-pitch dimension of the polar coordinate system. Figure 6 In one example, six areas are divided, including three rear seats and three aisles in front of three seats. These areas may overlap or not overlap with each other; some areas in the car may belong to multiple preset areas at the same time or not belong to any area.

[0130] Assume that a total of 11 target points are detected in a certain process, and the number of these target points in these preset areas is counted. In this example, the counts of these target points in the areas seat A, seat B, seat C, aisle A, aisle B and aisle C are: 0, 2, 6, 0, 1 and 0.

[0131] Taking the above partitioning and counting results as an example, judging whether there is a person in the processing and determining the location of the person includes the following steps:

[0132] Step 10.1 Initialize the flags flag_region_i of the six regions to be 0, i.e., no one exists (i=0, 1, ..., 5);

[0133] Step 10.2, determine whether the total valid target number total_valid_tgt_num is 0, if yes, determine that there is no one in the car and end, if not, execute step 10.3;

[0134] Step 10.3, traverse all regions, for region i, if the ratio of the statistical value of the number of valid targets tgt_num_region_i belonging to the region to the total number of valid targets total_valid_tgt_num exceeds a preset ratio value (for example, 25%, which can be set based on experience), then set the flag flag_region_i of region i to 1;

[0135] Step 10.4, if there is a region, if its flag_region_i is 1, then set flag_region_empty to 0, that is, there is someone in the car;

[0136] Step 10.5, traverse the seat regions, if there is any seat region where flag_region_i is 1, then determine that no one exists in the corresponding aisle region (for example, if it is determined that there is someone in seat A, then determine that no one exists in aisle A);

[0137] Step 10.6, output the results.

[0138] By dividing the area and setting the preset ratio value, the target person can be identified more accurately to prevent misjudgment.

[0139] In an exemplary embodiment, based on the first CFAR result (i.e., the result of step 6), a two-dimensional DBF spectrum of azimuth and elevation can be directly performed, and a second CFAR can be directly performed on the two-dimensional DBF spectrum of azimuth and elevation. The main steps include:

[0140] Extract 2D-FFT data, select antennas and perform two-dimensional DBF according to the azimuth and elevation two-dimensional steering vectors to obtain its DBF spectrum, estimate the noise floor of the DBF spectrum, and perform secondary CFAR (called AE-CFAR) to obtain all candidate target points T that meet the conditions. i,j and its corresponding azimuth angle θ i,j and pitch angle One possible way is to traverse all points on the DBF spectrum and determine that any point is a candidate target point if it satisfies the following conditions:

[0141] P i,j ≥β2·n a Formula 5

[0142] P i,j ≥γ·P max Formula 6

[0143] P i,j ≥ρ1·P i-1,j Formula 7

[0144] P i,j ≥ρ2·P i+1 ,j Formula 8

[0145] P i,j ≥ρ3·P i,j-1 Formula 9

[0146] P i,j ≥ρ4·P i,j+1 Formula 10

[0147] In the formula, i is the serial number of the point in the azimuth dimension, 0 <i<N a -1,N a is the number of points in the azimuth dimension DBF spectrum, j is the serial number of the point in the elevation dimension, 0 <j<N e -1,N e is the number of points in the pitch-dimensional DBF spectrum, P i,j is the power of the point, β2, γ, ρ1, ρ2, ρ3, ρ4 are settable parameters, 0<γ<1, β2>1. The above formula 5 indicates that the ratio of the power of the candidate target point to the noise floor needs to be above the preset threshold β2, which is equivalent to setting the first threshold, the first threshold is the noise floor estimation value + the first threshold, and the false target points can be filtered out by setting the first threshold; Formula 6 indicates that the ratio of the power of the candidate target point to the global maximum power needs to be above the preset threshold γ, which is equivalent to setting the second threshold, the second threshold is the global maximum power value - the second threshold, and multi-target detection can be achieved by setting the second threshold, which is conducive to identifying targets such as children or infants; when ρ1=ρ2=ρ3=ρ4=1, the above formulas 7-10 indicate that the point is a local maximum.

[0148] Fig. 7A -D shows the processing results for a single-person scene (baby in aisle C). There are 500 frames of data, 128 frames of inter-frame FFT, the sliding window length of the sliding window processing is 1, and a total of 373 processing times. Fig. 7A and Figure 7C The result of using NR-CFAR in the RD dimension only and then doing azimuth and elevation DoA (angle of arrival estimation); Figure 7B and Fig.7D The processing result is the NR-CFAR in the RD dimension followed by the azimuth-pitch DBF and the secondary CFAR. Fig. 7A and Figure 7B is the pitch-azimuth result, Figure 7C and Fig.7D It is the result of the azimuth-processing sequence number. It can be seen that the method of this embodiment (using NR-CFAR in the RD dimension and then performing azimuth-pitch DBF and secondary CFAR) can effectively reduce the number of invalid false target points, make the target points more concentrated, and effectively reduce the difficulty of post-processing and improve the detection effect.

[0149] Figures 8A-8D The processing results for a two-person scenario (baby in seat B and adult in seat A) are shown. Fig. 8A and Figure 8C The result of using NR-CFAR in the RD dimension only and then performing azimuth and elevation DoA. Figure 8B and Fig.8D The processing result is the NR-CFAR in the RD dimension followed by the azimuth-pitch DBF and the secondary CFAR. Fig. 8A and Figure 8B is the pitch-azimuth result, Figure 8C and Fig.8D It is the result of the position-processing sequence number. It can be seen that the method of this embodiment can effectively reduce the number of invalid false target points, make the target points more concentrated, and effectively reduce the difficulty of post-processing and improve the detection effect.

[0150] The embodiments of the present disclosure are not only applicable to in-vehicle human target detection and positioning scenarios, but also to other similar application scenarios such as indoor personnel detection and factory personnel detection.

[0151] The above operations can be performed in the MCU or in the baseband accelerator, or partially performed by the MCU and partially by the baseband accelerator. For example, 1D-FFT, 2D-FFT, CFAR and DBF are performed in the baseband accelerator, and target detection and position determination are performed in the MCU.

[0152] Compared with the existing technology, the CPD processing method proposed in the embodiment of the present disclosure mainly adopts a new inter-frame accumulation method, performs coherent accumulation on the frequency of human breathing, improves the signal-to-noise ratio of human targets and static strong clutter, and thus improves the detection performance; and uses CFAR secondary detection again on the DBF spectrum generated by the CFAR result in the azimuth / pitch dimension, effectively eliminates false alarms, effectively improves the detection and measurement performance, and thus enables special targets or weak targets such as infants and young children in the cabin to be accurately detected, realizing applications such as child detection (CPD) and seat belt reminder (SBR).

[0153] Performance evaluation was carried out through a large number of actual test experiments, and the results showed that the method proposed in the present invention can achieve a detection rate of more than 99%, a missed detection rate and a false alarm rate below 0.5% and 1%, which is significantly superior to the existing technology.

[0154] The disclosed embodiment also provides a method for target detection, which is applied to target detection in a specific target area, the method comprising: after performing a first constant false alarm processing on the range-Doppler data, continuing to perform a second constant false alarm processing in the angle dimension to obtain target data. The specific target area is, for example, a closed and / or semi-closed area such as a cabin or a room. The first constant false alarm processing and the second constant false alarm processing can refer to the description in the above embodiments.

[0155] It should be noted that the electromagnetic waves in the present disclosure may include radio waves and light waves. Radio waves include short waves, medium waves, long waves and microwaves, etc. Microwaves include centimeter waves (i.e. electromagnetic waves in the range of 3GHz to 30GHz, such as electromagnetic waves in the range of 3.1GHz to 10.6GHz, electromagnetic waves in the frequency band of 24GHz, etc.) and millimeter waves (i.e. electromagnetic waves in the range of 30GHz to 300GHz, such as electromagnetic waves in the frequency band of 60GHz, electromagnetic waves in the frequency band of 77GHz (such as 77GHz to 81GHz, etc.)), and light waves may include ultraviolet rays, visible light, infrared rays, and lasers, etc., among which the electromagnetic wave frequency band of lasers is (3.846 to 7.895)*10^5GHz, i.e., lasers are included in part of the frequency bands of ultraviolet rays and visible light.

[0156] The embodiments of the present disclosure also provide 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 reflection and / or scattering of the electromagnetic waves; and a processing module, configured to perform signal and data processing on the echoes to achieve target detection.

[0157] Optionally, the target detection is, for example, at least one of the following operations on the target in the region of interest: judgment, positioning and / or identification.

[0158] In an exemplary embodiment, the processing module includes at least a baseband unit, and the baseband unit can be configured to implement distance dimension FFT processing, velocity dimension FFT, first constant false alarm processing, and second constant false alarm processing in the method described in any embodiment of the present disclosure.

[0159] Optionally, in an exemplary embodiment, the processing module may further include an MCU unit, and when the method includes digital beamforming and frame data accumulation, the baseband unit is configured to implement the digital beamforming, and the MCU unit is configured to implement the frame data accumulation.

[0160] In an exemplary embodiment, the integrated circuit may be a millimeter wave radar chip or die.

[0161] The embodiment of the present disclosure also provides an integrated circuit, which may include a radio frequency module, an analog signal processing module and a digital signal processing module connected in sequence; 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; the digital processing module is used to perform analog-to-digital conversion on the intermediate frequency signal to obtain a digital signal; and based on the target detection method in the embodiment of the present disclosure, the digital signal is processed to achieve the purpose of target detection, for example, the integrated circuit may be a millimeter wave radar chip (chip or die). The above-mentioned digital processing module may include sub-units such as a BB unit and an MCU unit, and each sub-unit may be configured to execute the corresponding steps in the target detection method in the above-mentioned embodiment.

[0162] In some optional 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.

[0163] According to other embodiments of the present disclosure, an electromagnetic wave sensor is also proposed. The electromagnetic wave sensor may include an antenna, and an integrated circuit as described above. The integrated circuit is electrically connected to the antenna for receiving and transmitting electromagnetic wave signals. For example, the electromagnetic wave sensor may include: a carrier, an integrated circuit and an antenna as described in any of the above embodiments, and the integrated circuit may be arranged on the carrier; the antenna may be arranged on the carrier, or the integrated device may be integrated with the integrated circuit and arranged on the carrier (that is, the antenna may be an antenna provided in an AiP, AoP or AoC structure at this time); the integrated circuit is connected to the antenna (that is, the sensor chip or the integrated circuit is not integrated with an antenna at this time, such as a conventional SoC, etc.), for receiving and transmitting electromagnetic wave signals. The carrier may be a printed circuit board PCB, and the corresponding transmission line may be a PCB trace.

[0164] An embodiment of the present disclosure provides a terminal device, which may include: a device body; and an electromagnetic wave sensor as described above, which is arranged on the device body; wherein the electromagnetic wave sensor is used for target detection and / or communication to provide reference information for the operation of the device body.

[0165] The embodiment of the present disclosure also provides an electronic device (which can be understood as a terminal device), which can be expressed in the form of a general-purpose computing device. The 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 a storage unit and a processing unit), a display unit, etc. Among them, the storage unit stores a program code, and the program code can be executed by the processing unit so that the processing unit executes the method described in this specification according to various exemplary embodiments of the present disclosure. The storage unit may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) and / or a cache memory unit, and may further include a read-only memory unit (ROM).

[0166] The storage unit may also include a program / utility having a set (at least one) of program modules, such program modules including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0167] The bus may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0168] The electronic device may also communicate with one or more external devices (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may communicate with one or more devices that enable a user to interact with the electronic device, and / or may communicate with any device that enables the electronic device to communicate with one or more other computing devices (e.g., routers, modems, etc.). This communication may be performed through an input / output (I / O) interface. In addition, the electronic device may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) through a network adapter. The network adapter may communicate with other modules of the electronic device through a bus. It should be understood that, although not shown in the figure, 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, etc.

[0169] For example, the electronic device in the embodiments of the present disclosure may also include: a device body; and an electromagnetic wave sensor as described in any of the above embodiments arranged on the device body; wherein the electromagnetic wave sensor can be used to realize functions such as target detection and / or wireless communication.

[0170] On the basis of the above embodiments, in an optional embodiment of the present disclosure, the electromagnetic wave sensor may be arranged outside the device body, or arranged inside the device body, and in other optional embodiments of the present disclosure, the electromagnetic wave sensor may be arranged partially inside the device body and partially outside the device body. The embodiments of the present disclosure are not limited to this, and it may be determined according to circumstances.

[0171] In an optional embodiment, the device body can be a component or product used in fields such as smart cities, smart homes, transportation, smart homes, consumer electronics, security monitoring, industrial automation, in-cabin detection (such as smart cockpits), medical devices, and health care. For example, the device body can be intelligent transportation equipment (such as cars, bicycles, motorcycles, ships, subways, trains, etc.), security equipment (such as cameras), liquid level / flow rate detection equipment, smart wearable devices (such as bracelets, glasses, etc.), smart home devices (such as sweeping robots, door locks, televisions, air conditioners, smart lights, etc.), various communication devices (such as mobile phones, tablet computers, etc.), and gates, smart traffic lights, smart signs, traffic cameras, and various industrialized robotic arms (or robots), etc. It can also be various instruments for detecting life characteristic parameters and various devices equipped with the instruments, such as life characteristic detection in car cabins, indoor personnel monitoring, smart medical equipment, consumer electronic equipment, etc.

[0172] The embodiment of the present disclosure also provides a non-transitory computer-readable storage medium having computer-readable instructions stored thereon. When the instructions are executed by a processor, the processor executes the feeder unequal length compensation method as described above.

[0173] Through the description of the above implementation methods, it is easy for those skilled in the art to understand that the example implementation methods described here can be implemented by software, or by combining software with necessary hardware. The technical solution according to the implementation methods of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, or a network device, etc.) to execute the above method according to the implementation methods of the present disclosure.

[0174] The software product may use 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, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination thereof. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, 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 thereof.

[0175] Computer readable storage media may include data signals propagated in baseband or as part of a carrier wave, wherein readable program codes are carried. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The readable storage medium may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, device, or device. The program codes contained on the readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination thereof.

[0176] Program code for performing the operations of the present disclosure may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device may be connected to the user computing device through 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., through the Internet using an Internet service provider).

[0177] The computer-readable medium carries one or more programs. When the one or more programs are executed by a device, the computer-readable medium implements the aforementioned functions.

[0178] Those skilled in the art will appreciate that the above modules can be distributed in the device according to the description of the embodiment, or can be changed accordingly and only used in one or more devices different from the embodiment. The modules of the above embodiments can be combined into one module, or further divided into multiple sub-modules.

[0179] According to an embodiment of the present disclosure, a computer program is proposed, including a computer program or an instruction, which, when executed by a processor, can execute the method described above. In an optional embodiment, the above-mentioned integrated circuit can be a millimeter wave radar chip. The type of digital function module in the integrated circuit can be determined according to actual needs. For example, in a millimeter wave radar chip, a data processing module can be used for functions such as distance-dimensional Doppler transform, velocity-dimensional Doppler transform, constant false alarm detection, wave direction detection, point cloud processing, etc., to obtain information such as the distance, angle, speed, height, micro-Doppler motion characteristics, shape, size, surface roughness and dielectric properties of the target.

[0180] It should be noted that the electromagnetic wave sensor can realize functions such as target detection and / or communication by transmitting and receiving electromagnetic wave signals to provide the device body with detection target information and / or communication information, thereby assisting or even controlling the operation of the device body.

[0181] For example, when the above-mentioned device body is applied to an advanced driver assistance system (i.e., ADAS), the radio device (such as millimeter-wave radar) as a vehicle-mounted sensor can assist the ADAS system to realize application scenarios such as adaptive cruise control, automatic braking assistance (i.e., AEB), blind spot detection warning (i.e., BSD), assisted lane change warning (i.e., LCA), reversing assistance warning (i.e., RCTA), parking assistance, rear vehicle warning, collision avoidance, pedestrian detection, and in-cabin liveness detection (i.e., CPD).

[0182] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0183] The above-described embodiments only express the preferred embodiments of the present invention and the technical principles used, and the description thereof is relatively detailed, but it cannot be understood as limiting the scope of the invention patent. It is possible for those skilled in the art to make various obvious changes, readjustments and substitutions without departing from the protection scope of the present invention. Therefore, although the present invention is described in detail through the above embodiments, the present invention is not limited to the above embodiments, and may also include more other equivalent embodiments without departing from the concept of the present invention, and the protection scope of the patent of the present invention is determined by the scope of the attached claims.

[0184] It will be appreciated by those skilled in the art that all or some of the steps, systems, and functional modules / units in the methods disclosed above may be implemented as software, firmware, hardware, and appropriate combinations thereof. In hardware implementations, the division between the functional modules / units mentioned in the above description does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed by several physical components in cooperation. Some or all components may be implemented as software executed by a processor, such as a digital signal processor or a microprocessor, or implemented as hardware, or implemented as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or non-transitory medium) and a communication medium (or temporary medium). As known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as 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 tapes, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer. In addition, it is well known to those of ordinary skill in the art that communication media typically contain computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

Claims

1. A target detection method, characterized in that: include: Perform range dimension FFT within the chirp and velocity dimension FFT between frames on the echo signal to obtain range and velocity dimension data; Performing a first constant false alarm processing on the distance and speed dimension data to obtain a candidate target detection point; A second constant false alarm processing is performed on the candidate target detection point, and target detection is performed based on the second constant false alarm processing result.

2. The method according to claim 1, characterized in that The method of performing range dimension FFT within the chirp and speed dimension FFT between frames on the echo signal to obtain range and speed dimension data includes: After performing range-dimensional FFT processing on the echo signal within the chirp, 1D-FFT data is obtained; The 1D-FFT data is subjected to frame data accumulation until a preset amount of data is accumulated, and then speed dimension FFT processing is performed between frames to obtain distance-speed dimension data.

3. The method according to claim 1, characterized in that The step of performing a first constant false alarm processing on the distance and speed dimension data to obtain a candidate target detection point includes: Incoherent accumulation is performed on the distance and speed dimension data of multiple channels, and a first constant false alarm processing based on noise estimation is performed based on the incoherent accumulation result to obtain candidate target detection points.

4. The method according to claim 1, characterized in that The second constant false alarm processing includes: performing DAE CFAR processing on the azimuth angle and the elevation angle respectively.

5. The method according to claim 1, characterized in that The performing the second constant false alarm processing on the candidate target detection points includes: performing digital beamforming processing on the candidate target detection points, and then performing the second constant false alarm processing based on noise estimation to obtain a screening result in which false target points are filtered out.

6. The method according to claim 1, characterized in that The performing a second constant false alarm processing on the candidate target detection point includes: Performing azimuth dimension DBF on the candidate target detection point, performing azimuth constant false alarm processing in combination with the noise floor estimation result, and obtaining an azimuth dimension target screening result; and / or The pitch angle DBF is performed on the candidate target detection points, and pitch angle constant false alarm processing is performed in combination with the noise floor estimation result to obtain the pitch angle dimension target screening result.

7. The method according to claim 1, characterized in that The performing a second constant false alarm processing on the candidate target detection point includes: Perform azimuth and elevation two-dimensional DBF on the candidate target detection points, perform azimuth and elevation constant false alarm processing based on the noise floor estimation result, and obtain azimuth and elevation two-dimensional target screening results.

8. The method according to claim 6 or 7, characterized in that: The noise floor estimation result is obtained in the following way: The DBF spectrum of the current dimension is counted, and the quantile value is selected as the noise floor estimation result of the current dimension.

9. The method according to claim 6 or 7, characterized in that: When performing DBF, the amplitudes of all peaks in the DBF spectrum are obtained. For any peak, if its signal-to-noise ratio exceeds a predetermined threshold, and the difference between its amplitude and the global maximum amplitude is within a preset range, and the number of peaks that have been output is less than a preset number, then the peak is output as the target screening result.

10. The method according to claim 1, characterized in that The performing a second constant false alarm processing on the candidate target detection point, and performing target detection based on the second constant false alarm processing result, comprises: During the second constant false alarm processing, one or more of the following operations are performed on the candidate target detection point to obtain a target detection result: global maximum value screening, first threshold value screening, and second threshold value screening, wherein the global maximum value screening is used to screen whether the global maximum value is a target detection point, the first threshold value screening determines whether the current candidate screening result is a target detection point according to the relationship between the candidate screening result and the noise floor estimation value, and the second threshold value screening determines whether the current candidate screening result is a target detection point according to the relationship between the candidate screening result and the global maximum value.

11. The method according to claim 10, characterized in that The global maximum screening includes: determining whether the difference between the digital beamforming spectrum amplitude value corresponding to the current global maximum value and the noise floor estimation value is within a first preset range, and if within the first preset range, the candidate screening result corresponding to the global maximum value is the target detection point; The first threshold value screening includes: determining whether the difference between the current candidate screening result and the noise floor estimation value is within a second preset range, and if it is within the second preset range, the current candidate screening result is the target detection point; The second threshold value screening includes: judging whether the difference between the power value of the current candidate screening result and the power of the global maximum value is within a third preset range; if within the third preset range, the current candidate screening result is the target detection point.

12. The method according to claim 3, characterized in that The method of performing incoherent accumulation on the distance and speed dimension data of multiple channels and performing a first constant false alarm processing based on noise estimation based on the incoherent accumulation result comprises: Incoherent accumulation is performed on the distance and speed dimension data of multiple channels, and the noise floor of each distance unit after the incoherent accumulation is estimated to obtain a noise floor estimation value of each distance unit, and incoherent constant false alarm processing is performed using the noise floor estimation value, wherein when estimating the noise floor of each distance unit after the incoherent accumulation, the noise floor of each distance unit is adjusted using the global noise floor.

13. The method according to claim 12, characterized in that When estimating the noise floor of each distance unit after the incoherent accumulation, the noise floor of each distance unit is adjusted using the global noise floor, including: The noise floor of each range cell is adjusted in the following way: i ′=min(n i ,α*n g ), where n i is the original noise floor estimate of the ith distance unit, and n g is the mean of the noise floor estimates of multiple distance cells, n i ′ is the adjusted noise floor estimate of the ith distance unit, α>1.

14. The method according to claim 2, characterized in that After the data volume is accumulated to a preset amount, a speed dimension FFT process is performed between frames, including: The sliding window method is used to read the 1D-FFT data of a preset number of frames each time to perform velocity dimension FFT processing between frames.

15. The method according to claim 1, characterized in that The performing target detection based on the second constant false alarm processing result includes: The region of interest is pre-divided into multiple target sub-regions, and the number and position of target points in each sub-region are determined based on the position of the target detection points obtained after target detection. 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 ratio.

16. A method for target detection, characterized in that: Applied to target detection in a specific target area, the method comprises: After the first constant false alarm processing is performed on the range-Doppler data, the second constant false alarm processing in the angle dimension is continued to be performed to acquire the target data.

17. An integrated circuit, characterized in that: include: A signal transmitting module configured to transmit electromagnetic waves for target detection; A signal receiving module, configured to receive an echo formed by reflection and / or scattering of the electromagnetic wave; as well as A processing module is configured to process the echo signal according to any one of claims 1 to 15 or the method according to claim 16 to achieve target detection.

18. The integrated circuit of claim 17, wherein: The processing module includes a baseband unit, which can be configured to implement the distance dimension FFT, speed dimension FFT, first constant false alarm processing and second constant false alarm processing in the target detection method according to any one of claims 1 to 15 or claim 16.

19. The integrated circuit of claim 18, wherein: The processing module further includes an MCU unit. When the method includes digital beamforming and frame data accumulation, the baseband unit is configured to implement the digital beamforming, and the MCU unit is configured to implement the frame data accumulation.

20. The integrated circuit according to claim 17, characterized in that The integrated circuit is a millimeter wave chip or a sensor chip.

21. An electromagnetic wave sensor, characterized in that: include: Carrier; The integrated circuit according to any one of claims 17 to 20, arranged on the carrier; An antenna is arranged on the carrier, or the antenna and the integrated circuit are integrated into one device and arranged on the carrier; Wherein, the integrated circuit is connected to the antenna and is used for transmitting the electromagnetic wave signal and / or receiving the echo signal.

22. A terminal device, characterized in that: include: Equipment body; as well as The electromagnetic wave sensor as claimed in claim 21 is disposed on the device body; The electromagnetic wave sensor is used for target detection and / or communication to provide reference information for the operation of the device body.

23. A non-transitory computer-readable storage medium having computer-readable instructions stored thereon, which, when executed by a processor, causes the processor to execute the target detection method according to any one of claims 1 to 15 or claim 16.

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