Speed unblurring method of millimeter wave radar, millimeter wave radar and product

Abstract

The application provides a speed deambiguating method of a millimeter wave radar, a millimeter wave radar and a product. The method comprises the following steps: dividing a chirp frequency modulation signal into N chirp groups based on a preset relationship between a distance migration amount of a target in a segmented time and a distance resolution as a grouping rule; performing M-order FFT processing on the N chirp groups to obtain a target spectrum data matrix; wherein M is greater than or equal to N; determining a potential target based on the target spectrum data matrix; determining corresponding data segments of the potential target in each chirp group, respectively, and performing speed deambiguating calculation based on the data segments to determine the real speed of the potential target.

Description

Technical Field

[0001] This application relates to the field of radar technology, and in particular to a velocity deambiguation method for millimeter-wave radar, millimeter-wave radar, and computer program products. Background Technology

[0002] Millimeter-wave radar detects and tracks targets by emitting and receiving electromagnetic waves, simultaneously measuring the target's distance, speed, and angle. Compared to cameras and lidar, millimeter-wave radar offers superior environmental and weather adaptability, functioning well in environments such as strong light, nighttime, heavy rain, fog, and dust. It is widely used in automotive ADAS (Advanced Driving Assistance Systems) and AD (Autonomous Driving) systems. By integrating cameras, lidar, and millimeter-wave radar, the redundancy of intelligent decision-making can be improved, achieving high reliability and safety in ADAS and AD systems.

[0003] Currently, the main frequency band for automotive millimeter-wave radar is 77GHz, corresponding to a wavelength of 3.9mm, while the target speed measurement range can reach 300km / h. This can be determined using the radar speed measurement range formula:

[0004]

[0005] It can be known that T c <11.7us, where T c It is the chirp period, and the requirements for this speed measurement range are usually difficult to meet, so speed deambiguity needs to be used to meet the speed measurement requirements. Summary of the Invention

[0006] To address the existing technical problems, this application provides a velocity deambiguation method for millimeter-wave radar that estimates the true velocity of a target based on the target's range migration effect, as well as a millimeter-wave radar and computer program product.

[0007] Firstly, a velocity deambiguation method for millimeter-wave radar is provided, the method comprising:

[0008] Based on the preset relationship between the target's distance migration and distance resolution within a segmented time period, the chirp frequency modulation signal is divided into N chirp groups.

[0009] Perform an M-order FFT on the N chirp groups to obtain the target spectrum data matrix; where M ≥ N;

[0010] Potential targets are determined based on the target spectrum data matrix;

[0011] The corresponding data segments of the potential targets within each chirp group are determined, and velocity defuzzification calculation is performed based on the data segments to determine the true velocity of the potential targets.

[0012] In a second aspect, a vehicle-mounted millimeter-wave radar is provided, including a millimeter-wave radar processor and a memory, wherein the memory stores a computer program that can be executed by the millimeter-wave radar processor.

[0013] When the computer program is executed by the millimeter-wave radar processor, it implements the velocity deambiguation method for millimeter-wave radar according to any embodiment of this application.

[0014] Thirdly, a computer program product is provided, including a computer program that, when executed by a processor, implements the velocity deambiguation method for millimeter-wave radar according to any embodiment of this application.

[0015] The velocity deambiguation method for millimeter-wave radar provided in the above embodiments obtains target spectrum data by performing FFT processing on all chirp frequency-modulated signals. Potential target extraction is performed by performing FFT processing on all chirp frequency-modulated signals, so the maximum velocity range and SNR of the target are not lost. All chirp frequency-modulated signals are grouped according to grouping rules, and velocity deambiguation calculation is performed based on the corresponding data segment of the potential target in each chirp group. Since the observation time corresponding to each chirp group is short, the range migration effect within each chirp group can be ignored. The same column in the same chirp group contains all target information at the same distance, thus avoiding the target pairing problem. It is convenient to determine the ambiguity of the target based on the relative relationship of the targets in each group of chirp frequency-modulated signals, which helps to improve the accuracy of the target's true velocity estimation.

[0016] The vehicle-mounted millimeter-wave radar and computer program products provided in the above embodiments belong to the same concept as the corresponding millimeter-wave radar velocity de-ambiguation method embodiments, and thus have the same technical effects as the corresponding millimeter-wave radar velocity de-ambiguation method embodiments, which will not be repeated here. Attached Figure Description

[0017] Figure 1 This is a Fast-slow chirp waveform diagram from one embodiment.

[0018] Figure 2 This is a schematic diagram of a MIMO array in one embodiment.

[0019] Figure 3 This is a schematic diagram of the TDM-MIMO waveform in one embodiment.

[0020] Figure 4This is a schematic diagram of the amplitude-frequency result after FFT processing in one embodiment.

[0021] Figure 5 This is a schematic diagram of a radar processing layered structure in one embodiment.

[0022] Figure 6 This is a diagram illustrating an optional application scenario for the velocity deambiguation method of millimeter-wave radar in one embodiment.

[0023] Figure 7 This is a schematic diagram illustrating the working principle of a vehicle-mounted millimeter-wave radar in one embodiment.

[0024] Figure 8 This is a flowchart of a velocity deambiguation method for millimeter-wave radar in one embodiment.

[0025] Figure 9 A schematic diagram showing the result of FFT processing on chirp.

[0026] Figure 10 This is a flowchart of a velocity defuzzification method in an optional specific example.

[0027] Figure 11 for Figure 10 The chirp waveform is shown in the optional specific example.

[0028] Figure 12 for Figure 10 The diagram shows a grouping of chirp in an optional specific example.

[0029] Figure 13 This is a flowchart illustrating the operation of a vehicle-mounted millimeter-wave radar in one embodiment.

[0030] Figure 14 This is a schematic diagram of the structure of a millimeter-wave radar in one embodiment. Detailed Implementation

[0031] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0032] To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limitations on this application. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0033] In the following description, the phrase "some embodiments" refers to a subset of all possible embodiments. It should be noted that "some embodiments" can be the same subset or different subsets of all possible embodiments, and can be combined with each other without conflict.

[0034] In the following description, the terms "first, second, and third" are used merely to distinguish similar objects and do not represent a specific ordering of objects. It is understood that "first, second, and third" may be interchanged in a specific order or sequence where permitted, so that the embodiments of this application described herein can be implemented in an order other than that illustrated or described herein.

[0035] To address the velocity measurement requirements of millimeter-wave radar in deambiguation, the inventors of this application have conducted in-depth research and analysis on the principles and shortcomings of current velocity deambiguation algorithms, classifying them into three main categories:

[0036] First, a speed-expanded algorithm based on the Chinese Remainder Theorem.

[0037] like Figure 1 As shown, the chirp frequency modulation signal is divided into a fast chirp segment and a slow chirp segment. The speed defuzzification principle of the speed extension algorithm based on the Chinese Remainder Theorem is:

[0038] 1.1 Perform velocity and distance dimension FFT on the fast chirp segment to obtain the corresponding distance-velocity map, i.e., RDM1.

[0039] 1.2. Perform velocity and distance dimension FFT on the slow chirp segment to obtain the corresponding distance-velocity map, i.e., RDM2.

[0040] 1.3. When there is only one target, the target's positions on RDM1 are R1 and V1, and the target's positions on RDM2 are R2 and V2.

[0041] 1.4. Based on the radar speed measurement range formula, see Formula 1 below:

[0042]

[0043] Where T c Corresponding to Figure 1 In Tc_1 and Tc_2, assuming the maximum velocity measurement range of the fast chirp segment is v max1 The maximum speed measurement range for the slow chirp section is v. max2 Then, according to the constraint m*v max1 +V1=n*v max2 +V2, where m and n are both integers, can be used to calculate the target's true speed.

[0044] The drawback of this method is:

[0045] 1.1 From the velocity resolution formula, as shown in Formula 2:

[0046]

[0047] Where T f It is the total working time of all chirps used by the velocity dimension FFT. Since the fast chirp segment and the slow chirp segment each occupy a portion of the working time, the velocity resolution is reduced.

[0048] 1.2 Since the signal-to-noise ratio gain of the velocity dimension FFT is positively correlated with the number of chirps used, this method reduces the signal-to-noise ratio gain before detection.

[0049] 1.3 When multiple velocities exist at the same distance, the target velocity calculation will be incorrect due to the failure of target registration on the slow chirp and fast chirp.

[0050] Second, the speed extension algorithm based on the Doppler phase offset compensation assumption.

[0051] The radar uses TDM-MIMO operating mode, such as Figure 2 As shown, the radar antenna's transmitting and receiving arrays employ TDM-MIMO to form a large virtual array, and its transmitted waveform is as follows. Figure 3 As shown. The velocity defuzzification principle of the velocity extension algorithm based on the Doppler phase bias compensation assumption is:

[0052] 2.1 Perform distance and velocity dimension FFT on the data of the four receiving channels corresponding to Tx1.

[0053] 2.2 Perform distance and velocity dimension FFT on the data of the four receiving channels corresponding to Tx2.

[0054] 2.3. Assuming the target's positions on the RDM of Tx1 are R1 and V1, then points on the RDM of 8 receiving channels (4 for Tx1 and 4 for Tx2) with positions R1 and V1 can be selected to form an 8-channel sequence, whose arrangement is as follows: Figure 2 The array format is consistent with that in the previous example.

[0055] 2.4 Since Tx1 and Tx2 do not operate simultaneously, their time interval is Tc. If the target's velocity is v, there will be a phase difference between the four receiving channels of Tx1 and the four receiving channels of Tx2. This phase difference is related to the velocity v, and its formula is as follows: Formula 3:

[0056]

[0057] 2.5. Assume the target's true velocity v r The range is from 0 to 2*v maxThe target's velocity as represented in the RDM is V1. If the target's true velocity is v... r =V1, then the phase compensation required for Tx1 and Tx2 at this time can be calculated, as shown in Formula 4 below:

[0058]

[0059] If the target's true velocity is v r =V1+v max Then, the phase compensation required for Tx1 and Tx2 at this time can be calculated as follows: Formula 5:

[0060]

[0061] 2.6. Perform azimuth-dimensional FFT on two different phase-compensated 8-element sequences. The amplitude-frequency results of the two sequences after FFT are as follows: Figure 4 As shown, if the phase compensation value corresponding to the blue amplitude-frequency diagram is as shown in Formula 6 below:

[0062]

[0063] The velocity of the true target is V1; if the phase compensation value corresponding to the blue amplitude-frequency diagram is as shown in Formula 7:

[0064]

[0065] The velocity of the real target is V1 + v max .

[0066] The drawback of this method is:

[0067] 2.1 Since the signal-to-noise ratio gain of the velocity dimension FFT is positively correlated with the number of chirps used, this method reduces the signal-to-noise ratio gain before detection;

[0068] 2.2 According to the radar speed measurement range formula, as shown in Formula 1 above, the maximum speed measurement range corresponding to a single Tx decreases.

[0069] 2.3 The speed measurement range of this method has a limited extension range.

[0070] Third, the velocity extension algorithm based on the target tracking assumption.

[0071] like Figure 5 As shown, the radar processing flow mainly includes analog front-end (AFE) receiving data, digital front-end (DFE) processing, range-Doppler FFT processing, target detection, target tracking, target classification, and obtaining a target list.

[0072] The velocity defuzzing principle of the velocity extension algorithm based on the target tracking hypothesis is:

[0073] Using the information of the currently detected target, mainly including distance, speed (there are several possible speed scenarios), and angle, we match it with the existing historical trajectory. We pair each of the possible speed scenarios with the historical trajectory to find the speed value that best matches, and this speed value is the target's true speed.

[0074] The drawback of this method is:

[0075] 3.1 There is a target matching problem. When the target is mismatched, the target velocity calculation will be incorrect.

[0076] 3.2 When the target is accelerating, the longer the tracking period, the worse the effect.

[0077] To address the shortcomings of existing velocity deambiguity algorithms, the inventors of this application propose a technical approach for resolving velocity ambiguity in millimeter-wave radar based on target range migration. This approach divides the chirp frequency-modulated signal into N chirp groups based on a preset relationship between the target's range migration and range resolution over a segmented time period. Potential target extraction is achieved by performing FFT processing on all chirp frequency-modulated signals, ensuring that the target's maximum velocity range and SNR (Signal-to-Noise Ratio) gain are not compromised. Furthermore, since range migration effects within the same chirp signal group are negligible, the tracking cycle can be reduced, target pairing issues can be avoided, and the accuracy of estimating the target's true velocity can be improved.

[0078] Please see Figure 6 This diagram illustrates a possible application scenario of the millimeter-wave radar velocity de-ambiguation method provided in one embodiment of this application, applied to a vehicle. The vehicle-mounted millimeter-wave radar 200 is typically installed on both sides of the front of the vehicle 100; common installation locations are not limited to... Figure 6 The forward position shown also includes the front corner, rear corner, and rear corner positions, which are used to detect and perceive the surrounding environmental targets (target 1, target 2, target 3).

[0079] Please see Figure 7 This is a schematic diagram of a vehicle-mounted millimeter-wave radar. This vehicle-mounted millimeter-wave radar mainly consists of four functional parts:

[0080] 1) Antenna array, consisting of a transmitting array and a receiving array.

[0081] 2) MRTR (Millimeter-wave Radar Transceiver), a millimeter-wave radar transceiver module, mainly consists of a transmitting component, a receiving component, and a linear frequency modulated local oscillator component. The MRTR generates and transmits the working waveform signal of the millimeter-wave radar, receives and discretely samples the reflected waveform signal of the target, and sends the sampled reflected waveform signal to the MRP (Millimeter-wave Radar Processor).

[0082] 3) MRP (Millimeter-wave Radar Processor): This processor receives reflected waveform signals from the MRTR and performs digital processing to obtain information such as the distance, velocity, and angle of environmental targets. It then performs target clustering, identification, and trajectory tracking, outputting point cloud-level or target-level detection results. Functionally, the MRP mainly consists of a ranging module, a velocity measurement module, an angle measurement module, and a target clustering, identification, and tracking module. These modules can be implemented using embedded software, FPGA (Field Programmable Gate Array), or AISC (Application Specific Integrated Circuit).

[0083] The velocity deambiguation method for millimeter-wave radar provided in this application embodiment is mainly implemented in the MRP velocity measurement module. The implementation form, composition method, or module division method of MRP does not affect the use of the velocity deambiguation method for millimeter-wave radar provided in this application embodiment.

[0084] 4) Read / write memory, used to store millimeter-wave radar operating data, including pre-stored operating parameter data and process data.

[0085] It should be noted that the configuration or composition of the various functional parts of the vehicle-mounted millimeter-wave radar described above does not affect the use of the velocity de-ambiguity method for millimeter-wave radar provided in this application embodiment; in terms of application scenarios, the velocity de-ambiguity method for millimeter-wave radar provided in this application embodiment can be applied to all applications that use electromagnetic waves for target direction finding, and is not limited to vehicle-mounted millimeter-wave radar applications.

[0086] Please see Figure 8 The present application provides a velocity deambiguation method for millimeter-wave radar, comprising the following steps:

[0087] S101, based on the preset relationship between the target's distance migration and distance resolution within a segmented time period as the grouping rule, divide the chirp frequency modulation signal into N chirp groups.

[0088] Range migration refers to the phenomenon where the echo signal of the same target falls into different range cells at different times due to the relative motion between the radar and the target. Range resolution refers to the minimum distance at which the radar can distinguish two targets in a radar image when other quantities, such as azimuth or velocity, are the same but their distances are different. Segment time refers to the time required to determine the length of a chirp group.

[0089] The process involves dividing all chirp FM signals into equal groups. The number of chirp groups should not be too large, as this would result in low SNR gain for each group and significant noise-induced amplitude fluctuations within each group. Conversely, the number of chirp groups should not be too small, as this would lead to excessively long durations for each group, causing significant distance migration for high-speed targets within each group. Based on a preset relationship between the target's distance migration and range resolution within a segment, the chirp FM signals are divided into N chirp groups. This ensures that the SNR gain of each group is not compromised, the signal amplitude is protected from noise, and the duration of each group is not too long. For high-speed targets, the difference in distance migration within each chirp group is negligible. Furthermore, each chirp group contains information on all targets at the same distance within the same column, reducing the tracking cycle and avoiding target pairing issues.

[0090] Optionally, the grouping rule is that the target's distance migration within a segment time t is less than the range resolution, and the segment time t can be used to determine the length of the chirp group; or, the grouping rule is that the target's distance migration within a segment time t is greater than the product of the range resolution and a first ratio, and less than the product of the range resolution and a second ratio; wherein the first ratio is 1 / 3, and the second ratio is 1 / 2; the distance migration is the product of the target's maximum possible speed and the segment time t. Preferably, the number of groups is a power of 2.

[0091] In an optional example, assuming the radar range resolution is R_res and the maximum possible velocity of the target is Vel_max, then within this time interval t, 1 / 3*R_res <Vel_max*t<1 / 2*R_res。

[0092] S103, Perform M-order FFT processing on the N chirp groups to obtain the target spectrum data matrix; where M≥N.

[0093] FFT (Discrete Fourier Transform) processing converts time-domain signals to frequency-domain signals using Discrete Fourier Transform. The target spectrum data matrix is ​​a two-dimensional data matrix obtained by performing M-order FFT processing on N chirp groups, that is, performing M-order FFT processing on all chirp frequency-modulated signals. The horizontal and vertical axes of the data matrix are usually distance and velocity.

[0094] S105, determine potential targets based on the target spectrum data matrix.

[0095] Based on the target spectrum data matrix, each point corresponds to a distance and a velocity. The target is filtered according to the magnitude of the amplitude of each point, and potential targets are selected based on the location of the points with larger amplitudes.

[0096] It should be noted that there can be one or more potential targets. Each potential target corresponds to a coordinate point in the target spectral data matrix, thus each potential target corresponds to a range and a velocity.

[0097] S107, determine the corresponding data segments of the potential target in each chirp group, perform velocity defuzzification calculation based on the data segments, and determine the true velocity of the potential target.

[0098] Within the same chirp group, the target's distance migration can be ignored. For each chirp group's spectral data matrix, each column contains information on all targets at the same distance (if any).

[0099] The velocity deambiguation method for millimeter-wave radar provided in the above embodiments obtains target spectrum data by performing FFT processing on all chirp frequency-modulated signals. Potential target extraction is performed by performing FFT processing on all chirp frequency-modulated signals, so the maximum velocity range and SNR of the target are not lost. All chirp frequency-modulated signals are grouped according to grouping rules, and velocity deambiguation calculation is performed based on the corresponding data segment of the potential target in each chirp group. Since the observation time corresponding to each chirp group is short, the range migration effect within each chirp group can be ignored. The spectrum data matrix corresponding to the same chirp group contains all target information at the same distance in the same column, thus avoiding the target pairing problem. It is convenient to determine the ambiguity of the target based on the relative relationship of the targets in each group of chirp frequency-modulated signals, which helps to improve the accuracy of the target's true velocity estimation.

[0100] In some embodiments, S107 includes:

[0101] Based on the distance R of the potential target, determine the matching data segment of the potential target in each chirp group, and perform M-order FFT processing on the matching data segment respectively;

[0102] Based on the velocity V of the potential target, determine the matching data point of the potential target in each of the matching data segments;

[0103] The velocity ambiguity of the potential target is calculated based on the matching data points;

[0104] The true speed of the potential target is determined based on the speed ambiguity and the speed V.

[0105] Based on the target spectrum data matrix obtained by performing an M-order FFT on all chirp FM signals, all potential targets are screened out, and the range R and velocity V of each potential target are determined. For each potential target, the matching data segment of the potential target within each chirp group is determined based on the range R, and the matching data point of the potential target within the matching data segment is determined by combining the velocity V. Based on the matching data points corresponding to the potential targets determined by the chirp FM signals of each chirp group, the velocity ambiguity of the potential targets is calculated using the characteristics of each chirp group for the same potential target.

[0106] In the above embodiments, potential targets are determined by performing FFT processing on all chirp frequency modulation signals. Then, by dividing the chirp frequency modulation signals into a reasonable number of chirp groups, the corresponding data of each potential target in each chirp group is determined. Thus, the relative relationship of the targets in each chirp group can be used to solve the target velocity ambiguity, which can reduce the observation cycle, effectively retain the maximum velocity measurement range, and improve the accuracy of the target's true velocity estimation.

[0107] In some embodiments, each chirp group is a first two-dimensional data matrix, where the horizontal axis of the first two-dimensional data matrix represents ADC samples and the vertical axis represents chirp indices.

[0108] Step S103 includes:

[0109] A distance FFT is performed between the ADC samples in the first two-dimensional data matrix to obtain a second two-dimensional data matrix; the horizontal axis of the second two-dimensional data matrix is ​​the distance, and the vertical axis is the chirp index.

[0110] The second two-dimensional data matrix is ​​subjected to an M-order velocity FFT to obtain a third two-dimensional data matrix; the horizontal axis of the third two-dimensional data matrix is ​​distance, and the vertical axis is velocity.

[0111] Please see Figure 9 In the first two-dimensional data matrix, the horizontal axis represents ADC samples, and the vertical axis represents chirp indices. The k-th row represents all sampling points of the k-th chirp. Each point in the first two-dimensional matrix contains information about all targets. In the second two-dimensional data matrix, the horizontal axis represents distance, and the vertical axis represents chirp indices. Different columns of the second two-dimensional data matrix correspond to different distances, and each chirp in the same column contains information about all targets (if any) at that distance. In the third two-dimensional data matrix, the horizontal axis represents distance, and the vertical axis represents velocity. When the amplitude of a point in the third two-dimensional data matrix is ​​significantly larger than the amplitudes of other points, it can be considered that there is a potential target at the corresponding distance and velocity.

[0112] In the above embodiments, by performing distance-dimensional FFT and velocity-dimensional FFT processing on N chirp groups, and by performing FFT processing on all chirp frequency-modulated signals to obtain the target spectrum data matrix for extracting potential targets, it can be ensured that the maximum velocity range and SNR of the extracted potential targets are not lost.

[0113] In some embodiments, in step S107, calculating the velocity ambiguity of the potential target based on the matching data points includes:

[0114] Based on the amplitude of the matched data points, determine the amplitude ratio of the reference matched data point relative to each of the matched data points;

[0115] The amplitude ratio is fitted with the Sinc function to calculate the velocity ambiguity of the potential target.

[0116] After identifying the potential target, based on the potential target's distance R and velocity V, such as Figure 9 As shown in the matrix diagram on the right (the third two-dimensional data matrix), the red and green boxes correspond to two potential targets, one for distance and one for velocity. Selecting the first potential target (corresponding to...) Figure 9 The red box in the right-hand matrix diagram represents all chirps (corresponding to) the distance R of each segment of the matrix. Figure 9 The intermediate matrix diagram (a segment of the red column in the second two-dimensional data matrix) is subjected to M-order FFT processing. Matching data points with velocity V are extracted from each segment of the FFT processing result, and their amplitudes are calculated and denoted as a1, a2, a3, and a4. Using a1 as the reference matching data point, a1 is divided by a1, a2, a3, and a4 respectively to obtain the amplitude ratios b1, b2, b3, and b4 of the reference matching data point relative to each matching data point. By fitting b1, b2, b3, and b4 to the sinc function, the velocity ambiguity of the potential target is calculated. The true velocity of the potential target is then determined based on the velocity ambiguity and velocity V.

[0117] It should be noted that for each of all potential targets, the above steps are performed separately to determine the true speed of each potential target.

[0118] In the above embodiments, after identifying potential targets, since all chirp frequency modulation signals are divided into N chirp groups, the distance migration is negligible for each segment. Therefore, the impact of distance migration on each segment does not need to be considered. Furthermore, the chirp used by the velocity deambiguation method is the same chirp group, so the target pairing problem does not need to be considered. Under the premise of effectively preserving the maximum velocity measurement range and SNR, the observation period can be reduced, and the accuracy of the target's true velocity estimation can be improved.

[0119] In some embodiments, the velocity deambiguation method for millimeter-wave radar includes:

[0120] Based on different velocity ambiguities and their corresponding amplitude ratios, an ambiguity mapping table is formed;

[0121] The step of calculating the velocity ambiguity of the potential target based on the matching data points further includes:

[0122] Based on the amplitude ratio of the matching data points of the current potential target, a search is performed in the ambiguity mapping table to determine the velocity ambiguity of the matching target.

[0123] In this process, a large number of historical data on fitting amplitude ratios to the Sinc function are used to select typical amplitude ratio values ​​under different ambiguity conditions and store them to form an ambiguity mapping table. In the subsequent velocity deambiguation process, the method of fitting the amplitude ratio of the unknown target to the Sinc function can be replaced by a table lookup method. The amplitude ratios b1, b2, b3, and b4 calculated from the matching data points in the N chirp groups corresponding to the velocity V of the unknown target are compared with the values ​​in the ambiguity mapping table to determine the velocity ambiguity of the unknown target.

[0124] In an optional example, when the target distance is 45 meters and the true speed is 0 m / s, the target speed determined based on the detection results is 0 m / s, and b1≈b2≈b3≈b4≈1, indicating that the target's true speed is 0 m / s. When the target distance is 45 meters and the true speed is 56 m / s, the target speed determined based on the detection results is 0 m / s, and b1=1, b2=1.23, b3=2.09, b4=6.70, indicating that there is a speed ambiguity for the target. By using speed deambiguation, the true speed should be determined to be 56 m / s.

[0125] In the above embodiments, by forming an ambiguity mapping table and using a lookup table method to determine the velocity ambiguity of unknown targets, it is beneficial to reduce the computational load of the velocity deambiguation method of millimeter-wave radar and improve computational efficiency.

[0126] In some embodiments, step S107, determining the true velocity of the potential target based on the velocity ambiguity and the velocity V, includes:

[0127] Based on the following velocity calculation formulas, Formulas 8 and 9, the true velocity of the potential target is determined;

[0128] v = V + k * v max ; (Formula 8)

[0129]

[0130] Where v refers to the true velocity, k refers to the velocity ambiguity, and T... c λ refers to the period of the chirp frequency modulation signal, and λ refers to the wavelength of the chirp frequency modulation signal.

[0131] The velocity V of the potential target, i.e., the velocity V calculated by the radar and the maximum velocity v measured by the radar. max The relationship between the actual speed of the target and the actual speed satisfies Equation 8.

[0132] Thus, by determining the ambiguity of the target's velocity, the target's true velocity can be determined.

[0133] To gain a more comprehensive understanding of the velocity deambiguation method for millimeter-wave radar provided in the embodiments of this application, please refer to [the relevant documentation / reference]. Figures 10 to 12 ,by Figure 2 Taking the structure of the vehicle-mounted millimeter-wave radar shown as an example, the main working process of the vehicle-mounted millimeter-wave radar and the process of the speed deambiguation method of the millimeter-wave radar are explained.

[0134] Taking the 1T1R FMCW radar system as an example, the radar operating waveform is as follows: Figure 11 As shown.

[0135] Based on the radar waveform parameters, as shown in Table 1 below:

[0136] Serial Number matter value 1 Chirp's starting operating frequency 76.5GHz 2 Frequency modulation bandwidth of a single chirp 150MHz 3 Effective working time of a single chirp 20us 4 Chirp cycle 35us 5 ADC sampling points 128 6 chirp number 512

[0137] Divide all chirp frequency-modulated signals into 4 equal segments, forming N (N=4) chirp groups, such as Figure 12 As shown. Velocity defuzzification methods include:

[0138] S11, perform FFT processing on N chirp groups.

[0139] S12, determine the distance R and velocity V of the potential target.

[0140] In steps S11 to S12, the MRP velocity measurement module first performs an M-order FFT on N chirps, where M ≥ N, and determines the distance R and velocity V of the potential target based on the FFT results of all chirps.

[0141] S131, select all chirps in the first segment with a distance of R and perform M-order FFT processing.

[0142] S132, select a point with velocity V and calculate its amplitude, which is denoted as a1.

[0143] S141, select all chirps in the second segment with a distance of R and perform M-order FFT processing.

[0144] S142, select a point with velocity V and calculate its amplitude, which is denoted as a2.

[0145] S151, select all chirps in the third segment with a distance of R and perform M-order FFT processing.

[0146] S152, select a point with velocity V and calculate its amplitude, which is denoted as a3.

[0147] S161, select all chirps in the second segment with a distance of R and perform M-order FFT processing.

[0148] S162, select a point with velocity V and calculate its amplitude, which is denoted as a4.

[0149] S17, divide a1 by a1, a2, a3, and a4 respectively to obtain b1, b2, b3, and b4.

[0150] S18 solves the speed ambiguity by fitting b1, b2, b3, and b4 to the sinc function.

[0151] In steps S131 to S18, select all chirps of each segment of the distance R of the first potential target, perform M-order FFT processing on them, and calculate the amplitudes a1, a2, a3, and a4 of the points with velocity V in the FFT processing results of each chirp segment. Calculate the corresponding amplitude ratios b1, b2, b3, and b4, and perform velocity defuzzification.

[0152] For the remaining potential targets, repeat steps S131 to S18 to determine the velocity ambiguity of the remaining potential targets and calculate the true velocity.

[0153] The velocity de-ambiguation method provided in the above embodiments estimates the true velocity of a target based on the distance migration effect of the target, and has at least the following effects:

[0154] First, an FFT is performed on all chirps before extracting the target, therefore the target's maximum velocity range v max Neither SNR nor other parameters are lost. Compared to known second-type defuzzification algorithms (velocity extension algorithms based on the Doppler phase bias compensation assumption), the maximum velocity range v is [missing information]. max The speed resolution is relatively large compared to the known first-class defuzzification algorithm (the speed extension algorithm based on the Chinese remainder theorem); and there is no loss in signal-to-noise ratio compared to the known first-class and second-class defuzzification algorithms.

[0155] Second, dividing all chirps into several parts avoids the target matching problem. Furthermore, because the duration of each chirp is shortened, the distance migration effect within each chirp can be ignored. Compared to known third-type defuzzification algorithms (velocity extension algorithms based on target tracking assumptions), this method does not need to consider target pairing issues, reduces the tracking cycle, and achieves high velocity defuzzification accuracy.

[0156] Third, the main approach is to fit the relative relationship of the target amplitude in each chirp to the sinc function to estimate the step size between amplitudes, and then determine the ambiguity of the target by determining the size of this step size.

[0157] The typical workflow of an automotive millimeter-wave radar using the velocity deambiguation method proposed in the embodiments of this application is as follows: Figure 13 As shown, it includes the following steps:

[0158] S211, the vehicle-mounted millimeter-wave radar is powered on and initialized, reading and sending out operating parameters.

[0159] S212, the vehicle-mounted millimeter-wave radar received the command to start working.

[0160] S213, the millimeter-wave radar chip generates a linear frequency modulated continuous wave according to waveform parameters and transmits the signal to the transmitting and receiving components.

[0161] The S214 millimeter-wave radar chip transmits linear frequency modulated continuous waves through a transmitting antenna array.

[0162] The S215 millimeter-wave radar chip receives the echo reflected from the target through a receiving antenna array.

[0163] S216, a millimeter-wave radar chip, is used for ADC sampling of the received wave.

[0164] The S217 millimeter-wave radar chip performs ranging processing on the ADC data.

[0165] The S218 millimeter-wave radar chip processes ranging data for velocity measurement.

[0166] The S219 millimeter-wave radar chip performs clustering and tracking processing on the angle measurement results.

[0167] The S220 millimeter-wave radar chip outputs point cloud information or trajectory information.

[0168] The S221 millimeter-wave radar chip continuously detects targets until the conditions for ceasing operation are met.

[0169] The velocity deambiguation method proposed in this application embodiment can be executed by a millimeter-wave radar chip, specifically in step S218.

[0170] In another aspect, please refer to the embodiments of this application. Figure 2 and Figure 14 Furthermore, a millimeter-wave radar is provided, including a millimeter-wave radar processor 201 and a memory 202. The memory 202 stores a computer program that can be executed by the millimeter-wave radar processor 201; when the computer program is executed by the millimeter-wave radar processor 201, it implements the velocity de-ambiguation method of the millimeter-wave radar described in any embodiment of this application and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0171] In another aspect, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the velocity de-ambiguation method for millimeter-wave radar in any embodiment of this application and achieves the same technical effect. To avoid repetition, it will not be described again here.

[0172] In another aspect, this application also provides a computer-readable storage medium storing a computer program. When executed by a processor, the computer program implements the various processes of the above-described millimeter-wave radar velocity de-ambiguation method embodiment and achieves the same technical effect. To avoid repetition, it will not be described again here.

[0173] The computer-readable storage medium mentioned above includes, for example, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0174] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0175] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0176] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A velocity deambiguation method for millimeter-wave radar, characterized in that, The method includes: Based on the preset relationship between the target's distance migration and distance resolution within a segmented time period as the grouping rule, the chirp frequency modulation signal is divided into N chirp groups; wherein, the distance migration is the product of the target's maximum possible velocity and the segmented time t, and the same column within the same chirp group contains all target information at the same distance; Perform an M-order FFT on the N chirp groups to obtain the target spectrum data matrix; where M ≥ N; Potential targets are determined based on the target spectrum data matrix; The corresponding data segments of the potential targets within each chirp group are determined, and velocity defuzzification calculation is performed based on the data segments to determine the true velocity of the potential targets.

2. The velocity deambiguation method for millimeter-wave radar according to claim 1, characterized in that, The step of determining the corresponding data segments of the potential target within each chirp group, and performing velocity defuzzification calculation based on the data segments to determine the true velocity of the potential target includes: Based on the distance R of the potential target, determine the matching data segment of the potential target in each chirp group, and perform M-order FFT processing on the matching data segment respectively; Based on the velocity V of the potential target, determine the matching data point of the potential target in each of the matching data segments; The velocity ambiguity of the potential target is calculated based on the matching data points; The true speed of the potential target is determined based on the speed ambiguity and the speed V.

3. The velocity deambiguation method for millimeter-wave radar according to claim 2, characterized in that, Each chirp group is a first two-dimensional data matrix, where the horizontal axis of the first two-dimensional data matrix represents the ADC sample and the vertical axis represents the chirp index. The step of performing an M-order FFT on the N chirp groups to obtain the target spectrum data matrix includes: A distance FFT is performed between the ADC samples in the first two-dimensional data matrix to obtain a second two-dimensional data matrix; the horizontal axis of the second two-dimensional data matrix is ​​the distance, and the vertical axis is the chirp index. The second two-dimensional data matrix is ​​subjected to an M-order velocity FFT to obtain a third two-dimensional data matrix; the horizontal axis of the third two-dimensional data matrix is ​​distance, and the vertical axis is velocity.

4. The velocity deambiguation method for millimeter-wave radar according to claim 2, characterized in that, Solving the velocity ambiguity of the potential target based on the matched data points includes: Based on the amplitude of the matched data points, determine the amplitude ratio of the reference matched data point relative to each of the matched data points; The amplitude ratio is fitted with the Sinc function to calculate the velocity ambiguity of the potential target.

5. The velocity deambiguation method for millimeter-wave radar according to claim 4, characterized in that, Also includes: Based on different velocity ambiguities and their corresponding amplitude ratios, an ambiguity mapping table is formed; The step of calculating the velocity ambiguity of the potential target based on the matching data points further includes: Based on the amplitude ratio of the matching data points of the current potential target, a search is performed in the ambiguity mapping table to determine the velocity ambiguity of the matching target.

6. The velocity deambiguation method for millimeter-wave radar according to claim 2, characterized in that, Determining the true velocity of the potential target based on the velocity ambiguity and the velocity V includes: The true velocity of the potential target is determined based on the following velocity calculation formula; ； ； in, This refers to actual speed. This refers to speed ambiguity, This refers to the period of the chirp frequency modulation signal. It refers to the wavelength of the chirp frequency modulation signal.

7. The velocity deambiguation method for millimeter-wave radar according to claim 1, characterized in that, The grouping rule is that the distance migration of the target within a segment time t is less than the distance resolution, and the length of the chirp group is determined based on the segment time t.

8. The velocity deambiguation method for millimeter-wave radar according to claim 1, characterized in that, The grouping rule is that the distance migration of the target within a segment time t is greater than the product of the distance resolution and the first ratio, and less than the product of the distance resolution and the second ratio. The first ratio is 1 / 3, and the second ratio is 1 / 2.

9. A millimeter-wave radar, characterized in that, It includes a millimeter-wave radar processor and a memory, wherein the memory stores a computer program that can be executed by the millimeter-wave radar processor; When the computer program is executed by the millimeter-wave radar processor, it implements the velocity deambiguation method for millimeter-wave radar as described in any one of claims 1 to 8.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the velocity deambiguation method for millimeter-wave radar as described in any one of claims 1 to 8.

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