Hypothesis phase compensation velocity ambiguity solving method

Abstract

The invention discloses a hypothetical phase compensation solution velocity ambiguity method, which comprises the following steps of: identifying and detecting a dynamic target, and acquiring radar data, a Doppler phase difference phi cfu corresponding to the dynamic target, an all-channel data set S and a hypothetical velocity ambiguity period N; according to an assumed velocity ambiguity period N and a phase compensation formula, Doppler phase compensation is carried out on the radar data to obtain compensated radar data; drawing an angular power spectrum based on the compensated radar data, determining a target peak point according to a peak point in the angular power spectrum, and determining a representative point of the dynamic target and the time complexity of the representative point according to the target peak point and the geometric center of the dynamic target; and determining the real speed Vtrue of the dynamic target based on the time complexity corresponding to the representative point and the target speed estimated value Vcfu. By applying the method provided by the invention, the correctness and calculation performance of speed estimation are improved.

Description

Technical Field

[0001] The present application relates to the technical field of electronic data processing, and more particularly to a method for solving velocity ambiguity by assuming phase compensation. Background Art

[0002] In radar systems, Doppler shift is a means of measuring the target's velocity relative to the radar. However, velocity ambiguity occurs when the Doppler shift generated by the target's velocity exceeds the maximum frequency that the radar system can process. Although MIMO radar can provide richer target information, such as additional information such as target height and profile, its operating principle significantly reduces the radar system's unambiguous detection velocity range. Inaccurate target velocity estimation affects the accuracy of target angle estimation, making velocity ambiguity resolution crucial for MIMO imaging radars. Methods based on pulse repetition frequency (PRF) variation are often common solutions to velocity ambiguity, including the Chinese Remainder Theorem (CRT), table lookup, and one-dimensional set algorithms.

[0003] The Chinese Remainder Theorem (CRT) is an effective method for eliminating phase uncertainty in Doppler shift detection. It obtains the deambiguation result by establishing a system of congruence equations between the measured value and the target velocity. However, in the classic CRT, the greatest common divisor (GDC) of any pair of PRFs is equal to 1. While this method for resolving Doppler ambiguity is quite accurate and fast, its accuracy degrades rapidly when the remainder exhibits some deviation due to noise. On the other hand, the robust Chinese Remainder Theorem is a search-based algorithm that can guarantee the correct estimation of the Doppler frequency under certain conditions. For example, Yue-bin et al. proposed a new method for resolving velocity ambiguity in pulse Doppler (PD) radar. This method overcomes the velocity ambiguity by utilizing multi-carrier frequency waveforms and the Chinese Remainder Theorem. Based on this, an improved algorithm with greater robustness was derived. Xiao et al. proposed a robust phase unwrapping method based on the Chinese Remainder Theorem (CRT) and applied it to moving target synthetic aperture radar (SAR) imaging. This algorithm only performs a one-dimensional search, thus significantly reducing complexity. Both algorithms search for overlap by spreading the signal over different aliasing periods or by using algebraic techniques to spread different prf estimates over different aliasing periods. This concept of searching for overlapping estimates allows the application of clustering concepts to velocity ambiguity. An improved clustering algorithm is proposed and compared with an improved Chinese Remainder Theorem (CRT) algorithm. The results show that the proposed algorithm has a lower false alarm probability and can be generalized to multiple target and clutter scenarios.

[0004] The classic table lookup method precalculates and stores velocity-Doppler shift relationships for different scenarios to quickly match the observed Doppler shift and thus determine the target's true velocity. Based on this idea, a simple residual-based algorithm for range and velocity ambiguity was proposed. This algorithm uses a residual lookup table to achieve unambiguous results and employs another algorithm to address the specific problem of frequency-consistent filter bandwidth. While this method is simple and easy to implement in engineering, the table storage requires a large amount of storage space. Furthermore, the lookup table divides degrees by distance units, wasting significant time on unnecessary numerical calculations. This makes it difficult to achieve real-time processing requirements and is difficult to adapt to complex scenarios.

[0005] In view of the technical problems existing in existing MIMO radar technologies, such as velocity ambiguity, difficulty in meeting real-time processing requirements, complex processing, and large storage space, it is necessary to introduce an improved hypothetical phase compensation method. According to the factors affecting the angle estimation by the velocity ambiguity problem, this method simplifies the processing flow and complexity of the hypothetical phase compensation for the speed ambiguity resolution of the multi-channel MIMO millimeter-wave radar in the road test, reduces the number of target points that the algorithm needs to process, ensures the accuracy of the speed estimation, accelerates the processing flow of obtaining the target speed and angle in the MIMO radar imaging processing, reduces the number of abnormal speed points, and thus improves the accuracy of the speed estimation and the computational performance. Summary of the Invention

[0006] In response to the technical problems mentioned above, the present invention provides a method for resolving velocity ambiguity using assumed phase compensation. According to the factors affecting the velocity ambiguity problem on angle estimation, the present invention provides and applies a method for reducing the number of target points for dynamic target detection, as well as an improved algorithm formula for resolving velocity ambiguity using assumed phase compensation. The method solves the technical problems existing in the prior art, such as MIMO radar velocity ambiguity, difficulty in meeting real-time processing requirements, complex processing, and large data storage space. The method simplifies the processing flow and complexity of resolving velocity ambiguity using assumed phase compensation for road test multi-channel MIMO millimeter-wave radar, reduces the number of target points that the algorithm needs to process, ensures the accuracy of velocity estimation, accelerates the processing flow for obtaining target velocity and angle in MIMO radar imaging processing, reduces abnormal velocity points, and thereby improves the real-time, correctness, and computational performance of velocity estimation.

[0007] The present invention provides a method for resolving velocity ambiguity by assuming phase compensation, the method comprising: S1, based on the influencing parameters of the assumed phase compensation solution velocity ambiguity, define the phase compensation data structure; S2, based on the MIMO millimeter wave radar, the constant false alarm rate detector and the phase compensation data structure, identify and detect the dynamic target, obtain radar data, and the Doppler phase difference corresponding to the dynamic target , all channel data sets S and assumed speed ambiguity period N; S3, according to the assumed speed ambiguity period N and the phase compensation formula, the radar data is Doppler phase compensated to obtain compensated radar data; S4, based on the compensated radar data, an angular power spectrum is drawn, and the target peak point is determined according to the peak point in the angular power spectrum, and the representative point of the dynamic target and the time complexity of the representative point are determined according to the target peak point and the geometric center of the dynamic target; S5, based on the time complexity corresponding to the representative point and the target speed estimation value V cfar , determine the true speed of the dynamic target V true , and the complexity is reduced by a factor M.

[0008] Preferably, in step S1: the phase compensation data structure includes: the number of transmitting antennas , Number of receiving antennas , target distance and dynamic target data; wherein, the dynamic target data includes: target point, number of target points n, representative point, number of representative points a, true speed V true , Phase compensation value , the number of phase compensation values ​​m, the target speed estimate V cfar , Doppler phase difference , maximum speed V max .

[0009] Preferably, the step S2 includes: S21, according to the number of transmitting antennas and the number of receiving antennas of the MIMO millimeter wave radar, and the target point of the dynamic target, obtaining the channel data set of each target point detected by the MIMO millimeter wave radar and the constant false alarm rate detector , true phase compensation value and the detection result sequence, and the assumed velocity ambiguity period N; S22, according to the assumed velocity ambiguity period N and the true phase compensation value , determine the Doppler phase difference of each target point ; wherein the test result is , , is the number of transmitting antennas of the MIMO millimeter-wave radar, is the number of receiving antennas of the MIMO millimeter wave radar; the Doppler phase difference of the target point , is the actual phase compensation value of each target point , N is the assumed velocity ambiguity period, .

[0010] Preferably, the step S2 further includes: detecting the channel of each target point based on the MIMO millimeter wave radar, and obtaining the channel data set of each target point , and based on the channel data set of each target point and Doppler phase difference , determine the phase value that needs to be compensated ;in, Characterization i The radar data corresponding to the target point obtained in the channel, i is the channel number corresponding to each target point, and i is an integer greater than 1; the phase value of the target point that needs to be compensated .

[0011] Preferably, the step S2 further includes: all channel data sets Assume that the velocity ambiguity period N is determined according to the number of transmitting antennas of the MIMO millimeter-wave radar, wherein, when the number of transmitting antennas of the MIMO millimeter-wave radar is an odd number, , when the number of transmitting antennas of the MIMO millimeter-wave radar is an even number, The radar data is all channel data in the original format obtained based on the channel corresponding to each target point of the dynamic target detected by the MIMO millimeter wave radar, including target point, number of target points n, representative point, number of representative points a, true speed V true , Phase compensation value , the number of phase compensation values ​​m, the target speed estimate V cfar , Doppler phase difference and maximum speed V max .

[0012] Preferably, the step S3 includes: detecting a channel data set of each target point based on the MIMO millimeter wave radar , and assuming the velocity ambiguity period N and the phase compensation formula to perform Doppler compensation calculation on the radar data, obtaining the compensated radar data corresponding to each target point; wherein the compensation formula is , is the compensated radar data corresponding to each target point, is the phase value that needs to be compensated for the target point, i is the channel number corresponding to each target point, i and j Are greater than 1 and less than or equal to integer, is the number of transmitting antennas of the MIMO millimeter-wave radar.

[0013] Preferably, in step S4, the step of drawing an angular power spectrum based on the compensated radar data, determining a target peak point according to a peak point in the angular power spectrum, and determining a representative point of the dynamic target according to the target peak point and the geometric center of the dynamic target further includes: S41, based on the compensated radar data, according to the assumed velocity ambiguity period N, and the actual phase compensation value corresponding to each target point of the dynamic target , draw the angular power spectrum; S42, determine the target peak point and target peak value corresponding to each target point from the peak point in the curve of the angular power spectrum, and sort the target peaks to determine the maximum peak point and maximum peak value, as well as the time complexity of the maximum peak point, wherein the time complexity of the maximum peak point is , n is the number of target points, m is the number of phase compensation values; S43, determining the representative point of the dynamic target according to the target peak point and the geometric center of the dynamic target, and determining the time complexity of the representative point according to the time complexity of the maximum peak point.

[0014] Preferably, the step S43 further includes: connecting the target peak point and the geometric center of the dynamic target, and determining the center point of the connection and the time complexity, determining the representative point according to the distance between the target peak point and the center point, wherein the representative point is a target peak point whose distance from the center point is less than a preset value, and the preset value is set according to the target peak point near the center point; determining the time complexity of the representative point according to the number of representative points of the representative point and the time complexity of the maximum peak point; wherein the time complexity of the representative point is , a is the number of representative points of the representative points, and m is the number of phase compensation values.

[0015] Preferably, the step S5 further includes: S51, improving and optimizing the time complexity corresponding to the dynamic target according to the time complexity of the center point of the line connecting the target peak point and the geometric center of the dynamic target and the time complexity of the representative point, and obtaining the assumed phase compensation speed ambiguity solution algorithm formula and the complexity reduction factor M, wherein the assumed phase compensation speed ambiguity solution algorithm formula is: , the complexity is reduced by a factor of , n is the number of target points, m is the number of phase compensation values; S52, based on the assumed phase compensation speed ambiguity solution algorithm formula and the target speed estimation value V cfar Determine the true velocity of the dynamic targetV true , where the true speed of the dynamic target is , is the estimated target speed, is the maximum speed of the dynamic target, and N is the assumed speed fuzzy period.

[0016] By applying the above technical solutions, the present invention realizes the influencing factors of angle estimation based on the speed ambiguity problem. By providing and applying a method to reduce the number of target points for dynamic target detection and an improved assumed phase compensation speed ambiguity resolution algorithm formula, the technical problems existing in the prior art, such as MIMO radar speed ambiguity, difficulty in meeting real-time processing requirements, complex processing, and large data storage space, are solved. The processing flow and complexity of speed ambiguity resolution of multi-channel MIMO millimeter-wave radar assumed phase compensation for road test are simplified, the number of target points that the algorithm needs to process is reduced, and the accuracy of speed estimation is guaranteed. The processing flow of obtaining target speed and angle in MIMO radar imaging processing is accelerated, abnormal speed points are reduced, and the real-time performance, accuracy, and computational performance of speed estimation are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0018] Figure 1 A schematic flow chart of a method for resolving velocity ambiguity by assuming phase compensation, proposed in an embodiment of the present invention, is shown; Figure 2 Schematic diagram showing three phase compensation assumptions; Figure 3 A schematic diagram of a curve showing the power amplitude of three hypothetical signals in the angular power spectrum; Figure 4 A schematic diagram of the process of selecting a peak point as a target representative point is shown; Figure 5 A two-dimensional schematic diagram showing the selection of the geometric center point as the target representative point; Figure 6 A schematic diagram showing a representative point selection method combining a peak point with an RDM geometric center point is shown; Figure 7 The figure shows the comparison of the time complexity of the HPC algorithm before and after improvement under different target points; Figure 8 A two-dimensional schematic diagram of the defuzzification results of the original HPC algorithm for a moving target is shown; Figure 9 A two-dimensional schematic diagram of the defuzzification results of the improved HPC algorithm for moving targets is shown. DETAILED DESCRIPTION

[0019] 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.

[0020] The present invention provides a method for solving velocity ambiguity by assuming phase compensation, such as Figures 1 to 9 As shown, the method includes the following steps: S1, define the phase compensation data structure based on the influencing parameters of the assumed phase compensation solution to velocity ambiguity.

[0021] In this embodiment, in step S1: the phase compensation data structure includes: the number of transmitting antennas , Number of receiving antennas , target distance and dynamic target data; in, The dynamic target data includes: target point, number of target points n, representative point, number of representative points a, real speed V true , Phase compensation value , the number of phase compensation values ​​m, the target speed estimate V cfar , Doppler phase difference , maximum speed V max .

[0022] S2, based on MIMO millimeter wave radar, constant false alarm rate detector and the phase compensation data structure, identify and detect dynamic targets, obtain radar data, and the Doppler phase difference corresponding to the dynamic target , the total channel data set S and the assumed velocity ambiguity period N.

[0023] In this embodiment, the step S2 includes: S21, according to the number of transmitting antennas and receiving antennas of the MIMO millimeter wave radar, and the target point of the dynamic target, obtaining a channel data set of each target point detected by the MIMO millimeter wave radar and the constant false alarm rate detector , true phase compensation value and the sequence of detection results, and the assumed velocity ambiguity period N; S22, based on the assumed velocity ambiguity period N and the true phase compensation value , determine the Doppler phase difference of each target point ; in, The test results are , , is the number of transmitting antennas of the MIMO millimeter-wave radar, is the number of receiving antennas of the MIMO millimeter-wave radar; The Doppler phase difference of the target point , is the actual phase compensation value of each target point , N is the assumed velocity ambiguity period, .

[0024] In this embodiment, the step S2 further includes: Detect the channel of each target point based on the MIMO millimeter wave radar, and obtain the channel data set of each target point , and based on the channel data set of each target point and Doppler phase difference , determine the phase value that needs to be compensated ; in, Characterization i The radar data corresponding to the target point obtained in the channel, i is the channel number corresponding to each target point, and i is an integer greater than 1; The phase value of the target point that needs to be compensated .

[0025] In this embodiment, the step S2 further includes: All channel data sets ; Assume that the velocity ambiguity period N is determined according to the number of transmitting antennas of the MIMO millimeter-wave radar. When the number of transmitting antennas of the MIMO millimeter-wave radar is an odd number, , when the number of transmitting antennas of the MIMO millimeter-wave radar is an even number, ; The radar data is all channel data in the original format obtained by the MIMO millimeter wave radar detecting the channels corresponding to each target point of the dynamic target, including target point, number of target points n, representative point, number of representative points a, true speed V true , Phase compensation value , the number of phase compensation values ​​m, the target speed estimate V cfar , Doppler phase difference and maximum speed V max .

[0026] S3. Perform Doppler phase compensation on the radar data according to an assumed velocity ambiguity period N and a phase compensation formula to obtain compensated radar data.

[0027] In this embodiment, the step S3 includes: Channel dataset for detecting each target point based on the MIMO millimeter wave radar , and assuming a velocity ambiguity period N and a phase compensation formula to perform Doppler compensation calculation on the radar data, to obtain compensated radar data corresponding to each of the target points; in, The compensation formula is: , is the compensated radar data corresponding to each target point, is the phase value that needs to be compensated for the target point, i is the channel number corresponding to each target point, i and j Are greater than 1 and less than or equal to integer, is the number of transmitting antennas of the MIMO millimeter-wave radar.

[0028] S4, drawing an angular power spectrum based on the compensated radar data, determining the target peak point according to the peak point in the angular power spectrum, and determining the representative point of the dynamic target and the time complexity of the representative point according to the target peak point and the geometric center of the dynamic target.

[0029] In this embodiment, in step S4, the steps of drawing an angular power spectrum based on the compensated radar data, determining a target peak point according to a peak point in the angular power spectrum, and determining a representative point of the dynamic target according to the target peak point and the geometric center of the dynamic target further include: S41, based on the compensated radar data, according to the assumed velocity ambiguity period N and the actual phase compensation value corresponding to each target point of the dynamic target , plot the angular power spectrum; S42, determining the target peak point and target peak value corresponding to each target point from the peak point in the curve of the angular power spectrum, and sorting the target peaks to determine the maximum peak point and the maximum peak value, as well as the time complexity of the maximum peak point, wherein the time complexity of the maximum peak point is , n is the number of target points, m is the number of phase compensation values; S43 , determining a representative point of the dynamic target according to the target peak point and the geometric center of the dynamic target, and determining a time complexity of the representative point according to the time complexity of the maximum peak point.

[0030] In this embodiment, the step S43 further includes: Connect the target peak point and the geometric center of the dynamic target, determine the center point of the connection and the time complexity, and determine the representative point based on the distance between the target peak point and the center point, wherein the representative point is a target peak point whose distance from the center point is less than a preset value, and the preset value is set based on the target peak point near the center point; Determining the time complexity of the representative point according to the number of representative points of the representative point and the time complexity of the maximum peak point; Among them, the time complexity of the representative point is , a is the number of representative points of the representative points, and m is the number of phase compensation values.

[0031] S5, based on the time complexity corresponding to the representative point and the target speed estimation value V cfar , determine the true speed of the dynamic target V true , and the complexity is reduced by a factor M.

[0032] In this embodiment, the step S5 further includes: S51, based on the time complexity of the center point of the line connecting the target peak point and the geometric center of the dynamic target, and the time complexity of the representative point, the time complexity corresponding to the dynamic target is improved and optimized, and a hypothetical phase compensation speed ambiguity solution algorithm formula and a complexity reduction factor M are obtained, wherein the hypothetical phase compensation speed ambiguity solution algorithm formula is: , the complexity is reduced by a factor of , n is the number of target points, m is the number of phase compensation values; S52, based on the assumed phase compensation speed ambiguity resolution algorithm formula and the target speed estimation value V cfar Determine the true velocity of the dynamic target V true ,in, The true speed of the dynamic target , is the estimated target speed, is the maximum speed of the dynamic target, and N is the assumed speed fuzzy period.

[0033] By applying the above technical solutions, the factors affecting angle estimation based on the speed ambiguity problem are realized. By providing and applying a method to reduce the number of target points for dynamic target detection and an improved hypothetical phase compensation speed ambiguity resolution algorithm formula, the technical problems existing in the prior art, such as MIMO radar speed ambiguity, difficulty in meeting real-time processing requirements, complex processing, and large data storage space, are resolved. The processing flow and complexity of speed ambiguity resolution for the hypothetical phase compensation of a road test multi-channel MIMO millimeter-wave radar are simplified, the number of target points that the algorithm needs to process is reduced, and the accuracy of speed estimation is guaranteed. The processing flow for obtaining target speed and angle in MIMO radar imaging processing is accelerated, abnormal speed points are reduced, and the real-time, correctness, and computational performance of speed estimation are improved.

[0034] In order to facilitate those skilled in the art to better understand the velocity ambiguity resolution method provided by the present invention using an assumed phase compensation, the above steps are further described with examples.

[0035] Step 1: Analyze the impact of velocity ambiguity on angle estimation According to the moving target echo signal model received by the radar, the target's velocity parameter This will cause the radar echo signal frequency to produce Doppler shift , which will produce a phase difference , .

[0036] According to the radar angle measurement principle, the angle estimation , that is, the radar angle is mainly determined by the phase difference, and the phase difference of the moving target is related to the Doppler frequency shift That is, there is a coupling relationship between the angle estimation and velocity of a moving target. The ambiguity of the target velocity will cause the angle estimation to be incorrect, resulting in a shift in the target position in the final point cloud image.

[0037] It is crucial to the accuracy of radar imaging to calibrate the phase change caused by the target speed through velocity ambiguity resolution technology. After the radar range-Doppler map (RDM) containing the target speed and distance information is processed by CFAR detection, the estimated value of the target speed and the Doppler phase difference can be obtained. However, in actual applications, the speed of moving targets often exceeds the maximum detectable speed of the radar. Taking the MIMO radar as an example, a virtual antenna array with 1 transmitter and 8 receivers can be formed. Assume that the result sequence passing through the CFAR target point is , then the phase compensation satisfies the formula: .

[0038] Only by knowing the target's true speed Only then can we get the true phase compensation value Assume that the target velocity estimate after CFAR processing is , the Doppler phase difference is Target true speed and target speed estimate and the actual Doppler phase difference that needs to be compensated The relationship between them is: .

[0039] in The speed blur period. Only when the target speed is less than the maximum unambiguous speed , otherwise, the phase difference is Integer multiple.

[0040] According to the above formula, there is a multiple relationship between the target's true speed and the speed error, known as the speed ambiguity period. As the target speed increases, the difference between the target speed detected by the constant false alarm rate (CFAR) detector and the actual target speed increases, which leads to greater errors in the radar's target angle estimation. Furthermore, the difference between the target speed detected by the constant false alarm rate (CFAR) detector and the actual target speed continues to increase. In urban traffic environments, moving targets are often high-speed vehicles. Therefore, the accuracy of target angle measurement depends on effectively resolving speed ambiguity.

[0041] Step 2: Improved hypothetical phase compensation method for resolving velocity ambiguity In step 1, we analyzed the impact of velocity ambiguity on angle estimation. It was found that when a MIMO radar imaging system deambiguates a moving target, the target typically exhibits a large velocity ambiguity period. To address the impact of velocity ambiguity on angle estimation, this application introduces a hypothetical phase compensation (HPC) velocity deambiguation algorithm. This algorithm can estimate the target's true velocity even with a large velocity ambiguity period and perform velocity interpretation and deambiguation within a single frame. The following describes the principles of the HPC algorithm.

[0042] In a MIMO radar system with 4Tx, the phase difference between chirp1 and chirp5 represents the detected target Doppler shift. ,when When , velocity ambiguity appears as cyclic Doppler phase aliasing. Figure 2 As shown, the leftmost column shows such an aliasing situation, where three different speeds have the same detection phase shift , as shown in red traces within the true phase circle of each velocity region (R1, R2, and R3). R1 ​​represents a target with a speed less than the radar's maximum unambiguous velocity, R2 represents a target moving away from the radar at a speed greater than the radar's maximum unambiguous velocity, and R3 represents a target moving toward the radar at a speed greater than the radar's maximum unambiguous velocity. The true phase shifts of R2 and R3 are: So we can use HPC to establish three assumptions and extend the maximum unambiguous speed to First, the detection phase shift of the CFAR detection output is , enumerate all possible phase values ​​that may need to be compensated: .

[0043] Secondly, according to the formula , use each hypothetical phase compensation value to compensate the radar data in turn, and the compensated data is expressed as: ,in, Indicates the Signal data in channels, Starting from 2, since only non-initial codes require Doppler phase compensation, Indicates the phase value that needs to be compensated. Note that the number of assumptions cannot be increased infinitely, it is affected by the number of transmitting antennas in the system. Limitations, ideally, the maximum detection speed of the HPC algorithm is In the case of R1, the target speed is within the maximum unambiguous speed range detectable by the radar, and the compensation phase is In the coherence converges to the same position. and Additional phase compensation Compensation, the phase not only converges to in Compensation, when the speed is and hour, and Phase focusing occurs. Finally, after the angle-dimensional FFT, the angular power spectrum under each assumption is obtained. The assumption of the peak point is correct, and the corresponding speed and angle are the target's true speed and angle.

[0044] like Figure 3 As shown in the figure, three hypothetical signal amplitudes of the next target point on the angular power spectrum are given. 、 and exist There is a peak value around The peak value is the highest, indicating that the actual speed is .

[0045] Step 3: Select representative points based on geometric center and peak position The process of using the HPC velocity deambiguation algorithm to resolve velocity ambiguity and obtain the target's true velocity and angle is as follows: Step 1): Before implementing the algorithm, you need to determine the detection phase , channel dataset and the assumed velocity blur period The first CFAR detection of the target data collected by the radar can obtain the range and Doppler information of the target in the radar range-Doppler map (RDM) and calculate the detection phase At the same time, data is extracted from the same index position in the radar range-Doppler map (RDM) of each channel to form a channel data set: , The value depends on the number of transmitting antennas To calculate. Among them, Satisfies: When the number of transmitting antennas is odd, , when the number of transmitting antennas is even, .

[0046] Step 1): According to Value, list all HPC cases, and compensate the radar data with each hypothetical phase compensation value in turn, Satisfaction . Then for each compensation data (a total of ) to perform FFT operation and obtain the angular power spectrum under various assumptions. .

[0047] Step 3): Select the assumption that the peak point in the angular power spectrum is the corresponding speed and the angle obtained are the true values ​​of the target.

[0048] The HPC velocity deambiguation algorithm needs to perform the above processing for each target point detected by CFAR. The time complexity of listing all HPC cases for a target point depends on the number of assumed phase compensation values, which is set as After performing FFT calculations on all the obtained compensation values ​​to obtain the angular power spectrum, it is necessary to quickly sort the peak values ​​after all the assumed phase compensations. The time complexity of selecting the maximum peak point is: , then the overall time complexity of this step is: Assume that a moving target is detected by MIMO radar as target points, CFAR detection can usually be considered as linear time complexity, that is, ,in is the number of target points. That is, the time complexity of the entire HPC velocity defuzzification process is: ,in, The value is related to the number of transmitting antennas. Therefore, the time complexity of the HPC velocity ambiguity solution algorithm is mainly affected by the number of target points. impact.

[0049] MIMO radar can decompose a single target into multiple target points due to its high spatial resolution, and this high resolution leads to an increase in the number of target points. In the process of processing each target point to eliminate velocity ambiguity, if the HPC velocity ambiguity removal algorithm is applied to each target point, the algorithm time complexity will be significantly increased, thereby reducing the system processing speed. In addition, the higher energy of the sidelobe points may cause velocity expansion, thereby generating noise in the point cloud data. Because these target points actually represent the same target ... The HPC velocity defuzzification algorithm is used for each representative point. Its velocity value represents the overall velocity value, and its phase value that needs to be compensated is represents the phase value that needs to be compensated for all points, then the time complexity of the HPC velocity ambiguity solution process is: .

[0050] This method significantly reduces the time complexity of the HPC defuzzification process and improves system processing efficiency.

[0051] There are two ways to select a representative point. First, you can select the point with the strongest energy in the target point cloud image, that is, the peak point as the representative point, but this method has limitations. Although the peak point as the highest energy point of the target does represent the strongest reflection position of the target, this position is not always consistent with the geometric center of the target. Figure 4 As shown in the figure, in traffic scenarios, radars are generally installed on both sides of the road. When the peak point is selected as the representative point, the relative azimuth angle between the target and the radar is: , and the actual angle value is , there is an angle error: Therefore, representing the entire target based solely on the point with the highest reflected energy may lead to errors in angle estimation. This error may eventually manifest as a target position offset in the imaging results, affecting the imaging quality and the accuracy of target positioning.

[0052] On the other hand, another method is to select representative points based on the geometric center point of the target in the radar range-Doppler map (RDM). Figure 5As shown in the figure, the advantage of using the geometric center point as a representative point is that it can provide a point that better represents the overall position of the target. When the radar is located close to both sides of the road to observe vehicle targets, the target points obtained may present an "L"-shaped arrangement pattern. In this layout, the white dot represents the geometric center point obtained from the target radar range-Doppler map (RDM) analysis. However, the geometric center point is not always located in the area with the highest target energy, because MIMO radar has the characteristics of high range resolution and velocity resolution, which will cause the target energy to spread, resulting in the point possibly being located in the radar sidelobe area. If such a geometric center point is selected as the representative point, it may introduce errors in the velocity ambiguity resolution process, affecting the accuracy of phase compensation and thus affecting the imaging results.

[0053] Selecting the peak point as the representative point can reflect the target's highest energy point, but it may not reflect the target's exact location. Selecting the geometric center point of the target radar range-Doppler map (RDM) is closer to the target's true geometric center point, but this point may also introduce errors because it is located in the radar sidelobe area. Therefore, this application proposes a representative point selection strategy to combine the advantages of these two methods. Figure 6 As shown in the figure, the red dot represents the actual geometric center of the target. Since different moving targets have different geometric centers, for example, a car's geometric center is located inside the car and cannot be directly detected by radar. Therefore, the strategy adopted by this application is to find an optimal representative point near the midpoint of the line connecting the target peak point and the geometric center of the target's radar range-Doppler map (RDM), and use this point as the representative point.

[0054] Step 4: Algorithm performance simulation and comparative analysis This section verifies the effectiveness of the proposed improved hypothetical phase compensation velocity ambiguity resolution algorithm, specifically from two dimensions: time complexity and accuracy of target velocity ambiguity resolution.

[0055] First, verify the time complexity, assuming that the time complexity of the phase compensation velocity ambiguity solution algorithm is: ,in is the target number, is the number of assumed phase compensation values, which is 5 for a 12-transmit 16-receive MIMO radar. The time complexity of the improved assumed phase compensation velocity ambiguity resolution algorithm is calculated below. For a The time complexity of finding the peak point of the target and the geometric center of the radar range-Doppler map (RDM) is , then the time complexity of calculating the center point between two points is: Finally, the time complexity of finding the point closest to the target point, that is, the representative point, is: , combining the above steps, the total computational complexity is mainly composed of traversing The operation of each point is determined, so the total complexity is , the time complexity of HPC processing on the representative points is: That is, the time complexity of the improved hypothetical phase compensation velocity ambiguity resolution algorithm is: , it is assumed that the time complexity relationship of the phase compensation velocity deambiguation algorithm before and after improvement satisfies: ,in, The algorithm complexity is reduced by a factor of 2.

[0056] Because small cars are the most common moving targets in traffic scenes, this application uses small cars as an example to verify the algorithm. Assuming that the car length is about 4.5m, commonly used traffic radars will usually identify small cars as about 10 target points due to their low distance resolution and speed resolution. In the MIMO radar imaging system, due to the improvement of distance, speed and angle resolution and the shorter imaging distance, the size of the target can be presented as much as possible. A small car will be identified as about 400 target points. Set the number of target points from 10 to 400, such as Figure 7 As shown, the relationship between the number of target points and the time complexity of the algorithm is intuitively expressed. The time complexity reduction factor satisfies the formula: . As the number of target points increases, the time complexity of the HPC algorithm shows an upward trend. Compared with the original HPC algorithm, its time complexity is more sensitive to the number of target points, while the improved HPC algorithm is significantly lower than the original HPC algorithm in time complexity, and shows a more gentle upward trend with the increase in the number of target points, showing stronger robustness. For MIMO radar imaging systems, in order to achieve spatial stereo imaging of moving targets, the radar has a higher spatial resolution, which causes a target to be parsed into multiple points, increasing the processing complexity. When the original HPC algorithm is used to process high-resolution MIMO radar data, its time complexity rises rapidly with the increase in the number of target points, which will lead to low algorithm efficiency and long processing time in practical applications, especially when the number of target points is large. In contrast, the improved HPC algorithm can more effectively maintain algorithm performance and efficiency when processing MIMO radar data with high resolution. Replacement is suitable for the needs of spatial stereo imaging.

[0057] by Figure 8 and Figure 9 Taking the dynamic target shown in the figure as an example, assuming the radar is located to the left rear of the target and the actual speed of the car is approximately 25 km / h, the accuracy of the improved HPC algorithm in resolving target velocity ambiguity is first verified using a 2D point cloud image of the target. Figure 8, is the vehicle point cloud image obtained by using the original HPC speed defuzzification algorithm. From the point cloud image, it can be seen that the speed value after defuzzification by the original HPC algorithm has errors, resulting in a relatively scattered point cloud and a significant deviation between the point cloud image and the actual position of the target. This is because the original HPC algorithm needs to cite all possible hypothetical phase compensation values ​​for all target points when defuzzifying the speed. However, in the MIMO radar system, due to the sidelobe effect and the limitation of speed resolution, the target energy will expand in the distance dimension and Doppler dimension, which will cause errors in the speed after defuzzification. These errors will in turn affect the accuracy of the phase compensation, and ultimately cause the target position to be far away from the actual position. Figure 9 This is a vehicle point cloud image obtained using the improved HPC velocity defuzzification algorithm. By selecting the speed of a representative point to define the target speed and performing phase compensation on the target, this processing method ensures that the target speed is essentially consistent with the true speed and the position is also close to the true position. Table 1 shows that the improved HPC velocity defuzzification algorithm runs faster and the defuzzified speed value is closer to the vehicle's true speed.

[0058] Table 1 Comparison of velocity ambiguity resolution results of the HPC algorithm before and after improvement of moving targets Calculation time of the original HPC algorithm defuzzification results 128s Calculation time of defuzzification results using the improved HPC algorithm 45s Average speed of defuzzified point cloud using the original HPC algorithm 14.4km / h Average speed of defuzzified point cloud using improved HPC algorithm 24.5km / h By applying the above technical solutions, this application studies the velocity ambiguity problem of MIMO millimeter-wave radar systems and the hypothetical phase compensation (HPC) velocity ambiguity resolution algorithm. First, the causes of the velocity ambiguity problem and its impact on target angle estimation are discussed. Subsequently, the basic principles and algorithm implementation process of the hypothetical phase compensation velocity ambiguity resolution algorithm are explained, and through theoretical analysis, it is explained that the original HPC algorithm has a high time complexity problem when applied in MIMO radar systems. Finally, an improved hypothetical phase compensation velocity ambiguity resolution algorithm is proposed. The algorithm obtains target characteristics based on the MIMO radar and proposes a method of selecting target representative points by combining geometric center points with peak points. This method can significantly reduce the number of target points that the algorithm needs to process, ensure the accuracy of speed estimation, accelerate the processing flow of obtaining target speed and angle in MIMO radar imaging processing, and reduce abnormal speed points. Simulation results show that compared with the original HPC velocity ambiguity resolution algorithm, the improved HPC velocity ambiguity resolution algorithm has lower time complexity and has accurate speed estimation capabilities.

[0059] Each embodiment in this specification is described in a related manner. The same or similar parts between the embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0060] The above description is only a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention are included in the scope of protection of the present invention.

Claims

1. A method for resolving velocity ambiguity by assuming phase compensation, characterized in that: The method comprises: S1, defining a phase compensation data structure according to the influencing parameters of the assumed phase compensation solution velocity ambiguity; S2, based on the MIMO millimeter wave radar, the constant false alarm rate detector and the phase compensation data structure, the dynamic target is identified and detected, and the radar data and the Doppler phase difference corresponding to the dynamic target are obtained. φ cfar , all channel data sets S and assumed velocity ambiguity period N; S3, performing Doppler phase compensation on the radar data according to an assumed velocity ambiguity period N and a phase compensation formula to obtain compensated radar data; S4, drawing an angular power spectrum based on the compensated radar data, determining a target peak point according to a peak point in the angular power spectrum, and determining a representative point of the dynamic target and a time complexity of the representative point according to the target peak point and a geometric center of the dynamic target; S5, based on the time complexity corresponding to the representative point and the target speed estimation value V cfar , determine the true speed of the dynamic target V true , and the complexity is reduced by a factor M.

2. The method according to claim 1, characterized in that In step S1, the phase compensation data structure includes: the number of transmitting antennas M TX , Number of receiving antennas M RX , target distance and dynamic target data; in, The dynamic target data includes: target point, number of target points n, representative point, number of representative points a, true speed V true , Phase compensation value φ true , the number of phase compensation values ​​m, the target speed estimate V cfar , Doppler phase difference φ cfar , Maximum speed V max .

3. The method according to claim 1, characterized in that The steps of S2 include: S21, according to the number of transmitting antennas and receiving antennas of the MIMO millimeter wave radar, and the target point of the dynamic target, obtaining a channel data set of each target point detected by the MIMO millimeter wave radar and the constant false alarm rate detector , true phase compensation value φ true and the detection result sequence, and the assumed velocity ambiguity period N; S22, based on the assumed velocity ambiguity period N and the true phase compensation value φ true , determine the Doppler phase difference of each target point φ cfar ; in, The test results are , , M TX is the number of transmitting antennas of the MIMO millimeter-wave radar, M RX is the number of receiving antennas of the MIMO millimeter-wave radar; The Doppler phase difference of the target point , φ true is the actual phase compensation value of each target point φ true , N is the assumed velocity ambiguity period, .

4. The method according to claim 3, characterized in that The step of S2 further includes: Detect the channel of each target point based on the MIMO millimeter wave radar, and obtain the channel data set of each target point , and based on the channel data set of each of the target points and Doppler phase difference , determine the phase value that needs to be compensated ; in, Characterization i The radar data corresponding to the target point obtained in the channel, i is the channel number corresponding to each target point, and i is an integer greater than 1; The phase value of the target point that needs to be compensated .

5. The method according to claim 3, characterized in that The step of S2 further includes: All channel data sets ; Assume that the velocity ambiguity period N is determined according to the number of transmitting antennas of the MIMO millimeter-wave radar, wherein, when the number of transmitting antennas of the MIMO millimeter-wave radar is an odd number, , when the number of transmitting antennas of the MIMO millimeter-wave radar is an even number, ; The radar data is all channel data in the original format obtained based on the channels corresponding to each target point of the dynamic target detected by the MIMO millimeter wave radar, including target points, the number of target points n, representative points, the number of representative points a, and the true speed. V true , Phase compensation value φ true , the number of phase compensation values ​​m, the target speed estimate V cfar , Doppler phase difference and maximum speed V max .

6. The method according to claim 1, characterized in that The steps of S3 include: Channel data set based on the MIMO millimeter wave radar to detect each target point , and assuming a velocity ambiguity period N and a phase compensation formula to perform Doppler compensation calculation on the radar data, to obtain compensated radar data corresponding to each of the target points; in, The compensation formula is: , is the compensated radar data corresponding to each of the target points, is the phase value that needs to be compensated for the target point, i is the channel number corresponding to each target point, i and j All are greater than 1 and less than or equal to integer, is the number of transmitting antennas of the MIMO millimeter-wave radar.

7. The method according to claim 1, characterized in that In step S4, the step of drawing an angular power spectrum based on the compensated radar data, determining a target peak point according to a peak point in the angular power spectrum, and determining a representative point of the dynamic target according to the target peak point and the geometric center of the dynamic target further includes: S41, based on the compensated radar data, according to the assumed velocity ambiguity period N and the actual phase compensation value corresponding to each target point of the dynamic target φ true , plot the angular power spectrum; S42, determining the target peak point and target peak value corresponding to each target point from the peak point in the curve of the angular power spectrum, and sorting the target peaks to determine the maximum peak point and the maximum peak value, as well as the time complexity of the maximum peak point, wherein the time complexity of the maximum peak point is , n is the number of target points, m is the number of phase compensation values; S43, determining a representative point of the dynamic target according to the target peak point and the geometric center of the dynamic target, and determining the time complexity of the representative point according to the time complexity of the maximum peak point.

8. The method according to claim 7, characterized in that The step of S43 further comprises: Connect the target peak point and the geometric center of the dynamic target, determine the center point of the connection and the time complexity, and determine the representative point according to the distance between the target peak point and the center point, wherein the representative point is a target peak point whose distance from the center point is less than a preset value, and the preset value is set according to the target peak point near the center point; Determine the time complexity of the representative point according to the number of representative points of the representative point and the time complexity of the maximum peak point; Among them, the time complexity of the representative point is , a is the number of representative points of the representative points, and m is the number of phase compensation values.

9. The method according to claim 1, characterized in that The step of S5 further comprises: S51, according to the time complexity of the center point of the target peak point and the geometric center line of the dynamic target, the time complexity of the representative point is improved and optimized to obtain the assumed phase compensation speed ambiguity solution algorithm formula and the complexity reduction multiple M, wherein the assumed phase compensation speed ambiguity solution algorithm formula is: , the complexity is reduced by a factor of , n is the number of target points, m is the number of phase compensation values; S52, based on the assumed phase compensation speed ambiguity resolution algorithm formula and the target speed estimation value V cfar Determine the true speed of the dynamic target V true ,in, The true speed of the dynamic target , V cfar is the estimated target speed, V max is the maximum speed of the dynamic target, and N is the assumed speed fuzzy period.

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