Velocity ambiguity resolution method, device, electronic device and storage medium

Abstract

The embodiments of the present application provide a method, apparatus, electronic device, and storage medium for velocity ambiguity resolution. By combining the CFAR two-dimensional masks of multiple frames of radar signals, objects are associated, and the movement direction of the objects and a set of possible velocity values can be estimated. Based on the estimated target movement direction and the set of velocity values, some MIMO compensation modes are eliminated to obtain the remaining MIMO preprocessing compensation modes; when processing with the MIMO algorithm, only the remaining MIMO compensation modes are traversed to obtain the optimal MIMO compensation mode, which can simplify the operation complexity of the algorithm, increase the MIMO ambiguity resolution accuracy rate, and greatly reduce the performance loss caused by the sudden change of the target velocity and azimuth due to MIMO compensation errors. While improving the target detection rate, the target misdetection rate is reduced. Especially in the scenario of target congestion, the performance is more significant.

Description

Technical Field

[0001] The present application relates to the technical field of radar signal processing, and particularly to a method and device for velocity ambiguity resolution, an electronic device, and a storage medium. Background Art

[0002] Currently, millimeter-wave radar antennas generally adopt the TDM (Time-division multiplexing)-MIMO (Multiple In Multiple Out) form. By using virtual array elements, the actual size of the antenna can be effectively reduced, and high-resolution target angle measurement results similar to those of a large-size antenna can be obtained. Suppose a TDM-MIMO radar includes M transmitting antennas and N receiving antennas. By reasonably designing the spacing between the transmitting antennas and the spacing between the receiving antennas, the effect of 1 transmit and M*N receive can be achieved. Suppose the spacing between adjacent transmitting antennas is D, and the spacing between adjacent receiving antennas is d. To ensure that no antenna grating lobes appear, it is generally required that d ≤ 0.5λ, where λ is the radar wavelength. To maximize the utilization of the antenna aperture, it is generally required that D = Nd in the design.

[0003] Taking the FMCW (Frequency Modulated Continuous Wave) signal system with 2 transmitting antennas and 4 receiving antennas as an example, at this time d = 0.5λ, D = 2λ, and the schematic diagram of the virtual array elements of the obtained TDM-MIMO radar is as Figure 1 shown, where virtual antenna represents the virtual antenna and real antenna represents the real antenna. Since multiple transmitting antennas of the TDM-MIMO radar use an alternating signal transmission working form, there are two problems: First, the phase change amount brought by the Doppler frequency of the moving target during the switching time of different transmitting antennas will be coupled to each receiving antenna, affecting the correct synthesis of the receiving antenna aperture; Second, TDM itself reduces the sampling rate in slow time, significantly reducing the unambiguous velocity measurement range, and once velocity ambiguity occurs, it will further cause deviation in angle measurement. It can be seen that how to perform velocity ambiguity resolution for TDM-MIMO radar has become an urgent problem to be solved. Summary of the Invention

[0004] The purpose of the embodiments of the present application is to provide a method and device for velocity ambiguity resolution, an electronic device, and a storage medium to achieve velocity ambiguity resolution for TDM-MIMO radar. The specific technical solutions are as follows:

[0005] In a first aspect, the embodiments of the present application provide a method for velocity ambiguity resolution, and the method includes:

[0006] Obtain multiple frames of radar signals, and respectively determine the power diagrams of each frame of radar signals;

[0007] Map each of the power diagrams to a two-dimensional matrix in the range dimension and the Doppler dimension to obtain the constant false alarm rate (CFAR) two-dimensional mask of each frame of radar signal;

[0008] According to the time sequence of each frame of radar signal, perform trajectory association on the same objects in the CFAR two-dimensional masks of each frame of radar signal, and respectively obtain the estimated motion speed and estimated motion direction of each object;

[0009] For each object, select the preprocessing MIMO compensation mode of the object in the preset MIMO compensation modes according to the estimated motion speed and estimated motion direction of the object;

[0010] For each object, traverse each MIMO compensation mode in the preprocessing MIMO compensation mode of the object to obtain the target MIMO compensation mode, and obtain the velocity ambiguity resolution result of the object in the target MIMO compensation mode, where the velocity ambiguity resolution result of the object includes the true speed and true azimuth of the object.

[0011] In a possible implementation manner, the obtaining of multiple frames of radar signals and respectively determining the power diagrams of each frame of radar signal includes:

[0012] Obtain the analog-to-digital converter (ADC) data of each channel of the radar virtual antenna array to obtain multiple frames of radar signals;

[0013] Perform two-dimensional fast Fourier transform on the ADC data of each channel in each frame of radar signal to obtain the virtual array vectors of each frame of radar signal;

[0014] Perform non-coherent accumulation on the virtual array vectors of each frame of radar signal to obtain the power diagrams of each frame of radar signal.

[0015] In a possible implementation manner, the mapping of each of the power diagrams to a two-dimensional matrix in the range dimension and the Doppler dimension to obtain the CFAR two-dimensional mask of each frame of radar signal includes:

[0016] For each power diagram, perform CFAR detection on the power diagram to obtain the noise intensity threshold of the power diagram;

[0017] For each power diagram, in the two-dimensional matrix in the range dimension and the Doppler dimension, set the power greater than the noise intensity threshold of the power diagram to a first value, and set the power not greater than the noise intensity threshold of the power diagram to a second value to obtain the CFAR two-dimensional mask of the radar signal corresponding to the power diagram, where the area corresponding to the first value contains objects, and the area corresponding to the second value does not contain objects.

[0018] In a possible implementation, performing trajectory association on the same objects in the CFAR two-dimensional masks of each frame of radar signals according to the time sequence of each frame of radar signals, and respectively obtaining the estimated motion speed and estimated motion direction of each object, including:

[0019] Performing connected component analysis on each of the CFAR two-dimensional masks to obtain the target information of each object in each of the CFAR two-dimensional masks. Wherein, for any object, the target information of the object includes the object width, object height, and object center coordinates of the object;

[0020] Performing trajectory association on the same objects in the CFAR two-dimensional masks of each frame of radar signals according to the time sequence of each frame of radar signals and the target information of each object, and respectively obtaining the motion trajectories of each object;

[0021] Determining the estimated motion speed and estimated motion direction of each object respectively at least according to the motion trajectories of each object and the time difference between each frame of radar signals.

[0022] In a possible implementation, for each object, selecting the preprocessing MIMO compensation mode of the object in the preset MIMO compensation modes according to the estimated motion speed and estimated motion direction of the object, including:

[0023] For each object, obtaining the motion direction and motion speed of the object under each compensation mode in the preset MIMO compensation modes;

[0024] For each object, selecting, in the preset MIMO compensation modes, the compensation mode with the same motion direction as the estimated motion direction of the object to obtain the filtered compensation mode of the object;

[0025] For each object, selecting, in the filtered compensation mode of the object, the compensation mode with the error between the motion speed and the estimated motion speed of the object within the preset range to obtain the preprocessing MIMO compensation mode of the object.

[0026] In a second aspect, an embodiment of the present application provides a velocity ambiguity resolution device, where the device includes:

[0027] A power map acquisition unit, configured to acquire multiple frames of radar signals and respectively determine the power maps of each frame of radar signals;

[0028] A CFAR detection unit, configured to respectively map each of the power maps into a two-dimensional matrix in the range dimension and the Doppler dimension to obtain the CFAR two-dimensional masks of each frame of radar signals;

[0029] A motion estimation unit, configured to perform trajectory association on the same objects in the CFAR two-dimensional masks of each frame of radar signals according to the time sequence of each frame of radar signals, and respectively obtain the estimated motion speed and estimated motion direction of each object;

[0030] A compensation mode rejection unit, configured to select a preprocessing MIMO compensation mode for each object from a preset MIMO compensation mode according to the estimated motion speed and estimated motion direction of the object.

[0031] A velocity ambiguity resolution unit, configured to traverse each MIMO compensation mode in the preprocessing MIMO compensation mode of each object to obtain a target MIMO compensation mode, and obtain a velocity ambiguity resolution result of the object under the target MIMO compensation mode, where the velocity ambiguity resolution result of the object includes the true velocity and true azimuth of the object.

[0032] In a possible implementation manner, the power map acquisition unit is specifically configured to: acquire analog-to-digital converter (ADC) data of each channel of a radar virtual antenna array to obtain multiple frames of radar signals; perform two-dimensional fast Fourier transform on the ADC data of each channel in each frame of radar signal to obtain virtual array vectors of each frame of radar signals; perform non-coherent accumulation on the virtual array vectors of each frame of radar signals to obtain a power map of each frame of radar signals.

[0033] In a possible implementation manner, the CFAR detection unit is specifically configured to: perform CFAR detection on each power map to obtain a noise intensity threshold of the power map; in a two-dimensional matrix of the range dimension and Doppler dimension for each power map, set the power greater than the noise intensity threshold of the power map to a first value, and set the power not greater than the noise intensity threshold of the power map to a second value, to obtain a CFAR two-dimensional mask of the radar signal corresponding to the power map, where the area corresponding to the first value contains an object, and the area corresponding to the second value does not contain an object.

[0034] In a possible implementation manner, the motion estimation unit is specifically configured to: perform connected component analysis on each of the CFAR two-dimensional masks to obtain target information of each object in each of the CFAR two-dimensional masks, where for any object, the target information of the object includes the object width, object height, and object center coordinates of the object; perform trajectory association on the same objects in the CFAR two-dimensional masks of each frame of radar signals according to the time sequence of each frame of radar signals and the target information of each object, respectively obtain motion trajectories of each object; determine the estimated motion speed and estimated motion direction of each object respectively at least according to the motion trajectories of each object and the time difference between each frame of radar signals.

[0035] In a possible implementation manner, the compensation mode elimination unit is specifically configured to: for each object, obtain the movement direction and movement speed of the object under each compensation mode in the preset MIMO compensation mode; for each object, in the preset MIMO compensation mode, select the compensation mode whose movement direction is the same as the estimated movement direction of the object to obtain the filtered compensation mode of the object; for each object, in the filtered compensation mode of the object, select the compensation mode whose error between the movement speed and the estimated movement speed of the object is within the preset range to obtain the preprocessed MIMO compensation mode of the object.

[0036] In a third aspect, an embodiment of the present application provides an electronic device, including a processor and a memory;

[0037] The memory is used to store a computer program;

[0038] When the processor is used to execute the program stored on the memory, the speed ambiguity resolution method described in any one of the present application is implemented.

[0039] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, in which a computer program is stored, and when the computer program is executed by a processor, the speed ambiguity resolution method described in any one of the present application is implemented.

[0040] Beneficial effects of the embodiments of the present application:

[0041] The speed ambiguity resolution method, device, electronic device, and storage medium provided by the embodiments of the present application combine the CFAR two-dimensional mask of multi-frame radar signals to associate objects, and can estimate the object movement direction and the possible speed value set. Based on the estimated target movement direction and speed value set, some MIMO compensation modes are eliminated to obtain the remaining MIMO preprocessing compensation modes; when the MIMO algorithm is processed, only the remaining MIMO compensation modes need to be traversed to obtain the optimal MIMO compensation mode, which can simplify the algorithm operation complexity and increase the MIMO ambiguity resolution correct rate. And it greatly reduces the performance loss caused by the sudden change of the target speed and azimuth due to MIMO compensation errors, improves the target detection while reducing the target false detection. Especially in the target congestion scenario, the performance is more significant. Of course, it is not necessary for any product or method implementing the present application to achieve all the above advantages at the same time. Description of the Drawings

[0042] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0043] Figure 1 It is a schematic diagram of a virtual array element of an MIMO radar in the related art;

[0044] Figure 2 It is a schematic diagram of the operation process of the velocity ambiguity resolution system according to the embodiment of the present application;

[0045] Figure 3 It is a schematic diagram of a CFAR two-dimensional mask according to the embodiment of the present application;

[0046] Figure 4 It is a schematic diagram of object trajectory association according to the embodiment of the present application;

[0047] Figure 5 It is a schematic diagram of a velocity ambiguity resolution method according to the embodiment of the present application;

[0048] Figure 6 It is a schematic diagram of a specific implementation manner of step S103 in the embodiment of the present application;

[0049] Figure 7 It is a schematic diagram of a specific implementation manner of step S104 in the embodiment of the present application;

[0050] Figure 8 It is a schematic diagram of a velocity ambiguity resolution device according to the embodiment of the present application;

[0051] Figure 9 It is a schematic diagram of an electronic device according to the embodiment of the present application. Detailed implementation manners

[0052] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, rather than all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0053] First, the terms in the present application are explained.

[0054] Radar: An electronic device that uses electromagnetic waves to detect targets. The radar irradiates the target by transmitting electromagnetic waves and receives its echo, thereby obtaining information such as the distance from the target to the electromagnetic wave emission point, the rate of change of distance (radial velocity), azimuth, altitude, etc.

[0055] MIMO (Multiple In Multiple Out) radar: MIMO radar is a technology that improves the radar's angle estimation ability. There are several specific implementation forms, mainly FDM (Frequency Division Multiplexing), CDM (Code Division Multiplexing), and TDM (Time-division multiplexing). Considering the cost limitation of semiconductor devices and the implementation complexity, currently, millimeter-wave radars basically adopt TDM-MIMO technology. In this application, each MIMO refers to TDM-MIMO unless otherwise specified.

[0056] Velocity ambiguity: The phenomenon of confusion in the measured target velocity caused by spectral aliasing, making it difficult to distinguish the true velocity of the target. Especially for TDM-MIMO radars, due to the reduction of the sampling rate in slow time, the non-ambiguous velocity measurement range is significantly reduced, resulting in a more likely occurrence of velocity ambiguity problems.

[0057] Current millimeter-wave radar antennas generally adopt the TDM (Time-division multiplexing)-MIMO (Multiple In Multiple Out) form. By using virtual array elements, the actual size of the antenna can be effectively reduced, and high-resolution target angle measurement results similar to those of a large-size antenna can be obtained. Suppose a TDM-MIMO radar contains M transmitting antennas and N receiving antennas. By reasonably designing the spacing between transmitting antennas and the spacing between receiving antennas, the effect of 1 transmit and M*N receive can be achieved. Suppose the spacing between adjacent transmitting antennas is D, and the spacing between adjacent receiving antennas is d. To ensure no antenna grating lobes, it is generally required that d ≤ 0.5λ, where λ is the radar wavelength. To maximize the utilization of the antenna aperture, it is generally required that D = Nd in the design.

[0058] Taking the FMCW (Frequency Modulated Continuous Wave) signal system with 2 transmitting antennas and 4 receiving antennas as an example, at this time, d = 0.5λ, D = 2λ, and the schematic diagram of the virtual array elements of the obtained TDM-MIMO radar is as Figure 1As shown in the figure. Since multiple transmit antennas of the TDM-MIMO radar use an alternating signal transmission mode, there are two problems: First, the phase change amount brought by the Doppler frequency of the moving target during the switching time of different transmit antennas will be coupled to each receive antenna, affecting the correct synthesis of the receive antenna aperture; Second, TDM itself reduces the sampling rate in slow time, significantly reducing the unambiguous velocity measurement range, and once velocity ambiguity occurs, it will further cause deviation in angle measurement. It can be seen that the measurement error of the TDM-MIMO radar in the prior art is relatively high.

[0059] In the related art, in order to increase the accuracy of the TDM-MIMO radar, the following compensation method is used for velocity ambiguity resolution:

[0060] Step 1: Estimate the velocity-induced phase shift of the virtual array element vector S of the signal Velocity-induced phase shift Is related to the true velocity v true Of the target. The velocity v est Estimated based on the power map may have velocity ambiguity. Therefore, the true velocity v true Of the target is unknown, but its possible value set can be obtained from the velocity estimated by the power map, v true ∈{v est , v est +2v max , v est -2v max}, where v max Is the maximum unambiguous measurement velocity. It should be emphasized that the relationship between the true velocity and the velocity estimated by the power map is v true = v est +2xv max , x ∈ R. Usually, for the actual application scenario, x takes values of {0, 1, -1}, which can meet the requirements. In addition, the value of x in the MIMO algorithm is also restricted by the transmit antennas.

[0061] For different velocity values v est , v est +2v max , v est -2v max , the obtained velocity-induced phase shifts are respectively called compensation mode 0, compensation mode 1, and compensation mode 2, and the values are respectively And

[0062] In step 1, the calculation formula relationships between various symbols are as follows:

[0063] Estimate the velocity v est : Directly calculate and obtain based on the power map.

[0064] True speed v true and the estimated speed v est relationship:

[0065] v true ∈{v est , v est + 2v max , v est - 2v max}

[0066] Velocity-induced phase shift

[0067]

[0068] The maximum speed v max :

[0069]

[0070] where λ is the radar wavelength and T c is a chirp period. As Figure 1 shown, in the case of 2 transmitting antennas, T c = 2T.

[0071] Step 2: Use to correct the phase of each element of the virtual array vector S to obtain the corrected virtual array vector S c . Based on different compensation modes, the corresponding corrected virtual array vectors S c0 , S c1 and S c2 are obtained.

[0072] In Step 2, the calculation formula relationships between the symbols are as follows:

[0073] Virtual array vector S:

[0074]

[0075] Corrected virtual array vector S c :

[0076]

[0077] If the correction is correct, then the formula for S c is as follows:

[0078]

[0079] where is the phase caused by the path difference between adjacent receiving antennas θ is the target azimuth angle, d is the spacing between adjacent receiving antennas, and λ is the radar wavelength. is the phase change amount brought by the target Doppler frequency within the switching time of adjacent transmitting antennas. S mn is the signal obtained after two-dimensional FFT (Fast Fourier Transform) transformation of the discrete digital signal of the echo signal transmitted by the m-th transmitting antenna and received by the n-th receiving antenna.

[0080] Step 3: Perform the first Fourier transform on the corrected virtual array vector S c to generate the corrected virtual array spectrum P c . For different compensation modes, the corresponding virtual array spectra P c0 , P c1 and P c2 are obtained.

[0081] Step 4: Obtain the optimal compensation mode based on the corrected virtual array spectrum. Theoretically, the compensation method corresponding to the array spectrum with the highest peak is the optimal compensation mode.

[0082] Step 5: Obtain the target speed and azimuth. The speed and azimuth solved by the optimal compensation mode are the target speed and azimuth finally obtained for the target.

[0083] However, when using the above method, due to various factors such as noise disturbance and hardware errors, there is a certain error ratio, close to 10%, when the above algorithm performs velocity ambiguity resolution. The wrong compensation mode will cause the output target point cloud speed and azimuth to be abnormal. Moreover, the TDM-MIMO compensation methods for the same target at different times (different frames) are independent of each other and are not the same. It is manifested that the speed of the same target changes continuously over time, and the target azimuth jumps greatly, which in turn affects target tracking, resulting in a decline in performance indicators such as detection and false detection. Especially in a congested scene, the phenomenon is more significant.

[0084] The inventors found in their research that due to the perturbation existing in the data itself, it is impossible to optimize single-frame data; while the correlation between multiple frames in the time domain undoubtedly provides additional decision-making features for TDM-MIMO velocity ambiguity resolution. Specifically, by combining the CFAR (Constant False-Alarm Rate) detection results of multiple radar frames, the targets are correlated to estimate the target movement direction and the possible set of velocity values. Thus, some TDM-MIMO compensation phase patterns are excluded, and then the correct probability of TDM-MIMO ambiguity resolution is increased. In view of this, an embodiment of the present application provides a velocity ambiguity resolution system, including: a power map acquisition module, a CFAR detection module, a MIMO mode preprocessing module, a DOA (Direction Of Arrival) estimation module, and a clustering and tracking module.

[0085] As Figure 2 shown, the input of the entire system is the ADC (Analog to Digital Converter) data of the radar, and the output is the final target trajectory list information. This system is also the currently common radar target detection + tracking algorithm framework. Each module of the velocity ambiguity resolution system will be described in detail below.

[0086] Power map acquisition module: The input is the ADC data, and the output is the radar power map. The ADC data of each channel of the virtual antenna array are respectively subjected to two-dimensional FFT (Fast Fourier Transform), and then non-coherent accumulation is performed to obtain the power map.

[0087] CFAR detection module: The input is the power map, and the output is the set of detected target points, represented by a binary CFAR two-dimensional mask. A possible CFAR two-dimensional mask of the set of target points can be as Figure 3 shown, where the abscissa is the Doppler dimension and the ordinate is the Range dimension. After processing the noise in the input power map to determine a threshold, this threshold is compared with each signal point in the power map. If the input signal exceeds this threshold, it is determined that there is a target; otherwise, it is determined that there is no target.

[0088] MIMO mode preprocessing module: For the CFAR two-dimensional mask, the detected targets appear as bright spots. Based on the correlation of targets in multiple time domain frames, the movement trajectory of the target in the Range-Doppler dimension can be obtained, where one frame corresponds to one CFAR two-dimensional mask. A possible schematic diagram of target correlation in multiple time domain frames can be as Figure 4As shown in the figure. Among them, the dimension K represents the data frame number. After obtaining the motion trajectory of the target in the Range-Doppler dimension, the MIMO compensation mode of the target can be pre-screened. The screening process is as follows:

[0089] Step 1: Perform connected component analysis based on the output CFAR two-dimensional mask to obtain the target bright spot list of the current frame. The target bright spot list of the current frame includes the connected component information of each bright spot in the CFAR two-dimensional mask image, that is, the target information of the bright spot; for any bright spot, the target information of the bright spot can include information such as the width of the bright spot, the height of the bright spot, the range-Doppler coordinates of the center of the bright spot, and the signal-to-noise ratio.

[0090] Step 2: Perform trajectory association based on time-domain accumulation of multiple frames. When performing trajectory association, it can be processed based on an image tracking algorithm.

[0091] Step 3: Based on the target associated trajectory, obtain the target motion direction (away from the radar or towards the radar), and the magnitude of the target speed. Based on the CFAR two-dimensional mask, the estimated target speed v est can be obtained, but due to possible velocity ambiguity, the true target speed v true has an unknown value. Based on information such as the target trajectory displacement, frame difference, and frame rate, the accurate target speed information can be estimated.

[0092] Step 4: Eliminate some MIMO compensation modes based on the target motion direction and speed magnitude to obtain the MIMO preprocessing compensation mode.

[0093] DOA estimation module: For the CFAR detection points (bright spots), extract the data of each channel in the two-dimensional FFT of the virtual antenna array and perform one-dimensional azimuth FFT processing to obtain the direction of arrival of the target. When using the MIMO de-ambiguation algorithm for processing in this application, only traverse the MIMO preprocessing compensation modes, and select the optimal compensation mode, and the corresponding speed and azimuth information from them.

[0094] Clustering and tracking module: Use relevant clustering and tracking algorithms to cluster and track the CFAR detection points. When clustering, converge the target point cloud and output information such as the target speed and azimuth. Tracking is generally divided into two major modules: First, track initiation, generate an initial track, and after confirmation, tracking processing can be performed; Second, track maintenance, including track update, extrapolation, extinction processing, etc.

[0095] In the embodiments of the present application, by combining the CFAR two-dimensional mask of multiple frames of radar signals, objects are associated, and the moving direction of the objects and the set of possible speed values can be estimated. Based on the estimated target moving direction and the set of speed values, some MIMO compensation modes are eliminated to obtain the remaining MIMO preprocessing compensation modes; when processing with the MIMO algorithm, only the remaining MIMO compensation modes are traversed, and the optimal MIMO compensation mode is selected from them, thereby increasing the MIMO ambiguity resolution accuracy rate.

[0096] The embodiments of the present application also provide a method for speed ambiguity resolution. Refer to Figure 5 , and this method includes:

[0097] S101, Obtain multiple frames of radar signals, and respectively determine the power diagrams of each frame of radar signals.

[0098] The method for speed ambiguity resolution in the embodiments of the present application can be implemented by an electronic device, which can be a radar device, for example, specifically a MIMO radar device, or a device with computing functions connected to the radar device, etc.

[0099] The method for obtaining the power diagram of the radar signal can refer to the way of obtaining the power diagram of the radar signal in the related art. In one implementation, the above-mentioned obtaining multiple frames of radar signals and respectively determining the power diagrams of each frame of radar signals includes:

[0100] Step 1, Obtain the ADC data of each channel of the radar virtual antenna array to obtain multiple frames of radar signals.

[0101] The discrete digital signal data output by the ADC of each channel of the radar virtual antenna array can be obtained to obtain multiple frames of radar signals.

[0102] Step 2, Respectively perform two-dimensional fast Fourier transform on the ADC data of each channel in each frame of radar signals to obtain the virtual array vectors of each frame of radar signals.

[0103] The ADC data of each channel in each frame of radar signals can be subjected to FFT transform in two dimensions, namely the ADC sampling sequence number dimension and the sampling period dimension, to obtain the virtual array vectors of each frame of radar signals.

[0104] Step 3, Respectively perform non-coherent accumulation on the virtual array vectors of each frame of radar signals to obtain the power diagrams of each frame of radar signals.

[0105] S102, Respectively map each of the above power diagrams into a two-dimensional matrix in the range dimension and the Doppler dimension to obtain the CFAR two-dimensional mask of each frame of radar signals.

[0106] The CFAR two-dimensional mask includes a range dimension and a Doppler dimension. The CFAR two-dimensional mask can be binary. For example, the area where an object exists can be represented by a first value, and the area where no object exists can be represented by a second value. Here, the object is the object detected by the radar. A possible CFAR two-dimensional mask can be as shown in Figure 3 where the abscissa is the Doppler dimension and the ordinate is the range dimension.

[0107] In a possible implementation, mapping each of the above power maps into a two-dimensional matrix of the range dimension and the Doppler dimension to obtain the constant false alarm rate (CFAR) two-dimensional mask of each frame of radar signal includes:

[0108] Step A: For each power map, perform CFAR detection on the power map to obtain the noise intensity threshold of the power map.

[0109] Step B: For each power map, in the two-dimensional matrix of the range dimension and the Doppler dimension, set the power greater than the noise intensity threshold of the power map to the first value, and set the power not greater than the noise intensity threshold of the power map to the second value, to obtain the CFAR two-dimensional mask of the radar signal corresponding to the power map. Among them, the area corresponding to the first value contains an object, and the area corresponding to the second value does not contain an object. The area corresponding to the first value of the CFAR two-dimensional mask indicates that there is an object at the actual position corresponding to this area, and the area corresponding to the second value indicates that there is no object at the actual position corresponding to this area.

[0110] S103: According to the time sequence of each frame of radar signal, perform trajectory association on the same objects in the CFAR two-dimensional masks of each frame of radar signal, and respectively obtain the estimated motion speed and estimated motion direction of each object.

[0111] The target tracking algorithm in related technologies can be used to track the same objects in each CFAR two-dimensional mask, associate the trajectories of the same objects, so as to obtain the estimated motion speed and estimated motion direction of each object.

[0112] S104: For each object, select the preprocessing MIMO compensation mode of the object in the preset MIMO compensation modes according to the estimated motion speed and estimated motion direction of the object.

[0113] The preset MIMO compensation modes include multiple MIMO compensation modes. For any object, in the preset MIMO compensation modes, the MIMO compensation modes that do not meet the constraint conditions of the estimated motion speed and estimated motion direction of the object are excluded, and the remaining MIMO compensation modes in the preset MIMO compensation modes are used as the preprocessing MIMO compensation modes for the object. In one example, for any object, in the preset MIMO compensation modes, all compensation modes with an error in the motion speed within a preset range from the estimated motion speed of the object and with the same motion direction as the estimated motion direction of the object are selected as the preprocessing MIMO compensation modes for the object.

[0114] S105. For each object, traverse each MIMO compensation mode in the preprocessing MIMO compensation modes of the object to obtain a target MIMO compensation mode, and obtain the velocity ambiguity resolution result of the object under the above target MIMO compensation mode, where the velocity ambiguity resolution result of the object includes the true velocity and true azimuth of the object.

[0115] Traverse each MIMO compensation mode in the preprocessing MIMO compensation modes to obtain the optimal MIMO compensation mode, that is, the target MIMO compensation mode, and calculate the velocity ambiguity resolution result under the target MIMO compensation mode, and finally obtain the velocity and azimuth of the object. The specific method of traversing each MIMO compensation mode in the preprocessing MIMO compensation modes to obtain the optimal MIMO compensation mode, that is, the target MIMO compensation mode, can refer to the calculation method in the related technology. In one implementation, for each MIMO compensation mode in the preprocessing MIMO compensation modes, use the MIMO compensation mode to compensate and correct the virtual array vector to obtain the corrected virtual array vector under the MIMO compensation mode; for each MIMO compensation mode in the preprocessing MIMO compensation modes, perform a Fourier transform on the corrected virtual array vector under the MIMO compensation mode to obtain the virtual array spectrum under the MIMO compensation mode; determine the target MIMO compensation mode according to the virtual array spectra under each MIMO compensation mode in the preprocessing MIMO compensation modes. In one example, the MIMO compensation mode corresponding to the virtual array spectrum with the highest peak is used as the target MIMO compensation mode.

[0116] After obtaining the target MIMO compensation mode, the direction of arrival of the object under the target MIMO compensation mode can be obtained, including the distance information and azimuth information of the object. Using the distance information and azimuth information of the object in each frame of radar signal, the actual motion speed and actual motion direction of each object can be obtained by using the clustering and tracking algorithms in the related technology. Among them, the tracking algorithm generally includes two parts. One is track initiation, generating an initial track, and after confirmation, tracking processing can be carried out; the other is tracking track maintenance, including track update, extrapolation, extinction processing, etc.

[0117] In the embodiments of the present application, by combining the CFAR two-dimensional masks of multiple frames of radar signals to associate objects, the movement direction of the objects and a set of possible speed values can be estimated. Based on the estimated target movement direction and the set of speed values, some MIMO compensation modes are eliminated to obtain the remaining MIMO preprocessing compensation modes; when processing with the MIMO algorithm, only the remaining MIMO compensation modes are traversed to obtain the optimal MIMO compensation mode, which can simplify the operation complexity of the algorithm and increase the correct rate of MIMO ambiguity resolution. And it can greatly reduce the performance loss caused by the sudden change of target speed and azimuth due to MIMO compensation errors, improve the target detection rate while reducing the target false detection rate. Especially in the target congestion scenario, the performance is more significant.

[0118] In a possible implementation manner, referring to Figure 6 , the above-mentioned trajectory association of the same objects in the CFAR two-dimensional masks of each frame of radar signals according to the time sequence of each frame of radar signals, and respectively obtaining the estimated movement speed and estimated movement direction of each object, includes:

[0119] S1031, perform connected component analysis on each of the above-mentioned CFAR two-dimensional masks to obtain the target information of each object in each of the above-mentioned CFAR two-dimensional masks. Among them, for any object, the target information of the object includes the object width, object height and object center coordinates of the object.

[0120] Assume that the first value in the CFAR two-dimensional mask corresponds to white and the second value corresponds to black. Then, perform connected component analysis on the white (i.e., bright spots) in the CFAR two-dimensional mask to obtain the connected component information of each object in the CFAR two-dimensional mask, that is, the target information. For any object, the target information of the object includes the object width, object height and object center coordinates of the object, that is, the coordinates of the object center in the range dimension and Doppler dimension. In addition, the target information of the object may also include information such as the signal-to-noise ratio of the object.

[0121] S1032, according to the time sequence of each frame of radar signals and the target information of each object, perform trajectory association on the same objects in the CFAR two-dimensional masks of each frame of radar signals to respectively obtain the movement trajectories of each object.

[0122] For example Figure 4 as shown, perform trajectory association on the same object in terms of time sequence to respectively obtain the movement trajectories of each object.

[0123] When the objects in the radar signal are relatively sparse, for example, when the density of the objects (the number within a unit space) is less than a preset density threshold, the reliability of motion trajectory association is high, and a relatively large number of frames can be selected each time to associate and obtain the motion trajectories of each object. When the objects are relatively dense, for example, when the density of the objects is greater than the preset density threshold, in order to reduce the situation of incorrect motion trajectory association, the number of frames for associating and obtaining the motion trajectories of each object can be reduced each time. For example, two frames of radar signals can be selected each time to analyze and obtain the motion trajectories of each object, etc.

[0124] S1033. Determine the estimated motion speed and estimated motion direction of each object respectively at least according to the motion trajectories of each object and the time difference between each frame of radar signal.

[0125] For any object, after obtaining the associated trajectory of the object, the estimated motion speed and estimated motion direction of the object can be calculated according to the time difference between each frame of radar signal and the associated trajectory of the object.

[0126] In a possible implementation manner, referring to Figure 7 , for each object, selecting the preprocessing MIMO compensation mode of the object in the preset MIMO compensation mode according to the estimated motion speed and estimated motion direction of the object includes:

[0127] S1041. For each object, obtain the motion direction and motion speed of the object under each compensation mode in the preset MIMO compensation mode.

[0128] The obtaining methods of the motion direction and motion speed of the object under each compensation mode in the preset MIMO compensation mode can refer to the obtaining methods in the related art. In one example, the maximum unambiguous measurement speed and power map estimated speed of the object can be obtained, so as to calculate the motion direction and motion speed of the object under each compensation mode.

[0129] Specifically, estimate the velocity-induced phase shift of the virtual array element vector S of the radar signal Velocity-induced phase shift is related to the true velocity v of the target true The power map estimated speed v obtained based on the power map est may have velocity ambiguity. Therefore, the true velocity v of the target true has an unknown value, but its possible value set can be obtained from the power map estimated speed. v true = v est + 2*k*v max , k = …, -1, 0, 1, …, where v maxis the maximum unambiguous measurement speed, and k is an integer within a finite range, determined according to the motion characteristics of the object (i.e., the range of possible motion speeds of the object). Usually, for actual application scenarios, such as scenarios where the object is a motor vehicle, a pedestrian, a non-motor vehicle, etc., the value of k is {0, 1, -1}, then v true The different values are v est , v est + 2v max , v est - 2v max , and the obtained speed-induced phase shifts are respectively called compensation mode 0, compensation mode 1, and compensation mode 2, that is, each compensation mode in the preset MIMO compensation mode.

[0130] For example, the power map estimates the speed to be 8 m / s, and the maximum unambiguous speed measurement range is -15 m / s to 15 m / s, that is, the maximum unambiguous measurement speed is 15 m / s. For a vehicle object, the value of k is {-1, 0, 1}, and its speed may be {-24, 8, 38}. Under other k value conditions, for example, when k takes 2, the speed of the vehicle will reach 244.8 km / h, which does not conform to the motion characteristics of the vehicle.

[0131] S1042. For each object, in the preset MIMO compensation mode, select the compensation mode whose motion direction is the same as the estimated motion direction of the object to obtain the filtered compensation mode of the object.

[0132] For example, the motion direction of compensation mode 0 is "-", the motion direction of compensation mode 1 is "+", and the motion direction of compensation mode 2 is "+". For an object, the estimated motion direction of the object is the same as "+", then the filtered compensation modes are compensation mode 1 and compensation mode 2. It can be understood that the "-" and "+" here are predefined, and one direction can be set as "+", and the direction opposite to this direction can be set as "-".

[0133] S1043. For each object, in the filtered compensation mode of the object, select the compensation mode whose error between the motion speed and the estimated motion speed of the object is within the preset range to obtain the preprocessed MIMO compensation mode of the object.

[0134] The preset range can be customized according to the actual situation. For example, it can be set to 20%, 40%, 60%, or 80% of the estimated motion speed, etc. For example, if the estimated motion speed of an object is 10 m / s and the preset range is 50% of the estimated motion speed, that is 5 m / s; in the filtered compensation mode, the motion speed of the object in compensation mode 1 is 8 m / s; the motion speed of the object in compensation mode 2 is 38 m / s; then the error between the motion speed of the object in compensation mode 1 and the estimated motion speed is |8 - 10| = 2 m / s, and the error between the motion speed of the object in compensation mode 2 and the estimated motion speed is |38 - 10| = 28 m / s. Comparing the two with 5 m / s respectively, it can be seen that the error between the motion speed of the object in compensation mode 1 and the estimated motion speed is within the preset range. Therefore, compensation mode 1 is selected as the preprocessing MIMO compensation mode.

[0135] It can be seen that through the velocity ambiguity resolution method of the embodiments of the present application, some MIMO compensation modes can be eliminated. When processing the MIMO algorithm, only the remaining MIMO compensation modes need to be traversed to obtain the optimal MIMO compensation mode, which can greatly simplify the complexity of the algorithm, and while increasing the correct rate of MIMO ambiguity resolution, greatly increase the efficiency of velocity ambiguity resolution. And by filtering through the motion direction and then through the motion speed, the number of compensation modes for calculating the speed error can be reduced, thereby reducing the amount of calculation and increasing the efficiency of velocity ambiguity resolution.

[0136] The embodiments of the present application also provide a velocity ambiguity resolution device. Refer to Figure 8 , the device includes:

[0137] A power map acquisition unit 11, configured to acquire multiple frames of radar signals and respectively determine the power maps of each frame of radar signals;

[0138] A CFAR detection unit 12, configured to respectively map each of the above power maps into a two-dimensional matrix in the range dimension and the Doppler dimension to obtain the CFAR two-dimensional mask of each frame of radar signals;

[0139] A motion estimation unit 13, configured to perform trajectory association on the same objects in the CFAR two-dimensional masks of each frame of radar signals according to the time sequence of each frame of radar signals, and respectively obtain the estimated motion speed and estimated motion direction of each object;

[0140] A compensation mode elimination unit 14, configured to, for each object, select the preprocessing MIMO compensation mode of the object from the preset MIMO compensation modes according to the estimated motion speed and estimated motion direction of the object;

[0141] A velocity ambiguity resolution unit 15 is configured to, for each object, traverse each MIMO compensation mode in the preprocessed MIMO compensation modes of the object to obtain a target MIMO compensation mode, and acquire the velocity ambiguity resolution result of the object in the above target MIMO compensation mode, where the velocity ambiguity resolution result of the object includes the true velocity and true azimuth of the object.

[0142] The power map acquisition unit 11 in the embodiment of the present application is equivalent to the power map acquisition module in the above velocity ambiguity resolution system; the CFAR detection unit 12 in the embodiment of the present application is equivalent to the CFAR detection module in the above velocity ambiguity resolution system; the motion estimation unit 13 and the compensation mode rejection unit 14 in the embodiment of the present application are equivalent to the MIMO mode preprocessing module in the above velocity ambiguity resolution system; the velocity ambiguity resolution unit 15 in the embodiment of the present application is equivalent to the DOA estimation module and the clustering tracking module in the MIMO mode preprocessing module of the above velocity ambiguity resolution system.

[0143] In a possible implementation manner, the above power map acquisition unit is specifically configured to: acquire the analog-to-digital converter (ADC) data of each channel of the radar virtual antenna array to obtain multiple frames of radar signals; respectively perform two-dimensional fast Fourier transform on the ADC data of each channel in each frame of radar signal to obtain the virtual array vectors of each frame of radar signal; respectively perform non-coherent accumulation on the virtual array vectors of each frame of radar signal to obtain the power map of each frame of radar signal.

[0144] In a possible implementation manner, the above CFAR detection unit is specifically configured to: for each power map, perform CFAR detection on the power map to obtain the noise intensity threshold of the power map; for each power map, in the two-dimensional matrix of the range dimension and the Doppler dimension, set the power greater than the noise intensity threshold of the power map to a first value, and set the power not greater than the noise intensity threshold of the power map to a second value to obtain the CFAR two-dimensional mask of the radar signal corresponding to the power map, where the area corresponding to the first value contains objects, and the area corresponding to the second value does not contain objects.

[0145] In a possible implementation manner, the above motion estimation unit is specifically configured to: perform connected component analysis on each of the above CFAR two-dimensional masks to obtain the target information of each object in each of the above CFAR two-dimensional masks, where, for any object, the target information of the object includes the object width, object height, and object center coordinates of the object; according to the time sequence of each frame of radar signal and the target information of each object, perform trajectory association on the same objects in the CFAR two-dimensional masks of each frame of radar signal to respectively obtain the motion trajectories of each object; respectively determine the estimated motion speed and estimated motion direction of each object according to the motion trajectories of each object and the time difference between each frame of radar signal.

[0146] In a possible implementation manner, the above compensation mode rejection unit is specifically configured to: for each object, obtain the movement direction and movement speed of the object under each compensation mode in the preset MIMO compensation mode; for each object, in the preset MIMO compensation mode, select the compensation mode whose movement direction is the same as the estimated movement direction of the object to obtain the filtered compensation mode of the object; for each object, in the filtered compensation mode of the object, select the compensation mode whose error between the movement speed and the estimated movement speed of the object is within the preset range to obtain the preprocessed MIMO compensation mode of the object.

[0147] An embodiment of the present application further provides an electronic device, including: a processor and a memory;

[0148] The above memory is used to store a computer program;

[0149] When the above processor is used to execute the computer program stored in the above memory, the above any speed deblurring method is implemented.

[0150] Optionally, referring to Figure 9 , in addition to the above processor 21 and memory 23, the electronic device of the embodiment of the present application further includes a communication interface 22 and a communication bus 24. Among them, the processor 21, the communication interface 22, and the memory 23 complete mutual communication through the communication bus 24. Specifically, the electronic device in the embodiment of the present application may be a MIMO radar.

[0151] The communication bus mentioned in the above electronic device may be a PCI (Peripheral Component Interconnect, peripheral component interconnect standard) bus or an EISA (Extended Industry Standard Architecture, extended industry standard structure) bus, etc. This communication bus can be divided into an address bus, a data bus, a control bus, etc. For the convenience of representation, only a thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0152] The communication interface is used for communication between the above electronic device and other devices.

[0153] The memory may include a RAM (Random Access Memory, random access memory), or may also include an NVM (Non-Volatile Memory, non-volatile memory), such as at least one disk memory. Optionally, the memory may also be at least one storage device located far from the aforementioned processor.

[0154] The above-mentioned processor may be a general-purpose processor, including a CPU (Central Processing Unit), an NP (Network Processor), etc.; it may also be a DSP (Digital Signal Processing), an ASIC (Application Specific Integrated Circuit), an FPGA (Field-Programmable Gate Array), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0155] The embodiment of the present application also provides a computer-readable storage medium, in which a computer program is stored, and when the computer program is executed by a processor, the above-mentioned any speed ambiguity resolution method is implemented.

[0156] In another embodiment provided by the present application, a computer program product containing instructions is also provided. When it runs on a computer, the computer is enabled to execute the above-mentioned any speed ambiguity resolution method.

[0157] In the above embodiment, it can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer may be a general-purpose computer, a dedicated computer, a computer network, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire (such as coaxial cable, optical fiber, digital subscriber line) or wirelessly (such as infrared, wireless, microwave, etc.). The computer-readable storage medium may be any available medium that can be accessed by a computer, or a data storage device such as a server or data center that includes one or more integrated available media. The available medium may be a magnetic medium (for example, a floppy disk, a hard disk, a magnetic tape), an optical medium (for example, a DVD), or a semiconductor medium (for example, a solid-state disk (SSD)).

[0158] It should be noted that in this text, the technical features in each alternative solution can be combined as long as they are not contradictory to form a solution, and these solutions are all within the scope disclosed in this application. Relative terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising one..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the said element.

[0159] Each embodiment in this specification is described in a related manner. Each embodiment focuses on the differences from other embodiments, and the same or similar parts between the embodiments can be referred to each other.

[0160] The above description is only the preferred embodiment of this application and is not used to limit the protection scope of this application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of this application are all included in the protection scope of this application.

Claims

1. A method for velocity ambiguity resolution, characterized in that, The method includes: Obtaining multiple frames of radar signals and respectively determining the power maps of each frame of radar signals; Respectively mapping each of the power maps into a two-dimensional matrix in the range dimension and the Doppler dimension to obtain the constant false alarm rate (CFAR) two-dimensional masks of each frame of radar signals; According to the time sequence of each frame of radar signals, performing trajectory association on the same objects in the CFAR two-dimensional masks of each frame of radar signals, and respectively obtaining the estimated motion speed and the estimated motion direction of each object; For each object, selecting a preprocessing MIMO compensation mode for the object from a preset MIMO compensation mode according to the estimated motion speed and the estimated motion direction of the object; For each object, traversing each MIMO compensation mode in the preprocessing MIMO compensation mode of the object to obtain a target MIMO compensation mode, and obtaining the velocity ambiguity resolution result of the object under the target MIMO compensation mode, where the velocity ambiguity resolution result of the object includes the true speed and the true azimuth of the object; Among them, for each object, traversing each MIMO compensation mode in the preprocessing MIMO compensation mode of the object to obtain a target MIMO compensation mode, including: For each object, compensating and correcting the virtual array vector by using each MIMO compensation mode in the preprocessing MIMO compensation mode of the object to obtain the corrected virtual array vector under the MIMO compensation mode, performing Fourier transform on the corrected virtual array vector to obtain the virtual array spectrum under the MIMO compensation mode, and determining the target MIMO compensation mode according to the virtual array spectra under each MIMO compensation mode in the preprocessing MIMO compensation mode of the object; 2. The method according to claim 1, characterized in that, The obtaining multiple frames of radar signals and respectively determining the power maps of each frame of radar signals includes: Obtaining the analog-to-digital converter (ADC) data of each channel of the radar virtual antenna array to obtain multiple frames of radar signals; Respectively performing two-dimensional fast Fourier transform on the ADC data of each channel in each frame of radar signals to obtain the virtual array vectors of each frame of radar signals; Respectively performing non-coherent accumulation on the virtual array vectors of each frame of radar signals to obtain the power maps of each frame of radar signals; 3. The method according to claim 1, wherein The respectively mapping each of the power maps into a two-dimensional matrix in the range dimension and the Doppler dimension to obtain the constant false alarm rate (CFAR) two-dimensional masks of each frame of radar signals includes: For each power map, performing CFAR detection on the power map to obtain the noise intensity threshold of the power map; For each power map, in the two-dimensional matrix in the range dimension and the Doppler dimension, setting the power greater than the noise intensity threshold of the power map to a first value, and setting the power not greater than the noise intensity threshold of the power map to a second value to obtain the CFAR two-dimensional mask of the radar signal corresponding to the power map, where the area corresponding to the first value contains objects, and the area corresponding to the second value does not contain objects; 4. The method according to claim 1, characterized in that, The performing trajectory association on the same objects in the CFAR two-dimensional masks of each frame of radar signals according to the time sequence of each frame of radar signals and respectively obtaining the estimated motion speed and the estimated motion direction of each object includes: Perform connected component analysis on each of the CFAR two-dimensional masks to obtain the target information of each object in each of the CFAR two-dimensional masks. Among them, for any object, the target information of the object includes the object width, object height, and object center coordinates of the object; According to the time sequence of each frame of radar signal and the target information of each object, perform trajectory association on the same objects in the CFAR two-dimensional masks of each frame of radar signal to obtain the motion trajectories of each object respectively; Determine the estimated motion speed and estimated motion direction of each object respectively at least according to the motion trajectories of each object and the time difference between each frame of radar signal.

5. The method according to claim 1, characterized in that, For each object, selecting the preprocessing MIMO compensation mode of the object in the preset MIMO compensation mode according to the estimated motion speed and estimated motion direction of the object includes: For each object, obtain the motion direction and motion speed of the object under each compensation mode in the preset MIMO compensation mode; For each object, in the preset MIMO compensation mode, select the compensation mode with the same motion direction as the estimated motion direction of the object to obtain the filtered compensation mode of the object; For each object, in the filtered compensation mode of the object, select the compensation mode with the error between the motion speed and the estimated motion speed of the object within the preset range to obtain the preprocessing MIMO compensation mode of the object.

6. A speed ambiguity resolution device, characterized in that, The device includes: A power map acquisition unit for acquiring multiple frames of radar signals and respectively determining the power maps of each frame of radar signal; A CFAR detection unit for respectively mapping each of the power maps into a two-dimensional matrix in the range dimension and the Doppler dimension to obtain the CFAR two-dimensional masks of each frame of radar signal; A motion estimation unit for performing trajectory association on the same objects in the CFAR two-dimensional masks of each frame of radar signal according to the time sequence of each frame of radar signal to obtain the estimated motion speed and estimated motion direction of each object respectively; A compensation mode elimination unit for, for each object, selecting the preprocessing MIMO compensation mode of the object in the preset MIMO compensation mode according to the estimated motion speed and estimated motion direction of the object; A velocity ambiguity resolution unit for, for each object, traversing each MIMO compensation mode in the preprocessing MIMO compensation mode of the object to obtain a target MIMO compensation mode, and obtaining the velocity ambiguity resolution result of the object under the target MIMO compensation mode. Among them, the velocity ambiguity resolution result of the object includes the true speed and true azimuth of the object; Among them, the velocity ambiguity resolution unit is specifically configured to, for each object, use each MIMO compensation mode in the preprocessing MIMO compensation mode of the object to perform compensation and correction on the virtual array vector to obtain the corrected virtual array vector under the MIMO compensation mode, perform Fourier transform on the corrected virtual array vector to obtain the virtual array spectrum under the MIMO compensation mode, and determine the target MIMO compensation mode according to the virtual array spectra under each MIMO compensation mode in the preprocessing MIMO compensation mode of the object.

7. The device according to claim 6, characterized in that, The power map acquisition unit is specifically configured to: acquire the analog-to-digital converter (ADC) data of each channel of the radar virtual antenna array to obtain multiple frames of radar signals; perform two-dimensional fast Fourier transform on the ADC data of each channel in each frame of radar signals to obtain the virtual array vectors of each frame of radar signals; perform non-coherent accumulation on the virtual array vectors of each frame of radar signals to obtain the power maps of each frame of radar signals.

8. The device according to claim 6, characterized in that, The CFAR detection unit is specifically configured to: for each power map, perform CFAR detection on the power map to obtain the noise intensity threshold of the power map; for each power map, in the two-dimensional matrix in the range dimension and the Doppler dimension, set the power greater than the noise intensity threshold of the power map to a first value, and set the power not greater than the noise intensity threshold of the power map to a second value, to obtain the CFAR two-dimensional mask of the radar signal corresponding to the power map, where the area corresponding to the first value contains objects, and the area corresponding to the second value does not contain objects.

9. The device according to claim 6, characterized in that The motion estimation unit is specifically configured to: perform connected component analysis on each of the CFAR two-dimensional masks to obtain the target information of each object in each of the CFAR two-dimensional masks, where, for any object, the target information of the object includes the object width, object height, and object center coordinates of the object; according to the time sequence of each frame of radar signals and the target information of each object, perform trajectory association on the same objects in the CFAR two-dimensional masks of each frame of radar signals to obtain the motion trajectories of each object; determine the estimated motion speed and estimated motion direction of each object respectively at least according to the motion trajectories of each object and the time difference between each frame of radar signals.

10. The device according to claim 6, characterized in that, The compensation mode rejection unit is specifically configured to: for each object, acquire the motion direction and motion speed of the object under each compensation mode in the preset MIMO compensation mode; for each object, in the preset MIMO compensation mode, select the compensation mode with the same motion direction as the estimated motion direction of the object to obtain the filtered compensation mode of the object; For each object, in the filtered compensation mode of the object, select the compensation mode with the error between the motion speed and the estimated motion speed of the object within the preset range to obtain the preprocessed MIMO compensation mode of the object.

11. An electronic device, characterized in that, It includes a processor and a memory; The memory is used to store computer programs; When the processor is used to execute the program stored in the memory, it implements the method according to any one of claims 1-5.

12. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, it implements the method according to any one of claims 1-5.

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