Speed ​​defuzzification method, device, electronic device and storage medium

Abstract

Embodiments of the present application provide a speed deambiguation method, apparatus, electronic device, and storage medium, relating to the field of radar signal processing technology. The method determines the speed range of historical targets in a distance segment. Based on the speed range of historical targets in the distance segment, a preprocessing compensation mode set for the distance segment can be obtained. According to the preprocessing compensation mode set of the distance segment where the target to be detected is located, it can be effectively determined whether the current optimal compensation mode of the target to be detected is correct. If the optimal compensation mode of the target to be detected is one of the preprocessing compensation mode sets in the distance segment, it indicates that the speed of the optimal compensation mode is credible. Otherwise, the optimal compensation mode is reselected for correction, thereby realizing speed deambiguation for TDM-MIMO radar and improving the accuracy of speed deambiguation.

Description

Technical Field

[0001] The present application relates to the field of radar signal processing technology, and in particular to a velocity deambiguation method, device, electronic device, and storage medium. Background Art

[0002] Current millimeter-wave radar antennas generally employ a TDM (Time-Division Multiplexing)-MIMO (Multiple In Multiple Out) format. This approach effectively reduces the antenna's physical size through the use of virtual array elements, resulting in high-resolution target angle measurement results similar to those achieved with larger antennas. Consider a TDM-MIMO radar consisting of M transmitting antennas and N receiving antennas. By properly designing the spacing between transmitting and receiving antennas, a 1-transmit, M*N-receive effect can be achieved. Assume that the spacing between adjacent transmitting antennas is D, and the spacing between adjacent receiving antennas is d. To prevent antenna grating lobes, a requirement of d ≤ 0.5λ is generally met, where λ is the radar wavelength. To maximize antenna aperture utilization, the design typically requires D = Nd.

[0003] Taking the FMCW (Frequency Modulated Continuous Wave) signal system with 2 transmitting antennas and 4 receiving antennas as an example, where d = 0.5λ and D = 2λ, the resulting TDM-MIMO radar virtual array element diagram is shown below: Figure 1 As shown in the figure, "virtual antenna" represents a virtual antenna and "real antenna" represents a real antenna. Because TDM-MIMO radar's multiple transmit antennas operate by alternating signal transmission, two issues arise: First, the phase variation caused by the Doppler frequency of a moving target during the switching time between different transmit antennas is coupled to each receive antenna, affecting the correct synthesis of the receive antenna aperture. Second, TDM itself reduces the sampling rate during slow time, significantly reducing the unambiguous velocity measurement range. Furthermore, once velocity ambiguity occurs, it can lead to deviations in angle measurements. Therefore, finding a velocity deambiguation method for TDM-MIMO radars is an urgent problem to be solved. Summary of the Invention

[0004] The purpose of the embodiments of the present application is to provide a velocity deambiguation method, apparatus, electronic device, and storage medium to implement velocity deambiguation for TDM-MIMO radar. The specific technical solution is as follows:

[0005] In the first aspect, an embodiment of the present application provides a speed defuzzification method, which includes: obtaining channel information of each channel, dividing each channel into multiple distance segments according to the channel information of each channel; determining the speed range of historical targets in each distance segment for each channel; determining a preset MIMO compensation mode for each distance segment whose speed intersects with the speed range of the distance segment, and obtaining a set of pre-processing compensation modes for the distance segment; selecting a preset MIMO compensation mode that meets a preset optimal principle from among the preset MIMO compensation modes of the target to be detected that have not been selected, and obtaining the currently selected optimal compensation mode; according to the currently selected The clustering result of the optimal compensation mode is used to determine that the distance segment where the target to be detected is located is the target distance segment, wherein the clustering result includes the position information of the target to be detected; it is judged whether the optimal compensation mode currently selected by the target to be detected is one of the preprocessing compensation mode set of the target distance segment; if the optimal compensation mode currently selected by the target to be detected is one of the preprocessing compensation mode set of the target distance segment, then the speed deambiguation result of the target to be detected under the optimal compensation mode currently selected is determined, wherein the speed deambiguation result of the target to be detected includes the true speed and true direction of the target to be detected.

[0006] In one possible embodiment, after determining whether the optimal compensation mode currently selected for the target to be detected is one of the preprocessing compensation mode set for the target distance segment, the method further includes: if the optimal compensation mode of the target to be detected is not one of the preprocessing compensation mode set for the target distance segment, returning to the execution step: selecting a preset MIMO compensation mode that meets a preset optimal principle from each preset MIMO compensation mode that has not been selected for the target to be detected, to obtain an optimal compensation mode.

[0007] In one possible implementation, before selecting a preset MIMO compensation mode that satisfies a preset optimal principle from among the preset MIMO compensation modes that have not been selected for the target to be detected and obtaining the currently selected optimal compensation mode, the method further includes: obtaining a power map of multiple frames of radar signals, mapping each of the power maps into a two-dimensional matrix of a range dimension and a Doppler dimension, and obtaining a constant false alarm rate (CFAR) two-dimensional mask of the radar signal of each frame; and performing trajectory association on the target to be detected in the CFAR two-dimensional mask of the radar signal of each frame according to the time sequence of the radar signal of each frame to obtain the target to be detected. The estimated movement speed and estimated movement direction of the target; selecting the preprocessing MIMO compensation mode of the target to be detected from the preset MIMO compensation modes according to the estimated movement speed and estimated movement direction of the target to be detected; selecting the preset MIMO compensation mode that meets the preset optimal principle from the various preprocessing MIMO compensation modes of the target to be detected that have not been selected, and obtaining the currently selected optimal compensation mode, including: selecting the preprocessing MIMO compensation mode that meets the preset optimal principle from the various preprocessing MIMO compensation modes of the target to be detected that have not been selected, and obtaining the currently selected optimal compensation mode.

[0008] In a possible implementation, selecting a preprocessing MIMO compensation mode that satisfies a preset optimal principle from among the preprocessing MIMO compensation modes of the target to be detected that have not been selected to obtain a currently selected optimal compensation mode includes: selecting a preprocessing MIMO compensation mode with the largest average value of array spectrum peak values ​​from among the preprocessing MIMO compensation modes of the target to be detected that have not been selected to obtain the currently selected optimal compensation mode.

[0009] In one possible embodiment, before determining that the distance segment in which the target to be detected is located is the target distance segment based on the clustering result of the currently selected optimal compensation mode, the method further includes: acquiring a radar signal and determining a power map of the radar signal; performing CFAR detection on the power map to obtain the position of each target point in the power map; obtaining each preset MIMO compensation mode and the direction of arrival of each target point under each preset MIMO compensation mode based on the position of each target point in the power map; and for each preset MIMO compensation mode, clustering each target point under the preset MIMO compensation mode based on the direction of arrival of each target point under the preset MIMO compensation mode to obtain a clustering result of the target to be detected under the preset MIMO compensation mode.

[0010] In one possible implementation, determining the speed range of historical targets within each distance segment includes: tracking each historical target based on a preset target tracking algorithm according to a speed defuzzification result of each historical target in the historical data to obtain a trajectory of each historical target; for each distance segment, calculating the movement speed of each historical target within the distance segment according to the trajectory of each historical target; and for each distance segment, determining the speed range of the historical targets within the distance segment according to the movement speed of each historical target within the distance segment.

[0011] In one possible implementation, for each distance segment, the movement speed of each historical target in the distance segment is calculated based on the trajectory of each historical target, including: for each distance segment, the movement speed of each historical target in the distance segment is calculated based on the trajectory of each historical target in the m-frame radar signal before the current frame radar signal, where m is a preset integer.

[0012] In the second aspect, an embodiment of the present application provides a speed defuzzification device, which includes: a distance segment division unit, which is used to obtain channel information of each channel and divide each channel into multiple distance segments according to the channel information of each channel; a speed range determination unit, which is used to determine the speed range of historical targets in each distance segment for each channel; a mode set determination unit, which is used to determine, for each distance segment, a preset MIMO compensation mode whose speed intersects with the speed range of the distance segment, and obtain a pre-processing compensation mode set for the distance segment; an optimal compensation mode selection unit, which is used to select a preset MIMO compensation mode that meets a preset optimal principle from various preset MIMO compensation modes that have not been selected for the target to be detected, and obtain the currently selected optimal compensation mode ; A target distance segment determination unit, used to determine, based on the clustering result of the currently selected optimal compensation mode, that the distance segment in which the target to be detected is located is a target distance segment, wherein the clustering result includes the position information of the target to be detected; a compensation mode detection unit, used to determine whether the optimal compensation mode currently selected for the target to be detected is one of the preprocessing compensation mode set of the target distance segment; a first execution unit, used to determine the speed deambiguation result of the target to be detected under the currently selected optimal compensation mode if the currently selected optimal compensation mode for the target to be detected is one of the preprocessing compensation mode set of the target distance segment, wherein the speed deambiguation result of the target to be detected includes the true speed and true direction of the target to be detected.

[0013] In a possible implementation, the device further includes: a second execution unit, configured to return to executing the optimal compensation mode selection unit if the optimal compensation mode of the target to be detected is not one of the pre-processing compensation mode set of the target distance segment.

[0014] In one possible embodiment, the device further includes: a pre-processing MIMO compensation mode determination unit, configured to obtain power maps of multiple frames of radar signals, map each of the power maps into a two-dimensional matrix of a range dimension and a Doppler dimension, and obtain a constant false alarm rate (CFAR) two-dimensional mask of the radar signal of each frame; perform trajectory association on the target to be detected in the CFAR two-dimensional mask of the radar signal of each frame according to the time sequence of the radar signal of each frame to obtain an estimated motion speed and an estimated motion direction of the target to be detected; select a pre-processing MIMO compensation mode for the target to be detected from a preset MIMO compensation mode based on the estimated motion speed and the estimated motion direction of the target to be detected; and the optimal compensation mode selection unit is specifically configured to select a pre-processing MIMO compensation mode that satisfies a preset optimal principle from each unselected pre-processing MIMO compensation mode of the target to be detected to obtain a currently selected optimal compensation mode.

[0015] In a possible implementation, the optimal compensation mode selection unit is specifically configured to select, from among the unselected preprocessing MIMO compensation modes of the target to be detected, a preprocessing MIMO compensation mode having the largest average value of array spectrum peaks to obtain a currently selected optimal compensation mode.

[0016] In one possible embodiment, the device also includes: a power graph determination unit, used to obtain a radar signal and determine the power graph of the radar signal; a CFAR detection unit, used to perform CFAR detection on the power graph to obtain the position of each target point in the power graph; a DOA detection unit, used to obtain each preset MIMO compensation mode and the direction of arrival of each target point under each preset MIMO compensation mode based on the position of each target point in the power graph; and a clustering unit, used to cluster each target point under each preset MIMO compensation mode according to the direction of arrival of each target point under the preset MIMO compensation mode, to obtain a clustering result of the target to be detected under the preset MIMO compensation mode.

[0017] In one possible embodiment, the speed range determination unit includes: a target trajectory acquisition subunit, which is used to track each historical target based on a preset target tracking algorithm according to the speed defuzzification result of each historical target in the historical data to obtain the trajectory of each historical target; a motion speed determination subunit, which is used to calculate the motion speed of each historical target in each distance segment according to the trajectory of each historical target; and a speed range determination subunit, which is used to determine the speed range of the historical targets in each distance segment according to the motion speed of each historical target in the distance segment.

[0018] In one possible implementation, the motion speed determination subunit is specifically used to: for each distance segment, calculate the motion speed of each historical target in the distance segment based on the trajectory of each historical target in the m-frame radar signal before the current frame radar signal, where m is a preset integer.

[0019] In a third aspect, an embodiment of the present application provides an electronic device, including a processor and a memory;

[0020] The memory is used to store computer programs;

[0021] The processor is configured to implement any of the velocity defuzzification methods described in this application when executing the program stored in the memory.

[0022] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements any of the velocity defuzzification methods described in the present application.

[0023] Beneficial effects of the embodiments of the present application:

[0024] The speed deambiguation method, device, electronic device, and storage medium provided in the embodiments of the present application determine the speed range of historical targets in a distance segment. Based on the speed range of historical targets in the distance segment, a pre-processing compensation mode set for the distance segment can be obtained. According to the pre-processing compensation mode set of the distance segment where the target to be detected is located, it can be effectively determined whether the current optimal compensation mode of the target to be detected is correct. If the optimal compensation mode of the target to be detected is one of the pre-processing compensation mode sets in the distance segment, it means that the speed of the optimal compensation mode is credible, thereby realizing speed deambiguation for TDM-MIMO radar and improving the accuracy of speed deambiguation. Of course, implementing any product or method of the present application does not necessarily require achieving all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0026] Figure 1 A schematic diagram of a virtual array element of a MIMO radar in the related art;

[0027] Figure 2 A schematic diagram of the operation process of the speed defuzzification system according to an embodiment of the present application;

[0028] Figure 3 This is a first schematic diagram of the velocity defuzzification method according to an embodiment of the present application;

[0029] Figure 4 This is a second schematic diagram of the velocity defuzzification method according to an embodiment of the present application;

[0030] Figure 5 A schematic diagram of a possible implementation of step S102 in an embodiment of the present application;

[0031] Figure 6 This is a third schematic diagram of the velocity defuzzification method according to an embodiment of the present application;

[0032] Figure 7 This is a fourth schematic diagram of the velocity defuzzification method according to an embodiment of the present application;

[0033] Figure 8 A schematic diagram of a CFAR two-dimensional mask according to an embodiment of the present application;

[0034] Figure 9 A schematic diagram of target trajectory association according to an embodiment of the present application;

[0035] Figure 10 A schematic diagram of a speed defuzzification device according to an embodiment of the present application;

[0036] Figure 11 A schematic diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0037] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0038] First, the terms used in this application are explained.

[0039] Radar: An electronic device that uses electromagnetic waves to detect targets. Radar transmits electromagnetic waves to the target and receives the echoes, thereby obtaining information such as the distance from the target to the point of emission, the rate of change of distance (radial velocity), direction, and altitude.

[0040] MIMO (Multiple In Multiple Out) radar: MIMO radar is a technology that improves radar angle estimation capabilities. It is primarily implemented in FDM (Frequency Division Multiplexing), CDM (Code Division Multiplexing), and TDM (Time Division Multiplexing). Due to semiconductor device cost constraints and implementation complexity, millimeter-wave radar currently primarily uses TDM-MIMO technology. All references to MIMO in this application refer to TDM-MIMO unless otherwise specified.

[0041] Speed ​​ambiguity: Spectral aliasing causes confusion in the measured target speed, making it difficult to discern the target's true speed. This is especially true for TDM-MIMO radar, where the reduced sampling rate during slow time significantly reduces the unambiguous speed measurement range, making speed ambiguity more likely to occur.

[0042] Current millimeter-wave radar antennas generally employ a TDM (Time-Division Multiplexing)-MIMO (Multiple In Multiple Out) format. This approach effectively reduces the antenna's physical size through the use of virtual array elements, resulting in high-resolution target angle measurement results similar to those achieved with larger antennas. Consider a TDM-MIMO radar consisting of M transmitting antennas and N receiving antennas. By properly designing the spacing between transmitting and receiving antennas, a 1-transmit, M*N-receive effect can be achieved. Assume that the spacing between adjacent transmitting antennas is D, and the spacing between adjacent receiving antennas is d. To prevent antenna grating lobes, a requirement of d ≤ 0.5λ is generally met, where λ is the radar wavelength. To maximize antenna aperture utilization, the design typically requires D = Nd.

[0043] Taking the FMCW (Frequency Modulated Continuous Wave) signal system with 2 transmitting antennas and 4 receiving antennas as an example, where d = 0.5λ and D = 2λ, the resulting TDM-MIMO radar virtual array element diagram is shown below: Figure 1As shown in the figure. Because TDM-MIMO radar uses multiple transmit antennas to transmit signals alternately, two problems arise: First, the phase variation caused by the Doppler frequency of a moving target during the switching time between different transmit antennas is coupled to each receive antenna, affecting the correct synthesis of the receive antenna aperture. Second, TDM itself reduces the sampling rate during slow times, significantly reducing the unambiguous velocity measurement range. Furthermore, once velocity ambiguity occurs, it will further cause deviations in angle measurements. Therefore, existing TDM-MIMO radars have high measurement errors.

[0044] In related technologies, in order to increase the accuracy of TDM-MIMO radar, the following compensation method is used for velocity deambiguation:

[0045] Step 1: Estimate the velocity-induced phase shift of the virtual element vector S of the signal velocity-induced phase shift The target's true speed v true The power map obtained based on the power map estimates the speed v est There may be velocity ambiguity, so the target's true velocity v true The value is unknown, but the possible value set can be obtained by estimating the speed from the power diagram, v true ∈{v est ,v est +2v max ,v est -2v max}, where v max is the maximum unambiguous measured speed. It should be emphasized that the relationship between the true speed and the power diagram estimated speed is v true =v est +2xv max ,x∈R, usually for actual application scenarios, the value of x is {0,1,-1}, which can meet the requirements. In addition, the value of x in the MIMO algorithm is also limited by the transmitting antenna.

[0046] Different speed values ​​v est ,v est +2v max ,v est -2v max The obtained velocity-induced phase shifts are called compensation mode 0, compensation mode 1 and compensation mode 2, and their values ​​are and

[0047] In step 1, the calculation formula relationship between each symbol is as follows:

[0048] Estimated speed v est : Directly calculated based on the power diagram.

[0049] True speed v true With the estimated speed v est relation:

[0050] v true ∈{v est ,v est +2v max ,v est -2v max}

[0051] velocity-induced phase shift

[0052]

[0053] Maximum speed v max :

[0054]

[0055] Where λ is the radar wavelength, T c is a chirp cycle. Figure 1 As shown, in the case of 2 transmitting antennas, T c =2T.

[0056] Step 2: Use 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 vector S is obtained. c0 , S c1 and S c2 .

[0057] In step 2, the calculation formula relationship between each symbol is as follows:

[0058] Virtual array vector S:

[0059]

[0060] Corrected virtual array vector S c :

[0061]

[0062] like Correction is correct, S c The formula is as follows:

[0063]

[0064] in, is the phase caused by the path difference between adjacent receiving antennas, θ is the target azimuth, d is the distance between adjacent receiving antennas, and λ is the radar wavelength. It is the phase change caused by the target Doppler frequency during the switching time of adjacent transmitting antennas. mn The discrete digital signal of the echo signal transmitted by the mth transmitting antenna and received by the nth receiving antenna is transformed by a two-dimensional Fast Fourier Transform (FFT) to obtain a signal.

[0065] Step 3: Correct the virtual array vector S c Perform the first Fourier transform to generate the corrected virtual array spectrum P c Different compensation modes give the corresponding virtual array spectrum P c0 , P c1 and P c2 .

[0066] Step 4: Obtain the optimal compensation mode based on the corrected virtual array spectrum. In theory, the compensation mode corresponding to the array spectrum with the highest peak value is the optimal compensation mode.

[0067] Step 5: Obtain the target speed and direction. The speed and direction obtained by the optimal compensation mode are the final target speed and direction.

[0068] However, due to various factors such as noise disturbances and hardware errors, the aforementioned algorithm suffers from a certain error rate (approximately 10%) when performing velocity deambiguation. This incorrect compensation mode results in abnormal velocity and orientation in the output target point cloud. Furthermore, the TDM-MIMO compensation modes for the same target at different times (frames) vary independently, resulting in a constant change in the target's velocity over time and significant fluctuations in its orientation. This, in turn, affects target tracking, leading to a decrease in both detection and false detection performance, particularly in congested scenarios.

[0069] The inventors found in their research that due to the disturbance in the data itself, there is a certain probability of error when using the above algorithm. However, with the help of target scene information, this problem can be alleviated or even eliminated. Specifically, based on self-learning lanes or configured lane road condition information, the target speed range can be given by lane and section, thereby excluding some MIMO compensation phase modes to increase the correct probability of MIMO deambiguation. In view of this, an embodiment of the present application provides a speed deambiguation system, including: a power graph acquisition module, a CFAR (Constant False-Alarm Rate) detection module, a DOA (Direction Of Arrival) estimation module, a clustering module, a lane condition acquisition module, a MIMO correction module and a tracking module.

[0070] like Figure 2 As shown in the figure, the input of the entire system is the radar's ADC (Analog to Digital Converter) data, and the output is the final target trajectory list information. This system is also a common radar target detection and tracking algorithm framework. The following is a detailed description of each module of the velocity defuzzification system.

[0071] Power map acquisition module: The input is ADC data, and the output is the radar power map. The ADC data of each channel of the virtual antenna array is subjected to a two-dimensional Fast Fourier Transform (FFT), and then non-coherently accumulated to obtain the power map.

[0072] CFAR detection module: The input is a power map, and the output is a set of detected target points. After processing the noise in the input power map, a threshold is determined and compared with each signal point in the power map. If a signal point exceeds the threshold, it is determined to be a target; otherwise, it is determined to be absent.

[0073] The DOA estimation module extracts the data from each channel of the virtual antenna array's two-dimensional FFT and performs one-dimensional FFT processing on the target points detected by the CFAR detection module to obtain the direction of arrival of the target points under each compensation mode. Unlike related art methods that only output information such as the speed, azimuth, and array spectrum peak corresponding to the optimal compensation mode, the DOA estimation module in this embodiment outputs information such as the estimated speed, azimuth, and array spectrum peak of the target points under all compensation modes.

[0074] Clustering Module: Utilizing a relevant clustering algorithm, the target point cloud is aggregated and outputs information such as the target's velocity, azimuth, and array spectrum peak for each compensation mode. Radar signals striking different parts of the target will produce slightly different speed, range, and azimuth of the returned target points. A target is composed of multiple points. The DOA estimation module outputs information such as the direction of arrival (DOA) of the target point. The clustering module clusters these points into targets and outputs information such as the velocity, azimuth, and range of the target as a whole. Specifically, the clustering module clusters the same target under different compensation modes to obtain information such as the target's velocity and azimuth under different compensation modes. For example, a compensation module includes compensation modes 0, 1, and n. The point cloud of a given target is first clustered according to compensation mode 0, then clustered according to mode 1, until all compensation modes are exhausted. In addition to obtaining target clustering results for the optimal compensation mode, target clustering results for all remaining compensation modes are also obtained as a candidate set for MIMO correction. In one example, the target clustering result in the optimal compensation mode is used as the default output. If the MIMO correction module determines that the target clustering result is incorrect, the optimal compensation result is corrected using the output information of the remaining compensation modes.

[0075] Lane status acquisition module: Lane information can be configured by the user or acquired through self-learning. Lane information may include lane location, width, etc. Based on lane information, the steps for acquiring lane status are as follows:

[0076] Step 1: For each lane, divide it into non-overlapping distance segments, for example, each lane is divided into a distance segment of 10 meters.

[0077] Step 2: Based on the target tracking trajectory, historical target speed range information for each lane and distance segment is obtained. This information is accumulated over time, for example, by accumulating K frames of data for self-learning statistics. The value of K depends on the application scenario, such as the duration of traffic lights at intersections, road type, and traffic volume. Generally, the faster the target speed in the application scenario, the larger the value of K. Speed ​​range information can also be updated in real time using a sliding window of length K to adapt to changing road conditions. Maximum and minimum target speed values ​​can be obtained for each lane and distance segment.

[0078] Step 3: According to the speed range of each distance segment of each lane, determine the compensation modes with intersection between the speed and speed range, and obtain the pre-processing compensation mode set. ij Lane represents the jth distance segment of the i-th lane. ij The preprocessing compensation mode under DPC_LANE ij ={DPC0,DPC1}.

[0079] MIMO Correction Module: The input of this module is the lane condition and target clustering results, and the output is the corrected target clustering results. The MIMO Correction Module traverses all targets in turn, and the processing flow for each target is as follows:

[0080] Obtaining target road section information: Based on the coordinate information corresponding to the optimal mode, obtain the target lane and lane distance segment Lane ij .

[0081] Target information and scene consistency comparison: If the optimal compensation mode of the current target is Lane ij Preprocessing compensation mode set DPC_LANE ij If one of the compensation modes is not found, no correction is required; otherwise, the optimal compensation mode is selected again from the remaining compensation modes. The following two conditions must be met for the reselection:

[0082] (1) DPC_LANE ij one of the.

[0083] (2) If there are multiple alternative compensation modes that meet condition (1), they are selected according to the optimal principle.

[0084] The "optimal principle" in this application can adopt the optimal compensation mode selection method in the related art. In one example, the array spectrum peak value of the cluster point can be used for judgment, and the array spectrum peak value under each alternative compensation mode is averaged (in accordance with Pow avg Indicates), compare the Pow in clustering results under different compensation modes avg size.

[0085] Target information correction: Based on the optimal compensation mode selected again, the speed and direction of the clustered target are corrected.

[0086] For example, the possible compensation mode of the target is DPC_ALL = {DPC0, DPC1, DPC2}, the optimal compensation mode of the target is DPC2, and the pre-processing compensation mode of the range segment is DPC_LANE ij = {DPC0, DPC1}, which is clearly mismatched. The optimal compensation mode is then selected from the candidate compensation modes {DPC0, DPC1}. Both DPC0 and DPC1 are preprocessing compensation modes. The "optimality principle" finds that DPC0 has a higher probability, so the clustering information corresponding to DPC0 is used as the target output information.

[0087] Tracking module: This module uses relevant tracking algorithms to track the target. It generally includes two major functions: first, track initiation, which generates the initial track and then performs tracking after confirmation; second, track maintenance, which includes track updates, extrapolation, and extinction processing.

[0088] In the embodiment of the present application, the lane and distance segments are divided into self-learning lane conditions, and the lane conditions are used to eliminate some compensation modes, thereby increasing the accuracy probability of speed ambiguity resolution.

[0089] The present application also provides a method for defuzzifying speed. Figure 3 , the method comprising:

[0090] S101 , acquiring channel information of each channel, and dividing each channel into a plurality of distance segments according to the channel information of each channel.

[0091] The speed deambiguation method of the embodiments of the present application can be implemented by an electronic device, which can be a radar device, such as a traffic radar device, or a device with computing capabilities connected to the radar device. The channels in the present application can include motor vehicle lanes, non-motor vehicle lanes, ship channels, river channels, and aircraft channels, all of which are within the scope of protection of the present application.

[0092] The channel information may include the location, width, etc. of the channel. The channel information may be manually input or obtained through a related intelligent learning algorithm. For example, the channel information of each channel may be obtained by performing target detection on the image data collected by the camera.

[0093] The length of each distance segment can be the same or different, and can be customized based on actual conditions. In one example, the length of a distance segment is related to the target's possible speed; the faster the target's speed, the longer the distance segment can be. For example, the distance segment for an airplane's flight path is longer than the distance segment for a car's lane. In this application, a target is a target detected by the radar, such as a vehicle, pedestrian, ship, or aircraft.

[0094] S102: For each channel, determine the speed range of the historical target within each distance segment.

[0095] The preset target tracking algorithm can be any relevant tracking algorithm, and each historical target can be tracked separately based on the speed defuzzification result of each historical target, so as to calculate the maximum speed and minimum speed of the historical target in each distance segment, thereby obtaining the speed range of the target in each distance segment.

[0096] S103 : For each distance segment, determine a preset MIMO compensation mode whose speed intersects with the speed range of the distance segment, and obtain a pre-processing compensation mode set for the distance segment.

[0097] Each preset MIMO compensation mode corresponds to a specific speed range. For a specific target, the target's fuzzy speed can be calculated based on its power map, and the target's true speed can be determined based on the fuzzy speed and the MIMO compensation mode. Therefore, in this application, the speed of a target in a MIMO compensation mode refers to the target's specific speed value. When calculating the preprocessing compensation mode set for a range segment, because no specific target is involved, the speed range of the MIMO compensation mode is used.

[0098] For example, if the maximum unambiguous speed of the target is V1 (-V1 to V1 when considering the speed direction), then -V1 to V1 can be represented as MIMO compensation mode 1, V1 to 3*V1 as MIMO compensation mode 2, and -3*V1 to -V1 as MIMO compensation mode 3. In this way, the lane preset MIMO compensation mode and the target MIMO compensation mode can correspond consistently: if the target is MIMO compensation mode 0, its speed must fall within the range of -V1 to V1. For example, if the target's power graph estimates the speed (the ambiguous speed is 0.5V1), then its true speed may take the value set of {0.5V1, 2.5V1, -1.5V1}, corresponding to {MIMO compensation mode 1, MIMO compensation mode 2, MIMO compensation mode 3} respectively.

[0099] S104 , selecting a preset MIMO compensation mode that meets a preset optimal principle from among various preset MIMO compensation modes of the target to be detected that have not been selected, to obtain a currently selected optimal compensation mode.

[0100] The setting of the preset optimal principle can refer to the selection method of the optimal compensation mode in the related art. In one example, the MIMO compensation mode with the largest average value of array spectrum peaks can be selected from the previously selected preset MIMO compensation modes for the target to be detected to obtain the currently selected optimal compensation mode. The selection of the compensation mode corresponding to the array spectrum with the largest average value as the optimal compensation mode is merely an example; other methods may also be used.

[0101] S105 , determining, based on the clustering result of the currently selected optimal compensation mode, the distance segment where the target to be detected is located as the target distance segment, wherein the clustering result includes the position information of the target to be detected.

[0102] The clustering result of the target to be detected in the optimal compensation mode represents the position information of the target to be detected in each frame of radar signal. Based on the position information of the target to be detected, the range segment where the target to be detected is located can be obtained, which is hereinafter referred to as the target range segment.

[0103] S106 , determining whether the optimal compensation mode currently selected for the target to be detected is one of the pre-processing compensation mode set for the target distance segment.

[0104] S107, if the optimal compensation mode currently selected for the above-mentioned target to be detected is one of the pre-processing compensation mode set of the above-mentioned target distance segment, then determine the speed deambiguation result of the above-mentioned target to be detected under the optimal compensation mode currently selected, wherein the speed deambiguation result of the above-mentioned target to be detected includes the true speed and true direction of the above-mentioned target to be detected.

[0105] If the currently selected optimal compensation mode of the target to be detected is one of the preprocessing compensation mode sets of the target distance segment, the velocity deambiguation result of the target to be detected under the currently selected optimal compensation mode is determined as the final velocity deambiguation result of the target to be detected.

[0106] In an embodiment of the present application, the speed range of historical targets in a distance segment is determined. Based on the speed range of historical targets in the distance segment, a preprocessing compensation mode set for the distance segment can be obtained. According to the preprocessing compensation mode set for the distance segment where the target to be detected is located, it can be effectively determined whether the current optimal compensation mode of the target to be detected is correct. If the optimal compensation mode of the target to be detected is one of the preprocessing compensation mode sets in the distance segment, it means that the speed of the optimal compensation mode is credible, thereby realizing speed deambiguation for TDM-MIMO radar and improving the accuracy of speed deambiguation.

[0107] In one possible implementation, see Figure 4 After determining whether the optimal compensation mode currently selected for the target to be detected is one of the pre-processing compensation mode sets for the target distance segment, the method further includes:

[0108] S108. If the optimal compensation mode of the target to be detected is not one of the pre-processing compensation mode set of the target distance segment, return to the execution step: S104 and select the preset MIMO compensation mode that meets the preset optimal principle from the preset MIMO compensation modes that have not been selected for the target to be detected to obtain the optimal compensation mode.

[0109] If the current optimal compensation mode of the target to be detected is not one of the preprocessing compensation mode set of the target distance segment in which it is located, that is, the speed of the target to be detected under the current optimal compensation mode is not within the speed range of the historical data, it means that the current optimal compensation mode does not meet the requirements, and a new optimal compensation mode needs to be reselected until the currently selected optimal compensation mode is one of the preprocessing compensation mode set of the target distance segment. The speed defuzzification result of the target to be detected under the currently selected optimal compensation mode is determined as the final speed defuzzification result of the target to be detected.

[0110] In an embodiment of the present application, the speed range of historical targets in a distance segment is determined. Based on the speed range of historical targets in the distance segment, a preprocessing compensation mode set for the distance segment can be obtained. According to the preprocessing compensation mode set of the distance segment where the target to be detected is located, it can be effectively determined whether the current optimal compensation mode of the target to be detected is correct. If the optimal compensation mode of the target to be detected is one of the preprocessing compensation mode sets in the distance segment, it means that the speed of the optimal compensation mode is credible. Otherwise, the optimal compensation mode is reselected for correction, and some compensation modes are excluded, thereby realizing speed deambiguation for TDM-MIMO radar and improving the accuracy of speed deambiguation.

[0111] In one possible implementation, see Figure 5 , the above respectively determines the speed range of historical targets in each distance segment, including:

[0112] S1021 , according to the velocity defuzzification result of each historical target in the historical data, each historical target is tracked based on a preset target tracking algorithm to obtain a trajectory of each historical target.

[0113] The preset target tracking algorithm can be any relevant tracking algorithm, and each historical target can be tracked based on the velocity defuzzification result of each historical target, so as to obtain the trajectory of each historical target.

[0114] S1022 , for each distance segment, respectively calculating the movement speed of each of the historical targets within the distance segment according to the trajectory of each of the historical targets.

[0115] For any distance segment, the movement speed (including movement method) of the historical target in the distance segment can be calculated based on the trajectory of the historical target in the distance segment.

[0116] In one example, for each distance segment, the movement speed of each of the above historical targets in the distance segment is calculated based on the trajectory of each of the above historical targets, including: for each distance segment, the movement speed of each of the above historical targets in the distance segment is calculated based on the trajectory of each of the above historical targets in the m-frame radar signal before the current frame radar signal, wherein m is a preset integer. The speed of the target in the distance segment can be calculated based on time accumulation, for example, with m frames as the length of the sliding window, the speed of the target in the sliding window is calculated in real time, thereby updating the speed range to adapt to changes in road conditions. The value of m depends on the actual application scenario. For example, for lane scenarios, the value of m is related to factors such as the length of the traffic light at the intersection, the type of road, and the traffic volume. Generally, the faster the speed of the target in the application scenario, the larger the value of m.

[0117] S1023 , for each distance segment, determining a speed range of the historical targets within the distance segment based on the movement speeds of the historical targets within the distance segment.

[0118] For any distance segment, the speed range of historical targets in the distance segment can be obtained based on the minimum speed and maximum speed of each historical target in the distance segment.

[0119] In one possible implementation, see Figure 6 Before determining the distance segment where the target to be detected is located as the target distance segment based on the clustering result of the currently selected optimal compensation mode, the method further includes:

[0120] S201: Acquire a radar signal and determine a power diagram of the radar signal.

[0121] The method for obtaining the power map of the radar signal can refer to the method for obtaining the power map of the radar signal in the related art. In one embodiment, the above-mentioned acquisition of multiple frames of radar signals and determination of the power map of each frame of radar signals respectively include:

[0122] Step 1: Obtain ADC data of each channel of the radar virtual antenna array to obtain multi-frame radar signals.

[0123] The discrete digital signal data output by the ADC of each channel of the radar virtual antenna array can be obtained to obtain multi-frame radar signals.

[0124] Step 2: Perform a two-dimensional fast Fourier transform on the ADC data of each channel in each frame of radar signal to obtain a virtual array vector of each frame of radar signal.

[0125] The ADC data of each channel in each frame of radar signal can be subjected to FFT transformation in two dimensions, namely, the ADC sampling sequence number dimension and the sampling period dimension, to obtain a virtual array vector of each frame of radar signal.

[0126] Step 3: 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.

[0127] S202: Perform CFAR detection on the power map to obtain the position of each target point in the power map.

[0128] Targets are represented in radar signals as point clouds, meaning multiple target points form a single target. CFAR detection is performed on each power map to determine its noise intensity threshold. For each power map, the power points above the noise intensity threshold are identified as target points, while the power points below or equal to the noise intensity threshold correspond to noise. This allows the target's location to be determined within the power map.

[0129] S203 , obtaining each preset MIMO compensation mode and the direction of arrival of each target point in each preset MIMO compensation mode according to the position of each target point in the power map.

[0130] The target's direction of arrival may include the azimuth and distance of the target point. The detection method of the direction of arrival can refer to the direction of arrival detection method in the related art. The acquisition method of each preset MIMO compensation mode can refer to the acquisition method in the related art. In one example, the velocity-induced phase shift of the virtual array element vector S of the radar signal is estimated. velocity-induced phase shift The target's true speed v true The power map obtained based on the power map estimates the speed v est There may be velocity ambiguity, so the target's true velocity v true The value is unknown, but the possible value set can be obtained by estimating the speed from the power diagram, v true =v est +2*k*v max ,k=…,-1,0,1,…, where v max is the maximum unambiguous measurement speed, k is an integer within a limited range, determined according to the target's motion characteristics (i.e., the range of the target's possible motion speed). Usually for practical application scenarios, such as for scenarios where the target is a motor vehicle, pedestrian, or non-motor vehicle, the value of k is {0, 1, -1}, then v true Different values ​​of v est ,v est +2v max ,v est -2v max The obtained velocity-induced phase shifts are respectively referred to as compensation mode 0, compensation mode 1, and compensation mode 2, ie, the preset MIMO compensation modes.

[0131] S204 , for each preset MIMO compensation mode, clustering the target points in the preset MIMO compensation mode according to the direction of arrival of the target points in the preset MIMO compensation mode to obtain a clustering result of the target to be detected in the preset MIMO compensation mode.

[0132] The target is represented in the form of a point cloud in the radar signal. According to the wave direction of each target point under the preset MIMO compensation mode, the target points are clustered in the preset MIMO compensation mode to obtain the clustering result of each target under the preset MIMO compensation mode, and the clustering result of the target to be detected under the preset MIMO compensation mode is obtained from it.

[0133] In the embodiment of the present application, the speed range of the target in each distance segment is obtained by channel and distance segment. The speed range of the target in the distance segment is used to eliminate some compensation modes, thereby increasing the correct probability of speed deambiguation.

[0134] In one possible implementation, see Figure 7 Before selecting a preset MIMO compensation mode that satisfies a preset optimal principle from among the preset MIMO compensation modes that have not been selected for the target to be detected and obtaining the currently selected optimal compensation mode, the method further includes:

[0135] S301 , obtaining power maps of multiple frames of radar signals, mapping each of the power maps into a two-dimensional matrix of a range dimension and a Doppler dimension, and obtaining a CFAR two-dimensional mask of each frame of the radar signal.

[0136] 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 the target exists can be represented by a first value, and the area where the target does not exist can be represented by a second value. The target here is the target detected by the radar. A possible CFAR two-dimensional mask can be as follows Figure 8 As shown in the figure, where the abscissa is the Doppler dimension and the ordinate is the range dimension. For each power map, CFAR detection is performed on the power map to obtain the noise intensity threshold of the power map. For each power map, in a two-dimensional matrix of the range and Doppler dimensions, the power values ​​in the power map that are greater than the noise intensity threshold are set to a first value, and the power values ​​in the power map that are not greater than the noise intensity threshold are set to a second value. This yields a CFAR two-dimensional mask of the radar signal corresponding to the power map, where the area corresponding to the first value contains the target, and the area corresponding to the second value does not contain the target.

[0137] S302 , according to the time sequence of the radar signals of each frame, performing trajectory association on the target to be detected in the CFAR two-dimensional mask of the radar signals of each frame to obtain an estimated moving speed and an estimated moving direction of the target to be detected.

[0138] The target tracking algorithm in the related art can be used to track the target to be detected in each CFAR two-dimensional mask, and the trajectories of the target to be detected can be associated to obtain the estimated movement speed and estimated movement direction of the target to be detected.

[0139] In one example, step S302 may include:

[0140] Step 1: Perform connected component analysis on each CFAR two-dimensional mask to obtain target information of each target in each CFAR two-dimensional mask. For any target, the target information includes the target width, target height and target center coordinates of the target.

[0141] Assuming the first value in the CFAR 2D mask corresponds to white and the second value corresponds to black, a connected domain analysis is performed on the white areas (i.e., bright spots) in the CFAR 2D mask to obtain the connected domain information of each target in the CFAR 2D mask, i.e., target information. For any target, the target information includes the target width, target height, and target center coordinates (i.e., the coordinates of the target center in the range and Doppler dimensions). Furthermore, the target information may include information such as the target's signal-to-noise ratio.

[0142] In step 2, according to the time sequence of each frame radar signal and the target information of each target, the trajectory of the same target in the CFAR two-dimensional mask of each frame radar signal is associated to obtain the motion trajectory of each target.

[0143] For example Figure 9 As shown, the trajectory of the same target is associated in time sequence to obtain the motion trajectory of each target. When the targets in the radar signal are relatively sparse, for example, when the density of the targets (the number in a unit space) is less than the preset density threshold, the reliability of the motion trajectory association is high, and a larger number of frames can be selected each time to associate and obtain the motion trajectory of each target. When the targets are relatively dense, for example, when the density of the targets is greater than the preset density threshold, in order to reduce the possibility of motion trajectory association errors, the number of frames for each association to obtain the motion trajectory of each target can be reduced, for example, two frames of radar signal analysis are selected each time to obtain the motion trajectory of each target. Among them, the target to be detected is one of the targets.

[0144] Step three: determining the estimated motion speed and estimated motion direction of the target to be detected based on at least the motion trajectory of the target to be detected and the time difference between each frame of radar signal.

[0145] For any target, after obtaining the associated trajectory of the target, the estimated movement speed and estimated movement direction of the target can be calculated based on the time difference between the radar signals of each frame and the associated trajectory of the target.

[0146] S303 : selecting a pre-processing MIMO compensation mode of the target to be detected from preset MIMO compensation modes according to the estimated moving speed and estimated moving direction of the target to be detected.

[0147] The preset MIMO compensation modes include multiple MIMO compensation modes. For any target, MIMO compensation modes that do not meet the constraints of the target's estimated motion speed and estimated motion direction are eliminated from the preset MIMO compensation modes, and the remaining MIMO compensation modes in the preset MIMO compensation modes are used as pre-processing MIMO compensation modes for the target. For any target, all compensation modes whose motion speeds fall within a preset range from the target's estimated motion speed and whose motion directions are the same as the target's estimated motion direction can be selected from the preset MIMO compensation modes as the pre-processing MIMO compensation modes for the target.

[0148] In one example, for a target to be detected, the movement direction and movement speed of the target to be detected in each compensation mode in the preset MIMO compensation mode are obtained; in the preset MIMO compensation mode, a compensation mode whose movement direction is the same as the estimated movement direction of the target to be detected is selected to obtain a compensation mode after filtering the target to be detected; in the compensation mode after filtering the target to be detected, a compensation mode whose movement speed has an error with the estimated movement speed of the target to be detected within a preset range is selected to obtain a preprocessed MIMO compensation mode of the target to be detected.

[0149] Among the preset MIMO compensation modes that have not been selected for the target to be detected, a preset MIMO compensation mode that meets the preset optimal principle is selected to obtain the currently selected optimal compensation mode, including:

[0150] S1041 : Selecting a pre-processing MIMO compensation mode that meets a preset optimal principle from among the unselected pre-processing MIMO compensation modes of the target to be detected, to obtain a currently selected optimal compensation mode.

[0151] The setting of the preset optimal principle can refer to the method for selecting the optimal compensation mode in the relevant technology. In an example, the preprocessing MIMO compensation mode that meets the preset optimal principle is selected from the various preprocessing MIMO compensation modes that have not been selected for the above-mentioned target to be detected, and the currently selected optimal compensation mode is obtained, including: among the various preprocessing MIMO compensation modes that have not been selected for the above-mentioned target to be detected, the preprocessing MIMO compensation mode with the largest average value of the array spectrum peak value is selected to obtain the currently selected optimal compensation mode.

[0152] The present application also provides a speed defuzzification device, see Figure 10 , the device comprises:

[0153] The distance segment division unit 11 is used to obtain channel information of each channel and divide each channel into a plurality of distance segments according to the channel information of each channel;

[0154] A speed range determination unit 12 is used to determine the speed range of historical targets within each distance segment for each channel;

[0155] a mode set determining unit 13 for determining, for each distance segment, a preset MIMO compensation mode having a speed that intersects with a speed range of the distance segment, to obtain a preprocessing compensation mode set for the distance segment;

[0156] The optimal compensation mode selection unit 14 is configured to select a preset MIMO compensation mode that satisfies a preset optimal principle from among various preset MIMO compensation modes of the target to be detected that have not been selected, and obtain a currently selected optimal compensation mode;

[0157] a target distance segment determining unit 15, configured to determine, based on a clustering result of a currently selected optimal compensation mode, the distance segment where the target to be detected is located as a target distance segment, wherein the clustering result includes position information of the target to be detected;

[0158] a compensation mode detection unit 16 for determining whether the optimal compensation mode currently selected for the target to be detected is one of the pre-processing compensation mode sets for the target distance segment;

[0159] The first execution unit 17 is used to determine the speed deambiguation result of the target to be detected under the optimal compensation mode currently selected if the optimal compensation mode currently selected for the target to be detected is one of the preprocessing compensation mode set of the target distance segment, wherein the speed deambiguation result of the target to be detected includes the true speed and true direction of the target to be detected.

[0160] In a possible implementation, the above device further includes:

[0161] The second execution unit is configured to return to execute the optimal compensation mode selection unit if the optimal compensation mode of the target to be detected is not one of the pre-processing compensation mode set of the target distance segment.

[0162] In a possible implementation, the above device further includes:

[0163] a pre-processing MIMO compensation mode determination unit configured to obtain power maps of multiple frames of radar signals, map each of the power maps into a two-dimensional matrix of a range dimension and a Doppler dimension, and obtain a constant false alarm rate (CFAR) two-dimensional mask for each frame of the radar signal; perform trajectory association on a target to be detected in the CFAR two-dimensional mask of each frame of the radar signal according to a time sequence of each frame of the radar signal to obtain an estimated motion speed and an estimated motion direction of the target to be detected; and select a pre-processing MIMO compensation mode for the target to be detected from a preset MIMO compensation mode based on the estimated motion speed and estimated motion direction of the target to be detected;

[0164] The optimal compensation mode selection unit is specifically configured to select a preprocessing MIMO compensation mode that satisfies a preset optimal principle from among the unselected preprocessing MIMO compensation modes of the target to be detected, to obtain the currently selected optimal compensation mode.

[0165] In a possible implementation, the optimal compensation mode selection unit is specifically configured to select, from among the unselected preprocessing MIMO compensation modes of the target to be detected, a preprocessing MIMO compensation mode having the largest average value of array spectrum peaks, to obtain the currently selected optimal compensation mode.

[0166] In a possible implementation, the above device further includes:

[0167] a power graph determining unit, configured to acquire a radar signal and determine a power graph of the radar signal;

[0168] A CFAR detection unit, configured to perform CFAR detection on the power map to obtain the position of each target point in the power map;

[0169] A DOA detection unit is configured to obtain, based on the position of each target point in the power map, each preset MIMO compensation mode and the direction of arrival of each target point in each preset MIMO compensation mode;

[0170] The clustering unit is used to cluster the target points in each preset MIMO compensation mode according to the arrival direction of each target point in the preset MIMO compensation mode, and obtain the clustering result of the target to be detected in the preset MIMO compensation mode.

[0171] In a possible implementation manner, the speed range determining unit includes:

[0172] The target trajectory acquisition subunit is used to track each of the historical targets based on the velocity defuzzification results of each historical target in the historical data and a preset target tracking algorithm to obtain the trajectory of each of the historical targets;

[0173] a motion speed determination subunit, configured to calculate, for each distance segment, the motion speed of each of the historical targets within the distance segment according to the trajectory of each of the historical targets;

[0174] The speed range determining subunit is configured to determine, for each distance segment, a speed range of the historical targets within the distance segment according to the movement speeds of the historical targets within the distance segment.

[0175] In one possible implementation, the motion speed determination subunit is specifically configured to: for each distance segment, calculate the motion speed of each of the historical targets within the distance segment based on the trajectories of the historical targets in the m frames of radar signals preceding the current frame of radar signal, where m is a preset integer.

[0176] The power graph determination unit in the implementation of this application is equivalent to the power graph acquisition module in the above-mentioned speed defuzzification system; the CFAR detection unit in the implementation of this application is equivalent to the CFAR detection module in the above-mentioned speed defuzzification system; the DOA detection unit in the implementation of this application is equivalent to the DOA estimation module in the above-mentioned speed defuzzification system; the clustering unit in the implementation of this application is equivalent to the clustering module in the above-mentioned speed defuzzification system; the combination of the distance segment division unit, the speed range determination unit, and the pattern set determination unit in the implementation of this application is equivalent to the lane condition acquisition module and the tracking module in the above-mentioned speed defuzzification system; the optimal compensation mode selection unit, the first result output unit plus the second result output unit in the implementation of this application are equivalent to the MIMO correction module in the above-mentioned speed defuzzification system.

[0177] In the embodiment of the present application, the speed range of the target in each distance segment is obtained by channel and distance segment. The speed range of the target in the distance segment is used to eliminate some compensation modes, thereby increasing the correct probability of speed deambiguation.

[0178] An embodiment of the present application further provides an electronic device, comprising: a processor and a memory;

[0179] The memory is used to store computer programs;

[0180] When the processor is used to execute the computer program stored in the memory, any speed deambiguation method in the present application is implemented.

[0181] Optional, see Figure 11 In addition to the above-mentioned processor 21 and memory 23, the electronic device of the embodiment of the present application also includes a communication interface 22 and a communication bus 24, wherein the processor 21, the communication interface 22, and the memory 23 communicate with each other through the communication bus 24.

[0182] The communication bus mentioned in the electronic device mentioned above may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. This communication bus can be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, the figure shows only one thick line, but this does not mean that there is only one bus or only one type of bus.

[0183] The communication interface is used for communication between the above electronic device and other devices.

[0184] The memory may include RAM (Random Access Memory) or NVM (Non-Volatile Memory), such as at least one disk storage. Optionally, the memory may also be at least one storage device located away from the aforementioned processor.

[0185] The above-mentioned processor can be a general-purpose processor, including a CPU (Central Processing Unit), an NP (Network Processor), etc.; it can 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, and discrete hardware components.

[0186] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, any speed defuzzification method in the present application is implemented.

[0187] In another embodiment provided by the present application, a computer program product including instructions is also provided, which, when executed on a computer, enables the computer to execute any velocity deambiguation method in the present application.

[0188] In the above embodiments, 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 process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can 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 can be transmitted from one website, computer, server or data center to another website, computer, server or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive SolidState Disk (SSD)).

[0189] It should be noted that, in this article, the technical features in each optional solution can be combined to form a solution as long as there is no contradiction, and these solutions are all within the scope disclosed in this application. Relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that there is any such actual relationship or order between these entities or operations. Moreover, the term "comprise", "comprising" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process, method, article or equipment including a series of elements not only include those elements, but also include other elements not clearly listed, or also include elements inherent to such process, method, article or equipment. In the absence of more restrictions, the elements limited by the sentence "comprising a..." do not exclude the presence of other identical elements in the process, method, article or equipment including the elements.

[0190] 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 referenced to each other.

[0191] The above description is only a preferred embodiment of the present application and is not intended to limit the scope of protection of the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application are included in the scope of protection of the present application.

Claims

1. A velocity defuzzification method, characterized in that: The method comprises: Acquire channel information of each channel, and divide each channel into a plurality of distance segments according to the channel information of each channel; For each channel, determine the speed range of historical targets in each distance segment; For each distance segment, determine a preset MIMO compensation mode whose speed intersects with the speed range of the distance segment, and obtain a pre-processing compensation mode set for the distance segment; Selecting a preset MIMO compensation mode that satisfies a preset optimal principle from among various preset MIMO compensation modes that have not been selected for the target to be detected, to obtain a currently selected optimal compensation mode; Acquire a radar signal and determine a power map of the radar signal; perform CFAR detection on the power map to obtain a position of each target point in the power map; obtain each preset MIMO compensation mode and a direction of arrival of each target point in each preset MIMO compensation mode based on the position of each target point in the power map; for each preset MIMO compensation mode, cluster the target points in the preset MIMO compensation mode based on the direction of arrival of each target point in the preset MIMO compensation mode to obtain a clustering result of the target to be detected in the preset MIMO compensation mode; Determining, based on a clustering result of a currently selected optimal compensation mode, that the distance segment where the target to be detected is located is a target distance segment, wherein the clustering result includes position information of the target to be detected; Determining whether the currently selected optimal compensation mode of the target to be detected is one of the pre-processing compensation mode set of the target distance segment; If the optimal compensation mode currently selected for the target to be detected is one of the preprocessing compensation mode set of the target distance segment, then the speed deambiguation result of the target to be detected under the optimal compensation mode currently selected is determined, wherein the speed deambiguation result of the target to be detected includes the true speed and true direction of the target to be detected.

2. The method according to claim 1, characterized in that After determining whether the optimal compensation mode currently selected for the target to be detected is one of the preprocessing compensation mode set for the target distance segment, the method further includes: If the optimal compensation mode of the target to be detected is not one of the pre-processing compensation mode set of the target distance segment, return to the execution step: among the preset MIMO compensation modes that have not been selected for the target to be detected, select the preset MIMO compensation mode that meets the preset optimal principle to obtain the optimal compensation mode.

3. The method according to claim 1, characterized in that Before selecting a preset MIMO compensation mode that satisfies a preset optimal principle from among the preset MIMO compensation modes that have not been selected for the target to be detected and obtaining the currently selected optimal compensation mode, the method further includes: Obtaining power maps of multiple frames of radar signals, mapping each of the power maps into a two-dimensional matrix of a range dimension and a Doppler dimension, and obtaining a constant false alarm rate (CFAR) two-dimensional mask of the radar signal of each frame; According to the time sequence of the radar signals of each frame, trajectory association is performed on the target to be detected in the CFAR two-dimensional mask of the radar signals of each frame to obtain an estimated movement speed and an estimated movement direction of the target to be detected; Selecting a pre-processing MIMO compensation mode for the target to be detected from preset MIMO compensation modes according to the estimated motion speed and estimated motion direction of the target to be detected; The method of selecting a preset MIMO compensation mode that satisfies a preset optimal principle from among the preset MIMO compensation modes that have not been selected for the target to be detected, and obtaining the currently selected optimal compensation mode, includes: Among the unselected pre-processing MIMO compensation modes of the target to be detected, a pre-processing MIMO compensation mode that meets a preset optimal principle is selected to obtain a currently selected optimal compensation mode.

4. The method according to claim 3, characterized in that The method of selecting a preprocessing MIMO compensation mode that satisfies a preset optimal principle from among the unselected preprocessing MIMO compensation modes of the target to be detected, and obtaining a currently selected optimal compensation mode, includes: Among the unselected pre-processing MIMO compensation modes of the target to be detected, the pre-processing MIMO compensation mode with the largest average value of array spectrum peaks is selected to obtain the currently selected optimal compensation mode.

5. The method according to claim 1, characterized in that The method of determining the speed range of the historical target within each distance segment includes: According to the velocity defuzzification results of each historical target in the historical data, each historical target is tracked based on a preset target tracking algorithm to obtain the trajectory of each historical target; For each distance segment, respectively calculating the movement speed of each historical target within the distance segment according to the trajectory of each historical target; For each distance segment, the speed range of the historical targets in the distance segment is determined according to the movement speed of each historical target in the distance segment.

6. The method according to claim 5, characterized in that The step of calculating, for each distance segment, the movement speed of each historical target within the distance segment according to the trajectory of each historical target, includes: For each distance segment, the movement speed of each historical target in the distance segment is calculated based on the trajectory of each historical target in the m frames of radar signal before the current frame of radar signal, where m is a preset integer.

7. A speed defuzzification device, characterized in that: The device comprises: a distance segment division unit, configured to obtain channel information of each channel and divide each channel into a plurality of distance segments according to the channel information of each channel; A speed range determination unit, for determining the speed range of historical targets within each distance segment for each channel; a mode set determining unit, configured to determine, for each distance segment, a preset MIMO compensation mode having a speed that intersects with a speed range of the distance segment, and obtain a preprocessing compensation mode set for the distance segment; an optimal compensation mode selection unit, configured to select a preset MIMO compensation mode that satisfies a preset optimal principle from among various preset MIMO compensation modes of the target to be detected that have not been selected, and obtain a currently selected optimal compensation mode; a power graph determining unit, configured to acquire a radar signal and determine a power graph of the radar signal; A CFAR detection unit, configured to perform CFAR detection on the power map to obtain a position of each target point in the power map; A DOA detection unit is configured to obtain, based on the position of each target point in the power map, each preset MIMO compensation mode and the direction of arrival of each target point in each preset MIMO compensation mode; a clustering unit configured to cluster the target points in each preset MIMO compensation mode according to the direction of arrival of each target point in the preset MIMO compensation mode, thereby obtaining a clustering result of the target to be detected in the preset MIMO compensation mode; and a target distance segment determination unit configured to determine, based on the clustering result of the currently selected optimal compensation mode, the distance segment in which the target to be detected is located as the target distance segment, wherein the clustering result includes the position information of the target to be detected; a compensation mode detection unit, configured to determine whether the optimal compensation mode currently selected for the target to be detected is one of the pre-processing compensation mode sets for the target distance segment; The first execution unit is configured to determine a velocity deambiguation result of the target to be detected under the optimal compensation mode currently selected if the optimal compensation mode currently selected for the target to be detected is one of the preprocessing compensation mode set for the target distance segment, wherein the velocity deambiguation result of the target to be detected includes a true velocity and a true orientation of the target to be detected.

8. The device according to claim 7, characterized in that The device further comprises: The second execution unit is configured to return to execute the optimal compensation mode selection unit if the optimal compensation mode of the target to be detected is not one of the pre-processing compensation mode set of the target distance segment.

9. The device according to claim 7, characterized in that The device further comprises: a pre-processing MIMO compensation mode determination unit, configured to obtain power maps of multiple frames of radar signals, map each power map into a two-dimensional matrix of a range dimension and a Doppler dimension, and obtain a constant false alarm rate (CFAR) two-dimensional mask for each frame of the radar signal; perform trajectory association on a target to be detected in the CFAR two-dimensional mask of each frame of the radar signal according to a time sequence of each frame of the radar signal to obtain an estimated motion speed and an estimated motion direction of the target to be detected; and select a pre-processing MIMO compensation mode for the target to be detected from a preset MIMO compensation mode based on the estimated motion speed and estimated motion direction of the target to be detected; The optimal compensation mode selection unit is specifically configured to select a preprocessing MIMO compensation mode that satisfies a preset optimal principle from various preprocessing MIMO compensation modes that have not been selected for the target to be detected, to obtain a currently selected optimal compensation mode.

10. The device according to claim 9, characterized in that The optimal compensation mode selection unit is specifically configured to select a preprocessing MIMO compensation mode with the largest average value of array spectrum peaks from among the unselected preprocessing MIMO compensation modes of the target to be detected, to obtain the currently selected optimal compensation mode.

11. The device according to claim 7, characterized in that The speed range determining unit includes: A target trajectory acquisition subunit is used to track each historical target based on a preset target tracking algorithm according to the velocity defuzzification result of each historical target in the historical data, so as to obtain the trajectory of each historical target; a motion speed determination subunit, configured to calculate, for each distance segment, the motion speed of each of the historical targets within the distance segment according to the trajectory of each of the historical targets; The speed range determining subunit is configured to determine, for each distance segment, a speed range of the historical targets within the distance segment according to the movement speeds of the historical targets within the distance segment.

12. The device according to claim 11, characterized in that The motion speed determination subunit is specifically used to: for each distance segment, calculate the motion speed of each historical target in the distance segment according to the trajectory of each historical target in the m frames of radar signal before the current frame of radar signal, where m is a preset integer.

13. An electronic device, characterized in that: including processor and memory; The memory is used to store computer programs; The processor is configured to implement the velocity defuzzification method according to any one of claims 1 to 6 when executing the program stored in the memory.

14. 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 a processor, the speed defuzzification method according to any one of claims 1 to 6 is implemented.

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