Target detection method, integrated circuit, sensor and equipment

Abstract

The embodiment of the invention relates to the technical field of electromagnetic wave sensors, and discloses a target detection method, an integrated circuit, a sensor and equipment. The invention discloses a target detection method, and the method comprises the steps: carrying out the independent constant false alarm processing of at least two receiving and transmitting channels based on a distance-Doppler spectrum, so as to obtain the candidate target data of the at least two receiving and transmitting channels; and processing the candidate target data based on the at least two transceiving channels to obtain final target data. At least constant false alarm processing is independently carried out and then combined processing is carried out through a receiving and transmitting channel, a scheme of target detection in space areas such as an automobile cabin, a room and a factory building is realized, the detection rate can be effectively improved, the false alarm target number is reduced, the angle estimation accuracy can be greatly improved, and the method is suitable for large-scale popularization and application. Therefore, special targets or weak targets such as infants and the like in the cabin can be accurately detected, and applications such as CPD and SBR are realized.

Description

Technical Field

[0001] The embodiments of the present application relate to the technical field of electromagnetic wave sensors, and in particular to a target detection method, an integrated circuit, a sensor, and a device. Background Art

[0002] When performing target detection in a sealed or relatively sealed space area, such as detecting targets in a car cabin, there may be technical problems such as low detection rate, a large number of false alarm targets, and inaccurate angle estimation. Summary of the invention

[0003] The embodiments of the present application provide a target detection method, integrated circuit, sensor and device, which are at least conducive to performing constant false alarm processing separately and then jointly processing through the transceiver channel to realize a solution for target detection in spatial areas such as automobile cabins, indoors, and factory buildings. While effectively improving the detection rate and reducing the number of false alarm targets, it can also greatly improve the accuracy of angle estimation and other effects, thereby enabling special targets or weak targets such as infants and young children in the cabin to be accurately detected, realizing applications such as CPD (Child Presence Detection) and SBR (Safety Belt Reminder).

[0004] According to some embodiments of the present application, a first aspect of the embodiments of the present application provides a target detection method, comprising: based on the range-Doppler spectrum, performing constant false alarm processing on at least two transceiver channels separately to obtain candidate target data of at least two transceiver channels; processing the candidate target data based on at least two transceiver channels to obtain final target data.

[0005] According to some embodiments of the present application, a second aspect of the embodiments of the present application provides a target detection method, including: performing target detection according to the target detection method described in the first aspect of the embodiments of the present application to obtain a target; based on the targets falling into each preset area, verifying the targets in each preset area, the preset areas being obtained by dividing the detection space; based on the targets that pass the verification, outputting the detection results for each preset area.

[0006] According to some embodiments of the present application, a third aspect of the embodiments of the present application provides an integrated circuit, comprising a radio frequency module, an analog signal processing module and a digital signal processing module connected in sequence; wherein the radio frequency module is used to generate a radio frequency transmission signal and receive a radio frequency reception signal; the analog signal processing module is used to down-convert the radio frequency reception signal to obtain an intermediate frequency signal; the digital signal processing module is used to perform analog-to-digital conversion on the intermediate frequency signal, and based on the range-Doppler spectrum, perform constant false alarm processing on at least two transceiver channels separately to obtain candidate target data of at least two transceiver channels; the candidate target data of at least two transceiver channels are processed to obtain final target data.

[0007] According to some embodiments of the present application, the fifth aspect of the embodiments of the present application further provides an electromagnetic wave sensor, comprising: a carrier; the integrated circuit as described in the fourth aspect of the embodiments of the present application, which is arranged on the carrier; an antenna, which is arranged on the carrier, or the antenna and the integrated circuit are integrated into an integrated device and arranged on the carrier; wherein the integrated circuit is connected to the antenna for transmitting radio frequency transmission signals and / or receiving radio frequency reception signals.

[0008] According to some embodiments of the present application, the sixth aspect of the embodiments of the present application also provides a terminal device, including: a device body; and an electromagnetic wave sensor as described in the fifth aspect of the embodiments of the present application, 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.

[0009] The technical solution provided in the embodiment of the present application has at least the following advantages:

[0010] At least two transceiver channels are subjected to constant false alarm detection separately, so that the results of the at least two transceiver channels do not interfere with each other and do not affect other channels. Therefore, even if there are abnormal channels or abnormal data between the at least two transceiver channels, the processing of other normal channels will not be affected. Subsequently, processing is performed based on the candidate target data of the at least two transceiver channels to obtain the final target data, which supports the detection of the two transceiver channels, ensures the normal processing of the channel to which the abnormal channel or the abnormal data belongs, and avoids the interference of the abnormal channel or the channel to which the abnormal data belongs to other channels, reduces the problem of imbalance between channels, and the problem of different delays and phases between channels due to inconsistent lengths of the transceiver to the antenna and inaccurate compensation, thereby improving the accuracy of detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] One or more embodiments are exemplarily described by pictures in the corresponding drawings, and these exemplified descriptions do not constitute limitations on the embodiments. Elements with the same reference numerals in the drawings represent similar elements, and unless otherwise stated, the figures in the drawings do not constitute proportional limitations.

[0012] Figure 1 A flow chart of a target detection method provided in an embodiment of the present application;

[0013] Figure 2 Another flow chart of the target detection method provided in the embodiment of the present application;

[0014] Figure 3 Another flow chart of the target detection method provided in the embodiment of the present application;

[0015] Figure 4 This is a flow chart of implementing multi-frame joint target detection based on SISO-combine in an embodiment of the present application;

[0016] Figure 5 A schematic diagram of a target detection process after combining the target detection method provided in an embodiment of the present application with a multi-frame joint processing solution;

[0017] Figure 6 Another flow chart of the target detection method provided in the embodiment of the present application;

[0018] Figure 7 A schematic diagram of the working cycle of the radar provided in an embodiment of the present application;

[0019] Figure 8 A waveform diagram of a detection signal of an FMCW radar provided in an embodiment of the present application;

[0020] Fig. 9 A schematic diagram of data reading involved in the target detection method provided in an embodiment of the present application;

[0021] Fig.10 Another flow chart of the target detection method provided in the embodiment of the present application;

[0022] Fig.11 A schematic diagram of data movement involved in the target detection method provided in an embodiment of the present application;

[0023] Fig.12 Another flow chart of the target detection method provided in the embodiment of the present application;

[0024] Fig.13 A schematic diagram of the division of a preset area involved in the target detection method provided in an embodiment of the present application;

[0025] Fig.14 A schematic diagram of the distribution of target points in a preset area involved in the target detection method provided in an embodiment of the present application;

[0026] Fig.15 This is an output schematic diagram of the target detection method provided in the embodiment of the present application;

[0027] Fig.16 It is a flowchart of the target detection method in the embodiment of the present application when combined with DAE CFAR;

[0028] Fig.17 Based on Fig.16 A schematic diagram of azimuth or elevation CFAR in the target detection process shown;

[0029] Fig.18 This is a schematic diagram of a process for post-processing a target in an embodiment of the present application;

[0030] Fig.19 This is a schematic diagram of the process of post-processing a target in combination with a DL algorithm in an embodiment of the present application. DETAILED DESCRIPTION

[0031] After analysis, it was found that when the current radar performs digital signal processing, it usually accumulates all channels first to achieve non-correlated accumulation, and then performs CFAR detection based on the results of non-correlated accumulation. This will cause the results of CFAR processing to refer to the specific values ​​of all channels. Once a channel or a certain data has a problem, other normal channels and normal data will be interfered by abnormal channels and abnormal data through non-correlated accumulation, which will eventually lead to erroneous CFAR processing results and inaccurate target detection.

[0032] However, if the adverse effects of abnormal channels (or data) in non-correlated accumulation are resolved, the missed detection rate and false alarm rate are expected to continue to decrease.

[0033] Based on this, the embodiments of the present application provide a target detection method, integrated circuit, sensor and equipment, which perform constant false alarm detection through a single transceiver channel alone, avoiding interference between abnormal transceiver channels or abnormal transceiver channel data, so that the performance loss caused by channel imbalance can be effectively reduced, and it has better robustness in practical applications, that is, the candidate target data further obtained can accurately reflect the target distribution of the corresponding channel, and the candidate target data of the corresponding transceiver channel is further processed to obtain the final target data, which will be able to more accurately reflect the target distribution and is not easily interfered by abnormal channels or abnormal data, thereby improving the accuracy of the detection results.

[0034] In order to make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the embodiments of the present application will be described in detail below in conjunction with the accompanying drawings. However, it will be appreciated by those skilled in the art that in the embodiments of the present application, many technical details are provided in order to enable the reader to better understand the present application. However, even without these technical details and various changes and modifications based on the following embodiments, the technical scheme claimed in the present application can be implemented.

[0035] The following division of each embodiment is for the convenience of description and should not constitute any limitation on the specific implementation of the present application. Each embodiment can be combined with each other and referenced to each other under the premise of no contradiction. In addition, unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by technicians in the technical field of the present application. The terms used in the specification of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application.

[0036] In some embodiments, Figure 1 As shown, the target detection method includes the following steps:

[0037] Step 101 : Based on the range-Doppler spectrum, CFAR processing is performed on at least two transceiver channels separately to obtain candidate target data of the at least two transceiver channels.

[0038] Step 102, processing is performed based on candidate target data of at least two transceiver channels to obtain final target data.

[0039] In this way, at least two transceiver channels are individually subjected to constant false alarm detection, so that the results of the at least two transceiver channels do not interfere with each other, and do not affect other channels. Therefore, even if there is an abnormal channel or abnormal data between the at least two transceiver channels, it will not affect the processing of other normal channels. In addition, subsequent processing is performed based on the candidate target data of the at least two transceiver channels to obtain the final target data, which supports the detection of the two transceiver channels, ensures the normal processing of the channel to which the abnormal channel or the abnormal data belongs, and avoids the interference of the abnormal channel or the channel to which the abnormal data belongs to other channels, reduces the problem of imbalance between channels, and the problem of different delays and phases between channels due to inconsistent lengths of the transceiver to the antenna and inaccurate compensation, thereby improving the accuracy of detection.

[0040] To help understand the target detection method provided in the above embodiment, its steps will be explained and illustrated below.

[0041] In step 101, the number of transceiver channels that perform CFAR processing independently is not limited, for example, it may be 2, 3, 5, or all transceiver channels.

[0042] For example, in some embodiments, all the transceiver channels may be individually subjected to CFAR processing, thus completely avoiding interference between the channels and improving accuracy. Figure 2 As shown, based on the range-Doppler spectrum, constant false alarm processing is performed on at least two transceiver channels, which is achieved in the following manner: step 201, based on the range-Doppler spectrum, constant false alarm processing is performed on each transceiver channel. Correspondingly, processing is performed based on candidate target data of at least two transceiver channels, which is achieved in the following manner: step 202, processing the candidate target data of each transceiver channel.

[0043] Of course, the above is only an example. In some embodiments, CFAR detection may be performed on some transceiver channels separately to avoid the adverse effects of channels with relatively poor signal-to-noise ratio or severe TRX leakage, which will not be described in detail here.

[0044] In step 101, the constant false alarm processing method of a single transceiver channel is not limited. For example, in some embodiments, it can be implemented based on the existing constant false alarm processing, but the original data accumulation of multiple transceiver channels is changed to data accumulation of a single transceiver channel.

[0045] In some embodiments, Figure 3 As shown, based on the range-Doppler spectrum, constant false alarm processing is performed on at least two transmitting and receiving channels, which can be achieved by the following steps:

[0046] Step 1011 , based on the range-Doppler spectrum, the noise floor of the receiving and transmitting channel that is subjected to the CFAR processing is estimated separately to obtain the noise floor estimation result of the single receiving and transmitting channel.

[0047] Step 1012: Based on the noise floor estimation result of a single transceiver channel, a constant false alarm detection is performed on the corresponding transceiver channel separately to obtain candidate target data corresponding to each transceiver channel.

[0048] That is, when performing constant false alarm processing on a single transceiver channel, the single transceiver channel is regarded as an independent whole for noise floor estimation, so as to implement CFAR based on noise floor estimation based on the noise floors estimated by each channel.

[0049] In step 1011, the noise floor estimation method is not limited. For example, in some embodiments, the noise floor estimation is performed separately for the transceiver channel that is subjected to the constant false alarm processing separately according to the range-Doppler spectrum, which can be achieved in the following manner: according to the range-Doppler spectrum, the noise floor estimation is performed separately for the transceiver channel that is subjected to the constant false alarm processing separately within a preset range, and the noise floor estimation result of the single transceiver channel is obtained; wherein the upper limit of the preset range is determined according to the noise floor estimation result of the farthest distance unit. In other words, by setting the preset range to constrain the noise floor estimation, the upper limit is constructed according to the noise floor estimation result of the farthest distance unit, which is conducive to improving the accuracy of noise estimation.

[0050] In some examples, the noise floor is estimated for each transmit and receive channel separately, using the following expression:

[0051] n i '=min(n i ,α*n g );

[0052] Among them, n i ' is the noise floor estimation result of the i-th distance unit of a single transceiver channel within the preset range, n i is the initial noise floor estimation result of the i-th range unit on a single transceiver channel determined according to the range-Doppler spectrum, α is a preset parameter, α≥1, n g It is the initial noise floor estimation result of the farthest distance unit on a single transmit and receive channel.

[0053] It should be noted that the preset parameter α can be set and updated based on demand or engineering data, experience, etc.

[0054] It should also be noted that the above is only an exemplary description of the noise floor estimation. In some embodiments, the initial noise floor estimation result of the i-th distance unit on a single transceiver channel can be directly selected as the noise floor result used when the transceiver channel performs CFAR processing. The minimum value, maximum value, quantile, average, median, etc. of the initial noise floor estimation results of the last few regions of interest on a single transceiver channel can also be selected as the noise floor estimation. Alternatively, when estimating the noise floor, only some Doppler units of a certain distance unit are selected instead of all Doppler units, etc. The noise floor estimation methods are not listed one by one here.

[0055] In step 1012, constant false alarm detection can be implemented by threshold detection. For example, the noise floor estimation result of a single transceiver channel is composed of the noise floor estimation results of each distance unit on the single transceiver channel, and the constant false alarm detection result corresponding to the single transceiver channel is composed of the constant false alarm detection results of each Doppler unit of each distance unit on the single transceiver channel.

[0056] According to the noise floor estimation result of a single transceiver channel, constant false alarm detection is performed on the corresponding transceiver channel separately, which is achieved through the following expression:

[0057]

[0058] Wherein, F(c,r,v) is the constant false alarm detection result of the vth Doppler unit of the rth range unit of the cth channel, P(c,r,v) is the echo energy on the vth Doppler unit of the rth range unit of the cth channel, n′(c,r) is the noise floor estimation result of the rth range unit of the cth channel, β is a preset parameter, β≥1.

[0059] From the above expression, we can see that when F(c,r,v) is 1, it indicates that there is a target on the vth Doppler unit of the rth distance unit, and when F(c,r,v) is 0, it indicates that there is no target on the vth Doppler unit of the rth distance unit. In other words, the detection results of a single transceiver channel are binarized so that the detection process is no longer disturbed by specific values ​​(especially extreme data), thereby improving the accuracy of detection.

[0060] It should be noted that the above expression is only provided for explanation by taking binarization of the result and obtaining candidate target data as an example. In some embodiments, the result can be quantized by quantization methods other than binarization, which can also reduce the interference of extreme data to a certain extent, instead of having to adopt binarization. They will not be listed one by one here.

[0061] It should also be noted that the above is only an example of constant false alarm processing for a single transceiver channel provided in combination with CFAR based on noise floor estimation, but it does not mean that the constant false alarm processing for a single transceiver channel in the embodiment of the present application can only be implemented as above. For example, it can be implemented in combination with CA (Cell Averaging)-CFAR, OS-CFAR, GO-CFAR, etc. When implementing, the processing of multiple transceiver channels in the original algorithm can be regarded as a single transceiver channel, which will not be described one by one here.

[0062] In step 102, processing the candidate target data of at least two transceiver channels can actually be regarded as further performing target verification based on the candidate target data of the transceiver channel on the basis of performing target verification based on the data of a single transceiver channel in step 101, thereby improving the accuracy and reliability of the target. Therefore, step 102 can be implemented with the idea of ​​constant false alarm detection.

[0063] For example, in some embodiments, Figure 3 As shown, the candidate target data of each transceiver channel is processed to obtain the final target data, which can be achieved through the following steps:

[0064] Step 1021 , performing constant false alarm detection according to the currently acquired candidate target data and a preset threshold to obtain final target data.

[0065] That is to say, according to the existing constant false alarm processing, which is to judge the threshold value through the results of non-correlated accumulation, the candidate target data is processed by similar threshold value to obtain the final detection result. It is difficult to implement, efficient, and can improve the accuracy of detection, real-time performance, and improve user experience.

[0066] At this time, constant false alarm detection is performed based on the currently acquired candidate target data and the preset threshold, which is achieved through the following expression:

[0067]

[0068]

[0069] Where F(c,r,v) is the constant false alarm detection result of the vth Doppler unit of the rth range unit of the cth channel, N c is the total number of channels, F 0 is the preset threshold, and F′(r,v) is the constant false alarm detection result of the vth Doppler unit of the rth range unit.

[0070] That is to say, the candidate target data of the transmitting and receiving channels are accumulated, and then it is judged whether the accumulated result exceeds the preset threshold to determine the final target data, so that the final target data can be used to characterize whether there is a target on the vth Doppler unit of the rth distance unit, thereby avoiding mismonitoring and missed detection due to multipath, etc., and improving the accuracy and reliability of target detection.

[0071] Of course, the above is only an exemplary explanation of obtaining the final target data by means of a threshold value. In some embodiments, if the constant false alarm detection result of the vth Doppler unit of the rth distance unit of the cth channel is represented by a quantized numerical value, then the final target data can be obtained by determining how many channels the constant false alarm detection result of the vth Doppler unit of the rth distance unit exceeds a preset value; or, the above accumulation process is replaced by weighted summation or averaging, etc., which will not be described one by one here.

[0072] It should be noted that the above embodiment only limits the target verification in the target detection process (i.e., obtaining a result similar to the existing CFAR processing), but does not limit other data processing processes in the target detection process. It can be understood that Figure 1-Figure 3 The illustrated embodiment may be further implemented in combination with other data processing implementation schemes, for example, in combination with a multi-frame joint processing scheme.

[0073] To facilitate understanding of the above target detection scheme and the multi-frame joint processing scheme, the multi-frame joint processing scheme is first described below.

[0074] like Figure 4 As shown in FIG. 1 , the radar (taking the frequency modulated continuous wave (FMCW) radar as an example) receives the echo signal through the antenna and obtains the digital signal after analog-to-digital conversion and sampling. The digital signal will go through the following steps: Figure 4 The main control module (for example, it can be implemented based on a microcontroller unit (MCU)) and a baseband (BB) module (for example, it can be implemented based on a baseband chip) shown in the figure can process and realize target detection. Figure 4 In the process, the digital signal will be subjected to range DC removal and range Fourier transform (Range FFT, which can also be expressed as 1D-FFT or fast time FFT) in the baseband module in sequence. After that, the signal processed by the baseband module will be sent to the main control module and accumulated in multiple frames. When the data is accumulated to a preset number of frames, such as 128 frames, the main control module will send the accumulated data of multiple frames from the CPU static random access memory (SRAM) to the baseband module, and continue to perform Doppler DC removal, frame Fourier transform (Frame FFT), channel accumulation (such as non-coherent integration, correlation accumulation, etc.) based on the baseband module. Then, in the baseband module, constant false alarm detection (such as CFAR detection with peak based) is performed based on the noise obtained by noise estimation (such as using a noise estimator based on variance) selection), and finally perform angle detection and target classification to obtain the distance, speed and / or angle information of the target. When performing angle detection, azimuth estimation and elevation estimation can be performed based on the CFAR result, which can be achieved through technologies such as digital beam forming (DBF) and direction of arrival estimation.

[0075] The following will Figure 4 The specific implementation of the digital signal processing shown is described.

[0076] The distance-dimensional DC removal is aimed at the data corresponding to each chirp in each frame of data. At this time, the distance-dimensional DC removal is specifically as follows: the data collected by each receiving (RX) channel of each chirp is averaged along the fast time dimension, and then the DC component of all sampling points of each RX channel is subtracted.

[0077] The distance dimension FFT can be realized by windowing the data after the distance dimension DC is removed and then performing 1D-FFT.

[0078] It should be emphasized that in the process of target detection, each chirp data in each frame of data obtained by radar through ADC or sampling based on the echo signal is sent to the main control module through the above operation. The data is sent to the main control module by moving the data from Direct Memory Access (DMA) to CPU SRAM.

[0079] Doppler dimension DC removal can be achieved by calculating the complex average along the frame dimension of multiple frame data accumulated on the main control module (i.e. averaging the data of different frames in the same distance unit (bin) of the same channel), obtaining the average values ​​of different distances in different channels as the DC components of the corresponding distance units of the corresponding channels, and subtracting the respective DC components from each distance unit of each channel.

[0080] The inter-frame Fourier transform can be implemented by windowing the data after the Doppler dimension DC is removed along the frame dimension and performing 2D-FFT along the frame dimension, at which time the range dimension-Doppler information of each transmitting and receiving channel will be obtained.

[0081] Incoherent accumulation can be achieved by the following expression:

[0082]

[0083] or,

[0084]

[0085] Wherein, P(r,v) and A(r,v) are the power and amplitude at the r-th range unit and the v-th Doppler unit, respectively, and s(r,t,a,v) is the complex value obtained by inter-frame Fourier transform of the signal echo of the r-th range unit and the v-th Doppler unit on the t-th transmission channel and the a-th receiving channel.

[0086] The constant false alarm detection based on the noise floor obtained by noise estimation has been described in the existing CFAR technology, and will not be described in detail here.

[0087] Angle detection can be achieved by performing azimuth digital beam forming (DBF) and DOA on the target point obtained by CFAR, as well as elevation DBF and DOA. DBF and DOA have been described in the existing DBF and DOA technologies, and will not be described here one by one.

[0088] Target classification can be achieved by counting the target points detected in each frame (or a preset amount of data, i.e. each time) 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.

[0089] It should be noted that for radars with baseband modules and main control modules, Figure 4 The above is only an example, that is, frame data accumulation, target classification (which can be implemented by Region HIST, classification, etc.) and other operations are performed in the main control module, and other operations are performed in the baseband module, such as range dimension DC removal and Doppler dimension DC removal, etc. In other embodiments, frame data accumulation can also be performed in the baseband module, and Doppler dimension DC removal can be performed in the main control module.

[0090] It should also be noted that Figure 4 Only caching 128 frames of data is used as an example for explanation. In other embodiments, 56 frames of data or 32 frames can also be cached. The embodiments of the present application are not limited to this and can be determined according to demand or hardware support capabilities. In this way, the cached multi-frame data will continue to perform Doppler dimension DC removal and inter-frame FFT to obtain a range-Doppler (Range-Doppler, RD) spectrum.

[0091] It should also be noted that Figure 4 In the process shown, some steps can be skipped, such as distance-dimensional DC removal or distance-dimensional DC removal or windowing; some steps can be simplified, such as non-coherent accumulation can be skipped, and only one of the channels is subjected to CFAR detection, or only some channels are selected for non-coherent accumulation, and only these channels are subjected to CFAR detection.

[0092] Therefore, it can be seen from the above that, combined with the above-mentioned multi-frame joint processing scheme, that is, the multi-frame joint technology is used when obtaining the RD spectrum, or in other words, the non-correlated accumulation and CFAR processing in the above-mentioned multi-frame joint technology scheme are replaced by Figure 1-Figure 3 The target detection method provided in the embodiment shown is a method provided in the above embodiment combined with Figure 4 In the process shown, the target detection process will be as follows Figure 5At this time, after CFAR processing is performed on all channels one by one, binary integration in channel-domain processing is continued, and then the horizontal / azimuth and elevation angles are estimated. Specifically, for a system with M channels, each channel is processed as follows Figure 4 After the inter-frame FFT shown in , CFAR processing is performed on each channel to obtain M bit masks, each element of which can be of bool type, and each range unit (range bin) and / or Doppler bin is judged to determine whether there is a target (if it is 1, it is defined as existing, and if it is 0, it is defined as not existing). At the same time, these M bit masks can be accumulated, and the range of each element can be defined as 0 to (M-1). Subsequently, the accumulated mask can be subjected to secondary detection, that is, each element of the accumulated mask can be compared with a preset threshold value M0. If the value xi of the unit to be detected (such as an element of the accumulated mask) is greater than M0, it can be output that there is a target in the unit to be detected (that is, the final target data indicates that there is a target). If xi≤M0, it can be output that there is no target in the unit to be detected (that is, the final target data indicates that there is no target). Among them, xi, M, and M0 are positive integers. Among them, in Figure 5 In the detection method flow shown, since each channel is subjected to CFAR processing separately, the performance loss caused by channel imbalance can be effectively reduced, and the method has better robustness in practical applications.

[0093] To facilitate better understanding of those skilled in the art Figure 5 The process shown in the figure will be explained below through the processes of different target detection methods.

[0094] In some embodiments, the process of the target detection method is as follows Figure 6 As shown, the following steps are also included:

[0095] Step 103: Perform distance dimension FFT on the frame data.

[0096] Step 104: extract target data from the frame data obtained after the range-dimensional FFT processing.

[0097] Step 105, sliding windowing is performed on the target data, and 2D FFT is performed on the windowed data to obtain a range-Doppler spectrum.

[0098] The length of the sliding window corresponds to a duration of the same order of magnitude as the period of the periodic motion of the target.

[0099] To facilitate better understanding of those skilled in the art Figure 6The steps of the target detection method described in the illustrated embodiment will be explained below.

[0100] In step 103, the frame data refers to the data corresponding to the echo signal formed after a frame of detection signal is reflected by the target. It can be understood that the target detection behavior performed by the radar is usually as follows: Figure 7 As shown in FIG. 1 , the radar first sends a detection signal in a detection cycle. Taking the detection of frequency modulated continuous wave (FMCW) radar as an example, Figure 8 As shown, a frame of detection signal will be composed of multiple chirp signals, and at the same time as the detection signal starts to be transmitted, the radar is ready to start receiving the echo signal reflected by the target, and the echo signal corresponding to a frame of detection signal will be processed by the aforementioned analog-to-digital conversion, distance dimension DC removal, etc. to obtain the above frame data. Further, each frame data is subjected to distance dimension FFT as described above to implement step 103, which will not be repeated here.

[0101] In step 104, the target data is not limited in the embodiment of the present application, and it can be any data content belonging to the corresponding frame data. For example, when the storage resources are sufficient and the computing power is sufficient, the target data can be extracted by extracting all the data; or when the real-time performance of the target detection is expected to be the highest, partial data can be extracted, etc. The target data can be extracted according to the requirements and hardware conditions, etc. For ease of understanding, the following describes different implementations of extracting partial data as target data.

[0102] In some embodiments, extracting target data from frame data obtained after distance-dimensional FFT processing is achieved by the following steps: extracting data corresponding to a chirp cycle from each frame data obtained after distance-dimensional FFT processing as target data.

[0103] In other words, using the data corresponding to a chirp cycle as the representation of the frame data makes the processing of the target data concise and easy to implement, and the selected data is more real, which can perform target detection more efficiently and is conducive to improving the real-time performance of target detection.

[0104] In some embodiments, extracting target data from frame data obtained after distance-dimensional FFT processing can also be achieved through the following steps: based on each frame data obtained after distance-dimensional FFT processing, determining the parameters corresponding to each distance unit in each frame data to generate target data.

[0105] That is to say, multiple data of each distance unit in the frame data are compressed into corresponding parameters so that the overall characteristics of the frame data can be retained, so that the information that can be referred to when performing target detection based on the target data later is more comprehensive and the result obtained is more accurate.

[0106] Of course, the above embodiments are only provided to reduce the resources occupied by the target data. In some embodiments, the target data can also be extracted by considering the validity of the data. Specifically, the radar signal can usually cover a large range, but not all the ranges covered by the radar are areas that need to be paid attention to. Processing the data corresponding to these areas will cause a waste of resources. Therefore, in some embodiments, extracting the target data from the frame data obtained after the distance dimension FFT processing can also be achieved by the following steps: extracting the data within the preset distance range from the frame data obtained after the distance dimension FFT processing as the target data.

[0107] That is to say, in the frame data, the range of interest (represented by a preset distance range) is used as the target data. In this way, accurate target detection information can be retained and the storage resources and computing resources occupied by the target data can be reduced, thereby balancing the accuracy and real-time performance of target detection.

[0108] It should be noted that, in addition to the above-described method, the retention of data within the range of interest can also be achieved in some embodiments by windowing the target data according to a sliding window, and performing digital signal processing on the data within a preset Doppler range in the windowed data. Windowing the target data according to a sliding window and performing digital signal processing on the windowed data can also achieve the effect of retaining the data in the region of interest, but the dimensions of the two data are different, one is the distance dimension and the other is the Doppler dimension. Therefore, the selection method of the range of interest is different. Of course, it is also possible to extract target data in the distance dimension and extract data in the Doppler dimension for digital signal processing, etc., which will not be repeated here.

[0109] Of course, the above is only an example of the implementation method of step 104. In some embodiments, it can also be implemented by other methods. For example, the distance unit with a larger modulus between the distance units (similar to max-pooling) can be used as the target data, etc., which will not be described in detail here.

[0110] In step 105, the sliding windowing method is not limited. For example, the sliding window may be a window of fixed length; or the sliding window may be a window of variable length, etc., which will not be listed one by one here. For ease of understanding, a sliding window of fixed length may be used as an example for explanation. However, it does not mean that only a sliding window of fixed length can be used. For example, a window of variable length may be changed according to the periodic movement of the target, such as changes in breathing frequency.

[0111] In some embodiments, Fig. 9As shown, for the 1st to 129th frame data generated after the radar is started, taking the window length of the sliding window as 128 frames as an example, the data obtained by the sliding window for the first time is the 1st to 128th frame data ( Fig. 9 The data obtained the second time are the 2nd to 129th frame data ( Fig. 9 The data in the shaded part of the second row in ), that is, the sliding step of the sliding window is 1 frame.

[0112] Of course, the above are only examples. In other embodiments, the window length and sliding step size of the sliding window may also adopt other parameters, which will not be listed here one by one.

[0113] In step 105, when windowing the data, the data needs to have a certain order. If the order is disrupted, the temporal association of the data is destroyed and it can no longer reflect the movement accumulated for a certain period of time, which is not conducive to subsequent target detection. Figure 5 As shown, data is accumulated in the main control module. When a certain amount of data is accumulated, the baseband module reads the data and continues to process it. In this process, the data writing order and reading order may be different due to the storage method. Therefore, in some embodiments, the data needs to be rearranged to ensure its correct order. For example, in some embodiments, assuming that the data is cached using Ring FIFO, at this time, Fig.10 As shown, the target detection method also includes the following steps:

[0114] Step 106, reading data from the Ring FIFO and reordering the read data.

[0115] Thus, the efficiency of subsequent processing can be improved by rearranging data.

[0116] In order to facilitate those skilled in the art to better understand the above data storage and rearrangement, the following will be combined with Fig.11 Provide explanation.

[0117] like Fig.11 As shown, assuming that the baseband module (shown as BB in the figure) performs distance dimension FFT on the frame data and obtains the target data in the range of interest (such as from the 5th distance unit to the 20th distance unit), the main control module (shown as MCU in the figure) will need to accumulate the following Fig.11 The two-dimensional matrix shown in FIG. 1 , the rows of the matrix correspond to the frame numbers, and the columns of the matrix correspond to the distance units.

[0118] Assuming that Ring FIFO is used in the main control module to store the above two-dimensional matrix, the storage process can be as follows:

[0119] Initialize a CPU SRAM with Nf Columns, each column can store N r ×N rx ×N tx Number, where N f To design the maximum number of frames for inter-frame FFT, N r 、N rx and N tx The number of range units, the number of transmit (Tx) channels, and the number of receive (Rx) channels of interest are respectively. The buffer can be initialized to all 0s during initialization, and the FIFO head pointer p_head points to the beginning of the 0th column of the Ring-FIFO.

[0120] In the 0th frame, the corresponding data (of each distance unit, each Tx channel and Rx channel) is cached in the 0th column of the Ring-FIFO, and then p_head points to the 1st column.

[0121] Similarly, for the k-th frame data (k=1, 2, ..., 126), the corresponding data is cached in the k-th column of the Ring-FIFO, and then p_head points to the k+1-th column.

[0122] At the 127th frame, the corresponding data is cached in the 127th column of the Ring-FIFO; since 128 frames of data are already stored at this time, the data in the Ring-FIFO needs to be moved to the baseband module to perform the aforementioned inter-frame FFT processing. At this time, the data from the column pointed to by the p_head to the 127th column is first moved, and then the data from the 0th column to the column pointed to by the p_head is moved. Obviously, the order of reading data at this time is different from the order of writing, and it needs to be restored, that is, the data needs to be rearranged so that it is continuously distributed according to the frame dimension, thereby improving the computing efficiency on the baseband module.

[0123] After the move is completed, the pointer p_head of the Ring-FIFO points to column 0 again. Similarly, during the new data storage and move process, the data buffer of the 128th frame is stored in column 0 of the Ring-FIFO. During the move, the data of frames 1 to 127 will be moved to the baseband module first, and then the data of column 0 will be moved to the baseband module. The same goes for subsequent frames, which will not be described here.

[0124] It should be noted that in the above example, inter-frame processing is performed after all valid data is stored in the Ring-FIFO. In other embodiments, after storing part of the frame data, such as the 0th to 63rd frame data, the data in the Ring-FIFO can be moved to the baseband module in a similar manner to perform inter-frame FFT, etc., wherein, since the 64th to 127th columns are all initial values ​​assigned during initialization, it can be equivalent to the inter-frame FFT of the baseband module that fills the data sequence with zeros. Of course, the above is only an example, and does not mean that 128 frame data must be used for inter-frame FFT, or that Ring-FIFO storage must be used, etc., which will not be repeated here.

[0125] In step 105, it can also be understood that, during the detection process, multi-frame joint detection may need to be observed in combination with different periodic motions, so that the target detection result can be obtained more accurately. Based on this, sliding windowing is performed on the target data, and 2D FFT is performed on the windowed data, which can be achieved in the following manner: sliding windowing is performed on the target data according to the period of different vital sign parameters of the living body, and digital signal processing is performed on the windowed data corresponding to sliding windows of different lengths.

[0126] Among them, the periods of the vital sign parameters corresponding to different sliding windows are different, and the length of each sliding window has the same order of magnitude as the period of the vital sign parameters. At this point, it can be understood that when detecting based on a variety of different periodic motions, after the accumulation of data with a shorter period is completed, the data continues to accumulate until the accumulation of data with a longer period is completed, and the processing of the data is completed. For example, assuming that one periodic motion corresponds to 32 frames and one periodic motion corresponds to 128 frames, both of which need to be observed, then in the process of target detection, a detection result is output from the completion of the acquisition of the target data corresponding to the 32nd frame data, and a new detection result is output after the target data corresponding to the 33rd frame data is acquired... until the acquisition of the target data corresponding to the 128th frame data is completed, two detection results will be output (one is the detection result corresponding to the 32 frame data, and the other is the detection result corresponding to the 128 frame data), or the result after the comprehensive processing of these two results.

[0127] In this way, by obtaining the period of the vital sign parameters of the living body, the length of the sliding window can be adjusted in time, so as to better detect the living target and better monitor the status of the living body (such as children, pets, etc.) in order to monitor and take corresponding measures to the performance of the living body in time.

[0128] It should be noted that the embodiments of the present application do not limit the vital sign parameters. For example, the vital sign parameters may include breathing. Another example is that the vital sign parameters may include heartbeat and / or pulse. Of course, the above are only examples. Under other requirements, other vital sign parameters that can be reflected as motion characteristics can also be used, which will not be listed here one by one.

[0129] In addition, it can also be understood that if the user hopes to obtain more intuitive and accurate results, at this time, further processing can be performed after obtaining the target data, for example, target classification, further verification, etc.

[0130] Based on this, in some embodiments, such as Fig.12 As shown, after obtaining the target, the following steps are also performed:

[0131] Step 107 , verifying the targets in each preset area according to the targets falling in the preset areas, where the preset areas are obtained by dividing the detection space.

[0132] Step 108: output the detection results of each preset area according to the verified targets.

[0133] That is to say, taking into account that for a target with a certain volume and occupying a certain space, multiple destination points are usually detected, and the currently detected target is used to continue to be verified in combination with the area division to improve the detection result. Based on the division of the detection space in the application scenario, the detection results of each preset area are output, so that the target will be presented in the preset area. The distribution is more intuitive and accurate, which is more conducive to users making decisions based on the output results and providing a better user experience.

[0134] To facilitate better understanding of those skilled in the art Fig.12 The embodiment shown, the steps of which will be explained below.

[0135] In step 107, the detection space and the preset area are not limited, and they may be different according to different application scenarios, application requirements, etc. For example, in the application scenario of the vehicle-mounted radar, the detection space may be inside the car, and the preset area at this time includes at least any one of the following items, including the area corresponding to the seat and the area corresponding to the foot pad, so that the driver or adults and other objects can better perceive the situation inside the car. For example, in a factory work scenario, the detection space may be a factory building, and the preset area may include the workstations of each employee to avoid production risks caused by employees not being at their workstations and unable to monitor the working conditions of the machines. I will not go into details here. In the following, for ease of understanding, the above-mentioned in-car space will be used as an example for explanation, but this does not mean that the corresponding solution can only be implemented in the car.

[0136] Taking the interior of a car as an example, the interior space is abstracted as follows: Fig.13 The coordinate system shown in FIG. 1 is a coordinate system in which the horizontal axis of the coordinate system represents the azimuth angle relative to the radar installed in the car, and the horizontal axis of the coordinate system represents the elevation angle relative to the radar installed in the car. At this time, the car is divided into six preset areas, namely, three rear seats and three aisles in front of three seats. Fig.13 Different filling areas are shown. According to different requirements, such as Fig.13 As shown, different preset areas may overlap or not overlap with each other; certain areas in the vehicle may belong to multiple preset areas at the same time or may not belong to any area.

[0137] It should be noted that Fig.13 This is only an abstract way of detecting space. In some embodiments, the detection area can be abstracted from all or one of the dimensions of distance, azimuth, and pitch, or all or one of the dimensions of xyz along the rectangular coordinate system, and further divided into preset areas, which will not be described in detail here.

[0138] In step 108, the verification method is not limited, for example, the verification may be performed based on the number or proportion of target points.

[0139] For example, in some embodiments, based on the targets falling within each preset area, verifying the targets in each preset area can be achieved through the following steps: verifying the targets in each preset area based on the proportion of the targets falling within each preset area in the detected targets.

[0140] For another example, in some embodiments, verifying the targets in each preset area according to the targets falling in each preset area can be achieved by the following steps: verifying the targets in each preset area according to the signal-to-noise ratio of the targets falling in each preset area.

[0141] Of course, the above are only examples. In some cases, targets at other locations can also be verified based on the target point falling into locations such as the driver's seat where targets are known to exist. I will not go into details here.

[0142] In step 108, the output method is not limited. The detection results of each preset area can be directly output as a point cloud, or the divided preset areas and the point clouds of each preset area can be simultaneously presented and output, etc., which will not be listed here one by one.

[0143] In order to facilitate those skilled in the art to better understand the above embodiment, the following will be described by taking proportion verification as an example.

[0144] Assume that 11 target points are detected in a target detection process, and the preset area is divided as follows: Fig.13 As shown, the distribution of target points in the preset area is shown as Fig.14 As shown, the area seats A (6 target points), seat B (2 target points), seat C (0 target points), aisle A (0 target points), aisle B (1 target point) and aisle C (0 target points), where, Fig.14 The mid-target point is represented by a solid circle.

[0145] At this time, the algorithm obtained according to the above embodiment will be as follows:

[0146] Initialize the flags flag_region_i of the six regions to be 0, i.e., no one exists (i=0, 1, ..., 5);

[0147] If the total valid target number total_valid_tgt_num is 0, it is determined that there is no one in the car and the process ends, otherwise the following operations are performed;

[0148] Traverse all regions. For region i, if the statistical value of the number of valid targets belonging to the region tgt_num_region_i exceeds 25% of the total number of valid targets total_valid_tgt_num, set the flag flag_region_i of region i to 1;

[0149] 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;

[0150] Traverse the seat area, if there is any seat area where flag_region_i is 1, then determine that the corresponding aisle area is empty (for example, if it is determined that there is someone in seat A, then determine that there is no one in aisle A);

[0151] Output the result.

[0152] At this time, the output will be as follows Fig.15 As shown, only the seat area A displays 6 target points, and the rest of the preset areas are empty.

[0153] It should be noted that the threshold value of 25% in the above example is only an example. In other embodiments, other threshold values ​​or other judgment conditions may be used, which will not be described in detail here.

[0154] In this way, on the basis of performing constant false alarm processing through a single transceiver channel to avoid interference from abnormal channels or abnormal data, the frame-level FFT is used to replace the traditional chirp-level Doppler FFT to achieve effective accumulation of the target's motion energy to achieve the purpose of accurate detection and measurement, and the sliding window FFT processing can be performed on multiple frames to further improve the result update frequency; by performing post-processing methods such as regional statistics on the target points detected by the radar, it is possible to determine whether there are people in each area. Specifically, the frame-level FFT is used to replace the traditional chirp-level Doppler dimension FFT to determine the speed of the target 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. At the same time, 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 obtained by taking the minimum value based on the estimation of the current distance unit (bin) combined with the estimation of the last distance unit of interest 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.

[0155] Fig.16 FIG. 1 is a flow chart of target detection based on frame-level FFT combined with DAE CFAR in an embodiment of the present application. Fig.16 As shown, in Figure 5 Based on the process structure shown, based on the frame-dimensional FFT (frame-FFT), DAE CFAR (Constant False Alarm Rate) is used to estimate the azimuth and elevation angles for CFAR data. Specifically, after Doppler dimension CFAR detection is performed on the inter-frame FFT data, azimuth dimension DBF (Azimuth DBF) is performed, and the azimuth CFAR processing (Azimuth CFAR) is continued in combination with the NVE (noise variance estimator) noise floor estimation; similarly, the elevation DBF (Elevation DBF) is performed on the azimuth CFAR data, and the elevation CFAR processing (Elevation CFAR) is continued in combination with the NVE noise floor estimation, thereby obtaining the azimuth and elevation information of the target.

[0156] Fig.17 Based on Fig.16 The schematic diagram of the azimuth or elevation CFAR in the target detection process is shown in the figure. Fig.17As shown, the azimuth / elevation power data can be calculated by the NVE engine in a HIST manner, 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.

[0157] For example, when performing DBF, the amplitudes of all peak points in the DBF spectrum, including the global maximum (Global max) peak and the local maximum (Local max) peak, 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 the real target point.

[0158] Fig.18 Schematic diagram of the process of post-processing the target in the embodiment of the present application. Fig.18 As shown, it can be Figure 5 On the basis of the process structure shown, after the azimuth / elevation angle estimation, the output target point cloud data can be processed in combination with the machine learning model to accurately detect the target in the area of ​​interest. Specifically, for the single target point cloud data obtained after the azimuth / elevation angle estimation, the false alarm suppression technology of ML (Machine Learning, such as SVM or RF algorithm) can be combined to suppress non-ideal data such as false alarms generated by noise, stationary clutter or target multipath, that is, for the target point cloud data such as the pitch DBF&DoA output, continue to perform operations such as ML-based false-alarm suppression, clustering and / or ML-based target classification, where the clustering process can be agglomerative clustering or DBSCAN algorithms. At the same time, the point cloud data after clustering processing can also be input into a trained machine learning model (such as SVM or RF) to determine whether there is a person in the current processing. If there is, its location can be further determined, as well as the distinction and judgment of physical targets such as adults, children, and infants.

[0159] Fig.18In the embodiment shown, the influence of non-ideal factors such as noise, stationary clutter and target multipath can be effectively reduced, the difficulty of selecting regional parameters and the difficulty of complex regional judgment logic design can be effectively reduced, and point cloud information can be more effectively utilized, especially for applications in complex scenes, to obtain better target detection and distinction judgment performance.

[0160] Fig.19 This is a flow chart of post-processing the target in combination with the DL algorithm in the embodiment of the present application. Fig.19 As shown, it can be Figure 1 Based on the process shown, after extracting the data obtained from at least one of the operation steps such as multi-frame accumulation (store frame), inter-frame Fourier transform (FrameFFT), and binary joint analysis, a deep learning algorithm (Deep Learning, abbreviated as DL) is used to judge, locate and identify the target, such as identifying whether the physical target is an adult or a child (Adult / Child Classification).

[0161] Specifically, the subsequent target distinction and judgment can be achieved by using 1D-FFT raw data as DL input (Access 1D-FFT data as raw input of DL), using 2D-FFT raw data between frames as DL input (Access 2D-FFT data as raw input of DL), using non-coherent integrated 2DFFT data as DL input (Access non-coherentintegrated 2DFFT data as raw input of DL) and / or using SNR data as DL input (Access SNR data as raw input of DL). The raw data here can be complex data, modulus value of complex data, modulus square of complex data or other transformation forms, and can be values ​​in the linear domain, values ​​in the dB (log) domain or other calculation forms.

[0162] based on Fig.19 The process shown in the figure uses at least one of the above methods to extract multi-frame 1D-FFT data, multi-channel data after multi-frame FFT, and energy (power) or SNR data after multi-frame FFT and SISO-combine, and then inputs the extracted data into the constructed deep learning model (such as CNN or Transformer, etc.) to determine whether there is a target in the current processing, and to determine the current target type (such as determining whether the target is an adult or a child, etc.).

[0163] It should be noted that Fig.19 The process steps and related descriptions shown in Fig.18 The process steps shown and the related descriptions may be compatible with each other; for example, Fig.19 The Region HIST and subsequent modules in the process shown can be used Fig.18 In the process shown, the ML-based false alarm suppression and its subsequent modules are partially or completely replaced with each other.

[0164] The results of the above classification may include the following aspects: whether there is a target, the attributes of the target, the specific unit interval location (ie, regional location) of each target, etc.

[0165] For example, in an application scenario where the rear area in a car is the area of ​​interest or called the target area, and it is divided into three seat areas (interval units) and corresponding three aisle areas, it is determined whether there is a living target in the above-mentioned area of ​​interest. If there is a living target, it can be further determined whether the living target is an adult, a child, an infant or a pet, etc., and the specific location information such as which seat or aisle area the living target is in can be further determined.

[0166] In some optional embodiments, all FFT processing in the target detection method may be windowed FFT, and may also be implemented by using SVA combined with FFT.

[0167] In some optional embodiments, the multi-frame sliding window FFT may be replaced by other time-frequency transform processes, such as short-time Fourier transform, fractional Fourier transform, etc., in the target detection method, which can also achieve effective detection of the target of interest.

[0168] In some optional embodiments, the multi-frame sliding window FFT in the target detection method can also 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.

[0169] In some optional embodiments, the DBF in the target detection method can be replaced by Capon, MUSIC, ESPRINT and other algorithms and their variants. At the same time, the beam synthesis at the center of the area of ​​interest and / or the antenna arrangement can be optimized and integrated to further improve the system target detection performance.

[0170] It should be noted that the embodiments of the present application 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 the needs. At the same time, for a system with a baseband module and a main control module, when the system detects a target by transmitting electromagnetic waves and receiving corresponding echo signals, each step in each target detection method of the embodiment of the present application can be configured to run in the baseband module and / or the main control module according to actual needs and the data processing capacity and timeliness of the system operation. The relevant examples in the figure can be used as a reference for some of the options.

[0171] In some optional embodiments, the above target detection process is applicable to TDM-MIMO systems. For systems that do not use TDM-MIMO, different phase shifts can be preset during different Tx transmissions. After directly transmitting the waveform digitally (phased array) of the region of interest, the above-mentioned processing flow can be used to perform operations such as target detection and judgment.

[0172] In some other embodiments, for systems using a pulse system, a pulse compression system or an ultra-wideband system, the above process is directly used for target detection, and the performance of target detection can be further improved by using a transmit beam digital synthesis technology. Among them, for a system using a pulse compression system, 1D-FFT processing is not required, but a pulse compression step needs to be added.

[0173] It should be noted that the above-mentioned processing methods corresponding to systems of different standards can also be applied to systems of mixed standards. For example, for a system with 6 transmitting antennas (i.e., 6 Tx), 3 of the transmitting antennas (3 Tx) can be TDM standards, and the other 3 transmitting antennas can be other standards. In the subsequent processing, the processing method corresponding to the above-mentioned standards can be used.

[0174] After the inter-frame FFT (Frame FFT) processing, two-dimensional digital beamforming (2D-DBF) processing and DL-based target classification processing (DL-based Classification) operations are directly performed. That is, CFAR processing is not performed after the inter-frame FFT, but 2D-DBF operations are performed on the azimuth and elevation angles of each region of interest (or unit interval). 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.

[0175] The step division of the above methods is only for the purpose of clear description. When implemented, they can be combined into one step or some steps can be split and decomposed into multiple steps. As long as they include the same logical relationship, they are all within the scope of protection of this patent; adding insignificant modifications to the algorithm or process or introducing insignificant designs without changing the core design of the algorithm and process are all within the scope of protection of this patent.

[0176] The embodiment of the present application 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 application, 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). Among them, the above-mentioned digital processing module may include a baseband module and a main control module, etc., and each module may be configured to execute each corresponding step in the target detection method in the above-mentioned embodiment.

[0177] In some embodiments, the digital signal processing module is used to perform constant false alarm processing on at least two transceiver channels separately based on the range-Doppler spectrum to obtain candidate target data of at least two transceiver channels; and process the candidate target data of at least two transceiver channels to obtain final target data.

[0178] In some embodiments, the digital signal processing module is also used to verify the targets in each preset area according to the situation that the targets indicated in the constant false alarm detection results of the range-Doppler spectrum fall into each preset area, and the preset areas are obtained by dividing the detection space; according to the verified targets, the detection results of each preset area are output.

[0179] 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.

[0180] According to other embodiments of the present application, 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 circuit may be integrated with the integrated circuit as an integrated device 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 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.

[0181] It should be noted that the electromagnetic waves in the present application 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.

[0182] An embodiment of the present application 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.

[0183] The embodiment of the present application also provides an electronic 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 application. 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).

[0184] 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.

[0185] 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.

[0186] 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.

[0187] For example, the electronic device in the embodiment of the present application 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.

[0188] Specifically, based on the above embodiments, in an optional embodiment of the present application, the electromagnetic wave sensor can be arranged outside the device body, or arranged inside the device body, and in other optional embodiments of the present application, the electromagnetic wave sensor can also be arranged partially inside the device body and partially outside the device body. The embodiments of the present application are not limited to this, and the specific situation can be determined.

[0189] 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.

[0190] The embodiment of the present application also provides a non-transitory computer-readable storage medium on which computer-readable instructions are stored. When the instructions are executed by a processor, the processor executes the feeder unequal length compensation method as described above.

[0191] 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 method of the present application 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 method of the present application.

[0192] 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.

[0193] 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.

[0194] Program code for performing the operations of the present application 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 the case of 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., via the Internet using an Internet service provider).

[0195] 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.

[0196] 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.

[0197] According to an embodiment of the present application, 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, the 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.

[0198] It should be noted that radio devices can achieve functions such as target detection and / or communication by transmitting and receiving radio 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.

[0199] 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).

[0200] 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.

[0201] The above-described embodiments only express the preferred embodiments of the present invention and the technical principles used. The description is relatively specific and 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 more 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.

Claims

1. A target detection method, characterized in that: include: Based on the range-Doppler spectrum, performing constant false alarm processing on at least two transceiver channels separately to obtain candidate target data of at least two transceiver channels; The candidate target data of at least two transceiver channels are processed to obtain final target data.

2. The target detection method according to claim 1, characterized in that: The method of performing constant false alarm processing on at least two transceiver channels based on the range-Doppler spectrum includes: Based on the range-Doppler spectrum, performing constant false alarm processing on each transmitting and receiving channel respectively; The processing of the candidate target data based on at least two transceiver channels includes: The candidate target data of each transceiver channel is processed.

3. The target detection method according to claim 1 or 2, characterized in that: The method of performing constant false alarm processing on at least two transceiver channels based on the range-Doppler spectrum includes: According to the range-Doppler spectrum, the noise floor of the transceiver channel that is subjected to the constant false alarm processing is estimated separately to obtain the noise floor estimation result of the single transceiver channel; According to the noise floor estimation result of the single transceiver channel, constant false alarm detection is performed on the corresponding transceiver channel separately.

4. The target detection method according to claim 3, characterized in that: The noise floor estimation result of the single transceiver channel is composed of the noise floor estimation results of each distance unit on the single transceiver channel, and the constant false alarm detection result of the single transceiver channel is composed of the constant false alarm detection results of each Doppler unit of each distance unit on the single transceiver channel; According to the noise floor estimation result of the single transceiver channel, the corresponding transceiver channel is subjected to constant false alarm detection separately, which is realized by the following expression: Wherein, F(c,r,v) is the constant false alarm detection result of the vth Doppler unit of the rth range unit of the cth channel, P(c,r,v) is the echo energy on the vth Doppler unit of the rth range unit of the cth channel, n′(c,r) is the noise floor estimation result of the rth range unit of the cth channel, β is a preset parameter, β≥1.

5. The target detection method according to claim 3, characterized in that: The step of separately estimating the noise floor of the receiving and transmitting channels that are separately subjected to constant false alarm processing according to the range-Doppler spectrum includes: According to the range-Doppler spectrum, a noise floor estimation is performed separately on a receiving and transmitting channel that is subjected to a constant false alarm processing within a preset range; The upper limit of the preset range is determined according to the noise floor estimation result of the farthest distance unit.

6. The target detection method according to claim 5, characterized in that: According to the range-Doppler spectrum, the noise floor estimation is performed separately on the receiving and transmitting channels that are subjected to the constant false alarm processing within a preset range, which is achieved by the following expression: n i =min(n i α*n g ): Among them, n i ' is the noise floor estimation result of the ith distance unit of a single transceiver channel within the preset range, n i is the initial noise floor estimation result of the i-th range unit on a single transceiver channel determined according to the range-Doppler spectrum, α is a preset parameter, α≥1, n g It is the initial noise floor estimation result of the farthest distance unit on a single transmit and receive channel.

7. The target detection method according to claim 1 or 2, characterized in that: The processing of the candidate target data based on at least two transceiver channels includes: A constant false alarm detection is performed according to the currently acquired candidate target data and a preset threshold to obtain the final target data.

8. The target detection method according to claim 7, characterized in that: The constant false alarm detection is performed according to the candidate target data currently obtained and the preset threshold value, which is achieved by the following expression: Wherein, F(c,r,v) is the candidate target data of the vth Doppler unit of the rth range unit of the cth channel, N c is the total number of channels, F0 is the preset threshold, and F′(r,v) is the final target data of the vth Doppler unit of the rth range unit.

9. The target detection method according to claim 1 or 2, characterized in that: The method further comprises: Perform distance dimension FFT on frame data; Extract target data from the frame data obtained after distance dimension FFT processing; Performing sliding windowing on the target data, and performing 2D FFT on the windowed data to obtain the range-Doppler spectrum; The length of the sliding window corresponds to a duration of the same order of magnitude as the period of the periodic motion of the human physiological target.

10. The target detection method according to claim 9, characterized in that: The step of extracting target data from the frame data obtained after the distance dimension FFT processing comprises: Data corresponding to a chirp cycle is extracted from each frame of data obtained after distance-dimensional FFT processing as the target data.

11. The target detection method according to claim 9, characterized in that: The step of extracting target data from the frame data obtained after the distance dimension FFT processing comprises: According to each frame of data obtained after the distance dimension FFT processing, the parameters corresponding to each distance unit in each frame of data are determined to generate the target data.

12. The target detection method according to claim 9, characterized in that: The step of extracting target data from the frame data obtained after the distance dimension FFT processing comprises: Extracting data within a preset distance range from each frame of data obtained after distance dimension FFT processing as the target data; and / or, The step of performing 2D FFT on the windowed data to obtain the range-Doppler spectrum comprises: The windowed data within the preset Doppler range is subjected to 2D FFT to obtain the range-Doppler spectrum.

13. The target detection method according to claim 9, characterized in that: In the case where the data is cached using Ring FIFO, the method further includes: Data is read from the Ring FIFO, and the read data is rearranged.

14. A target detection method, characterized in that: include: Perform target detection according to the target detection method according to any one of claims 1 to 13 to obtain a target; According to the target falling into each preset area, verifying the target in each preset area, wherein the preset area is obtained by dividing the detection space; According to the verified target, the detection result of each preset area is output.

15. The target detection method according to claim 14, characterized in that: The verifying the target in each preset area according to the target falling in each preset area includes: The targets in each preset area are verified according to the proportion of the targets falling within each preset area in the detected targets.

16. The target detection method according to claim 14, characterized in that: The verifying the target in each preset area according to the target falling in each preset area includes: The target in each preset area is verified according to the signal-to-noise ratio of the target falling within each preset area.

17. The target detection method according to any one of claims 14 to 16, characterized in that: The detection space is inside the vehicle, and the preset area includes at least any one of the following: an area corresponding to a seat and an area corresponding to a foot pad.

18. An integrated circuit, characterized in that: It includes a radio frequency module, an analog signal processing module and a digital signal processing module connected in sequence; Wherein, the RF module is used to generate RF transmission signals and receive RF reception signals; The analog signal processing module is used to down-convert the RF received signal to obtain an intermediate frequency signal; The digital signal processing module is used to perform analog-to-digital conversion on the intermediate frequency signal, and based on the range-Doppler spectrum, perform constant false alarm processing on at least two transceiver channels separately to obtain candidate target data of at least two transceiver channels; and process the candidate target data of at least two transceiver channels to obtain final target data.

19. The integrated circuit according to claim 18, characterized in that The digital signal processing module is also used to verify the target in each preset area according to the situation that the target indicated in the constant false alarm detection result of the range-Doppler spectrum falls into each preset area, and the preset area is obtained by dividing the detection space; According to the verified target, the detection result of each preset area is output.

20. An electromagnetic wave sensor, characterized in that: include: Carrier; An integrated circuit as claimed in claim 18 or 19, arranged on a 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; The integrated circuit is connected to the antenna and is used to transmit a radio frequency transmission signal and / or receive a radio frequency reception signal.

21. A terminal device, characterized in that: include: Equipment body; and an electromagnetic wave sensor as claimed in claim 20 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.

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