A method for eliminating false alarm points based on the combination of radar array sub-arrays

Abstract

The present invention relates to a method for eliminating false alarm points based on the combination of radar array sub-arrays. By dividing the MIMO array of the radar into several sub-arrays and performing independent signal processing on each sub-array, including mixing and filtering the received signal, converting the received signal of each sub-array into a beat frequency signal, then performing fast Fourier transform (FFT) calculation, CFAR detection, and DOA calculation to generate a potential target point cloud; through the sub-array joint detection step, matching and verification are performed on the target point cloud, false alarm points are eliminated, and weighted fusion is performed by combining the detection results of each sub-array to generate a final target list; finally, through the post-processing and tracking step, the density clustering algorithm is used to eliminate isolated points, and the Kalman filter is used to perform multi-frame tracking on the target to further eliminate possible false alarm points. The present invention can significantly reduce the false alarm rate, especially in the multi-path effect and dynamic noise environment, while improving the robustness of target detection and ensuring the stable and reliable operation of the radar in complex scenarios.

Description

Technical Field

[0001] The present invention relates to the technical field of radar, and in particular to a method for eliminating false alarm points based on the combination of radar array sub-arrays. Background Art

[0002] During the use of a radar system, false alarm points may occur due to various influences such as thermal noise (pseudo signals caused by environmental electromagnetic noise), multipath effects (false targets caused by reflection paths), hardware problems (imperfect antenna arrays or signal processing errors), and clutter interference (such as clutter generated by trees, the ground, rain, and snow). However, the real targets will be masked by these false detection results, leading to missed detections and thus affecting the normal operation of the millimeter-wave radar. Therefore, it is important to effectively eliminate false alarm points. Currently, the common methods for eliminating false alarm points are based on Doppler maps or one-dimensional range profiles. The Doppler map effect can distinguish stationary targets from moving targets, reduce false alarm points of stationary targets, and the Doppler map provides the velocity information of the target, which helps to identify and track targets within a specific velocity range, thereby reducing false alarms. However, eliminating false alarm points using the Doppler map is difficult to interpret due to multipath effects in a complex environment, resulting in false alarms. The one-dimensional range profile can provide the structural information of the target, which helps to identify the target type and thus reduce false alarms. Compared with traditional radar systems, the one-dimensional range profile can provide a higher range resolution, which helps to distinguish targets at close range and has a certain resistance to noise and clutter. However, when eliminating false alarm points using the one-dimensional range profile, complex signal processing is required and the computational load is large, and the quality is greatly affected by the target attitude. Different observation angles may result in different range profiles, thereby generating false alarms. These two common methods for eliminating false alarm points are prone to eliminating normal targets, causing missed detections and affecting the normal operation of the millimeter-wave radar. Summary of the Invention

[0003] In view of this, the present invention provides a method for eliminating false alarm points based on the combination of radar array sub-arrays, which effectively solves the problem of high false alarm rate in a multipath effect and dynamic noise environment and ensures stable and reliable operation in complex scenarios.

[0004] To achieve the above object, a method for eliminating false alarm points based on the combination of radar array sub-arrays of the present invention includes the following steps:

[0005] S1. Sub-array division and signal processing;

[0006] The sub - array division divides the MIMO antenna array of the radar into S sub - arrays based on the physical positions of the array elements: element 1 - element 1+S, element 2 - element 2+S, …, element S - element S+S. Each sub - array processes signals independently; signal processing is performed on the S sub - arrays and 1 virtual array, and the signal processing includes frequency - modulated continuous - wave (FMCW) processing, fast Fourier transform (FFT) calculation, constant false - alarm rate (CFAR) detection, and radar direction - of - arrival (DOA) estimation calculation;

[0007] S2. Joint detection of sub - arrays;

[0008] S201. Target matching;

[0009] Perform spatial comparison on the results of constant false - alarm rate (CFAR) detection of all arrays. If there is a target point cloud detected only in a single array and not detected in other arrays, then judge that the target point cloud is a potential false - alarm point;

[0010] S202. Target verification and false - alarm point elimination. Judge the target point cloud according to the sub - array coverage consistency and intensity comparison verification;

[0011] The sub - array coverage consistency means that if the target point cloud appears only in one array and is not verified by other arrays, then judge that the target point cloud is a false - alarm point; The verification includes distance consistency, speed consistency, and spatial consistency. Meeting the distance consistency means that the distance value of the target point cloud has a difference less than a preset threshold among arrays, and the expression is:

[0012] ;

[0013] ;

[0014] where, represents the distance value of the th point cloud in the th array, represents different arrays;

[0015] Meeting the speed consistency means that the speed value of the target point cloud has a difference less than a preset threshold among arrays, and the expression is:

[0016] ;

[0017] where, represents the The velocity value of a point cloud in the th array;

[0018] Meeting the spatial consistency means that the solution angle value of the target point cloud has a difference less than a preset threshold among arrays, and the expression is: ;

[0019] ;

[0020] where, for the same distance, the solution angle value is the angle index, i.e., the abscissa, indicating the abscissa of the th point cloud in the th array;

[0021] The intensity contrast verification means that if the target point cloud has a random intensity distribution, it is determined as a false alarm point, and the reflected intensity of the target point cloud cannot satisfy the following expression:

[0022] ;

[0023] where, indicates the signal-to-noise ratio of the th point cloud in the th array;

[0024] S203. Weighted fusion: Combine the detection intensities and confidences of each array to perform weighted fusion on the verified target point clouds to generate a final list of target point clouds;

[0025] S3. Post-processing and tracking: Use density clustering to cluster the target point clouds to remove isolated points, use a Kalman filter to track the target point clouds, and continue to remove false alarm points through multi-frame fusion.

[0026] Preferably, signal processing is performed on the divided S sub-arrays and 1 virtual full array, and the signal processing includes the following steps:

[0027] S101. Frequency-modulated continuous wave FMCW processing: Use the FMCW technology to process the received signals of each sub-array, including first mixing the received signals and then filtering the signals to convert the received signals of each sub-array into beat frequency signals;

[0028] S102. Fast Fourier transform FFT calculation: Perform fast Fourier calculations on the beat frequency signals in the range and Doppler dimensions respectively to obtain a range-Doppler two-dimensional map;

[0029] S103. Constant False Alarm Rate (CFAR) Detection: Use the CFAR detection algorithm to detect the echo points on the range-Doppler two-dimensional map to obtain potential targets;

[0030] S104. Radar Direction of Arrival (DOA) Estimation and Calculation: Perform DOA estimation on the potential targets to obtain angle information and generate a point cloud list , where, represents the number of point clouds, represents the abscissa of the -th point cloud, represents the ordinate of the -th point cloud, represents the velocity of the -th point cloud, represents the signal-to-noise ratio of the -th point cloud.

[0031] Preferably, the final target point cloud list is generated through weighted fusion as:

[0032] ;

[0033] ;

[0034] where, represents the number of target point clouds that pass the verification, represents the abscissa of the -th array target point cloud, represents the ordinate of the -th array target point cloud, represents the velocity of the -th array target point cloud, represents the signal-to-noise ratio of the -th array target point cloud, represents the weights of different arrays.

[0035] Compared with the prior art, the beneficial effects of the present invention are:

[0036] Through the method of sub-array division and joint detection, the present invention can effectively eliminate the false alarm points caused by thermal noise, multipath effect, hardware problems, and clutter interference. Especially under multipath effect and dynamic noise, the false alarm rate is significantly reduced, avoiding the missed detection problem caused by misdeleting normal targets in the prior art;

[0037] The present invention can perform multi-dimensional verification of distance consistency, velocity consistency, and spatial consistency on the target, and ensure the accuracy and stability of target detection through weighted fusion, enabling the radar to still maintain high detection performance in complex scenarios with a large amount of clutter or dynamic noise;

[0038] The present invention further eliminates possible false alarm points through multi-frame fusion and Kalman filtering technologies, effectively improving the continuity of target tracking. Moreover, the present invention makes full use of the multi-channel information of the radar array to improve the efficiency and accuracy of signal processing. Description of the Drawings

[0039] FIG. 1 is a schematic diagram of the algorithm flow of the present invention;

[0040] Figure 2 is a schematic diagram of the antenna array division of 2T3R in the embodiment of the present invention;

[0041] Figure 3 is an angular diagram with poor spatial consistency in the embodiment of the present invention;

[0042] Figure 4 is an angular diagram with good spatial consistency in the embodiment of the present invention. Detailed Embodiments

[0043] To further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific embodiments, structures, features, and their effects according to the present invention as follows.

[0044] To solve the problems of inaccurate elimination of false alarm points and easy misdeletion of normal targets in the prior art, the present invention provides the following technical solution: a method for eliminating false alarm points based on the combination of sub-arrays of a radar array, including the following steps:

[0045] S1. Sub-array division and signal processing;

[0046] Sub-array division: Divide the MIMO antenna array of the radar into several sub-arrays according to the physical position or angular coverage range of the array elements, and each sub-array processes signals independently;

[0047] As Figure 2 shown, in this embodiment, taking a uniform array of 2T3R (2 transmitting antennas and 3 receiving antennas) as an example, divide the radar MIMO antenna array of 2T3R into 3 sub-arrays based on the physical position of the array elements: array elements 1 - 4, array elements 2 - 5, and array elements 3 - 6. Together with the virtual full array, there are 4 arrays in total. Perform signal processing on the 4 arrays. The signal processing includes the following steps:

[0048] S101. Frequency-modulated continuous wave (FMCW) processing: Use FMCW technology to process the received signals of each sub-array, including first mixing the received signals and then filtering the signals to convert the received signals of each sub-array into beat frequency signals;

[0049] S102. Fast Fourier transform (FFT) calculation: Perform fast Fourier calculations on the beat frequency signals in the range and Doppler dimensions respectively to obtain a range-Doppler two-dimensional map;

[0050] S103, Constant False Alarm Rate (CFAR) Detection: Use the CFAR detection algorithm to detect the echo points on the range-Doppler two-dimensional map to obtain potential targets;

[0051] S104, Radar Direction of Arrival (DOA) Estimation and DOA Calculation: Perform DOA estimation on the potential targets (including Fast Fourier Transform (FFT), Digital Beamforming (DBF), and Direction of Maximum Likelihood (DML) method) to obtain angle information and generate a point cloud list , where, represents the number of point clouds, represents the th abscissa of the point cloud, represents the th ordinate of the point cloud, represents the th velocity of the point cloud, represents the th signal-to-noise ratio of the point cloud.

[0052] S2, Sub-array Joint Detection;

[0053] S201, Target Matching: Compare the results of the CFAR detection of all arrays in space. If there is a target point cloud detected only in a single array and not detected in other arrays, then determine that the target point cloud is a potential false alarm point;

[0054] S202, Target Verification and False Alarm Point Elimination: Judge the target point cloud based on the sub-array coverage consistency and intensity comparison verification;

[0055] The sub-array coverage consistency means that if the target point cloud appears only in one array and is not verified by other arrays, then determine that the target point cloud is a false alarm point; The verification includes range consistency, velocity consistency, and spatial consistency. Meeting the range consistency means that the range value of the target point cloud has a difference less than the preset threshold among arrays, and the expression is:

[0056] ;

[0057] ;

[0058] where, represents the range value of the th point cloud in the th array, represent different arrays;

[0059] Meeting the speed consistency means that the speed values of the target point cloud have a difference less than a preset threshold among the arrays , and the expression is:

[0060] ;

[0061] where, represents the speed value of the th point cloud in the th array;

[0062] According to the field of view of the antenna and the set relationship between sub-arrays, check the spatial consistency of the target point cloud. False alarm points usually appear at certain specific angles or are randomly distributed and cannot pass the verification of all arrays. Meeting the spatial consistency means that the solution angle values of the target point cloud have a difference less than a preset threshold among the arrays , and the expression is:

[0063] ;

[0064] where, represents the abscissa of the th point cloud in the th array;

[0065] The spatial consistency is reflected in the abscissa of the point cloud, that is, the solution angle value. For example, for the uniform radar array and sub-array division of 2T3R shown in Figure 2 , view their angle spectrograms and solution angle values respectively, and observe the spatial consistency. The results are shown in Figure 3 and Figure 4 . The abscissa represents the angle index (taking the number of points of the angle dimension FFT as 128 as an example), and the ordinate represents the energy value of the angle spectrum. Figure 3 The shown angle diagram with poor spatial consistency is a false alarm point, with a distance of 4.68 and an angle index of 54. At this time, the solution angle value of the full array is -0.73125, the solution angle value of sub-array 1 (array elements 1 - 4) is 2.34, the solution angle value of sub-array 2 (array elements 2 - 5) is 0.36562, and that of sub-array 3 (array elements 3 - 6) is -4.0219. It can be seen that the angle indices of each array differ greatly (for the same distance, the angle index represents the solution angle value, that is, the abscissa); Figure 4Shown is an angular diagram with good spatial consistency, that is, the target point, the distance is 1.89, the angular index is 75. At this time, the angle resolution value of the full array is 0.32484, the angle resolution value of sub-array 1 (array elements 1 - 4) is 0.32484, the angle resolution value of sub-array 2 (array elements 2 - 5) is 0.29531, and the angle resolution value of sub-array 3 (array elements 3 - 6) is 0.26578. The angular indices of each array differ slightly;

[0066] The intensity contrast verification refers to the target point cloud shows a random intensity distribution, then it is determined as a false alarm point. This is because in a multi-array, the reflection intensity RCS of a real target should have relatively small fluctuations, and false alarm points usually have a random intensity distribution; if the target point cloud 's reflection intensity (expressed as the signal-to-noise ratio SNR of the point cloud) cannot satisfy the following expression, then it is determined as a false alarm point. The expression is:

[0067] ;

[0068] Among them, represents the th point cloud's signal-to-noise ratio in the th array;

[0069] That is to say, only when the point cloud satisfies:

[0070] ;

[0071] then it is determined that the point cloud is a point cloud of a real target, otherwise it is considered that the point cloud is a false alarm point;

[0072] S203. Weighted fusion: Combine the detection intensity and confidence of each array to perform weighted fusion on the verified target point cloud to generate a final list of target point clouds;

[0073] A high confidence level is manifested as a good detection intensity of the array, that is, a good signal-to-noise ratio SNR of the point cloud. Then, the weight assigned to this array by the final point cloud is higher; After weighted fusion, the generated final list of target point clouds is:

[0074] ;

[0075] Among them, represents the number of verified target point clouds, represents the th array target point cloud's abscissa, represents the th array target point cloud's ordinate, represents the th array target point cloud's speed, represents the th array target point cloud's signal-to-noise ratio, represent the weights of different arrays and satisfy:

[0076] ;

[0077] S3. Post-processing and tracking. Use density clustering to cluster the target point cloud to remove isolated points, use the Kalman filter to track the target point cloud, and continue to remove false alarm points through multi-frame fusion.

[0078] The above are only the preferred embodiments of the present invention, and do not impose any form of limitation on the present invention. Although the present invention has been disclosed above with the preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some changes or modifications to the equivalent embodiments with equivalent changes within the scope of the technical solution of the present invention. However, as long as it does not depart from the technical solution content of the present invention, any brief modifications, equivalent changes and modifications made to the above embodiments according to the technical essence of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. A false alarm point elimination method based on radar array sub-array combination, characterized in that: The following steps are involved: S1, sub-array division and signal processing; The sub-array division is to divide the MIMO antenna array of the radar into S sub-arrays based on the physical position of the array element: array element 1-array element 1+S, array element 2-array element 2+S, ..., array element S-array element S+S, and each sub-array processes the signal independently; Performing signal processing on the S sub-arrays and one virtual array, wherein the signal processing includes frequency modulation continuous wave FMCW processing, fast Fourier transform FFT calculation, constant false alarm rate CFAR detection, and radar angle of arrival estimation DOA calculation; S2, sub-array joint detection; S201, target matching; The results of the constant false alarm rate CFAR detection of all arrays are spatially compared. If there is a target point cloud If it is detected only in a single array but not in other arrays, the target point cloud is judged is a potential false alarm point; S202, target verification and elimination of false alarm points, verifying the target point cloud according to the sub-array coverage consistency and strength comparison Make judgments; The subarray coverage consistency means that if the target point cloud If it only appears in one array and fails to pass the verification of other arrays, the target point cloud is determined The verification includes distance consistency, speed consistency and space consistency. Meeting the distance consistency means that the target point cloud The distance value The difference between the arrays is less than the preset threshold , the expression is: ； ； in, Indicates The point cloud is in The distance values ​​in the array, Represents different arrays; Satisfying the speed consistency means that the target point cloud The difference in speed values ​​between the arrays is less than the preset threshold , the expression is: ； in, Indicates The point cloud is in The velocity values ​​in an array; Satisfying the spatial consistency means that the target point cloud The difference in the solution angle between the arrays is less than the preset threshold , the expression is: ； Among them, for the same distance, the solution angle value is the angle index, that is, the horizontal coordinate, Indicates The point cloud is in The horizontal coordinate in the array; The intensity comparison verification refers to the target point cloud If a random intensity distribution appears, it is determined to be a false alarm point. The target point cloud The reflection intensity cannot satisfy the following expression: ； in, Indicates The point cloud is in The signal-to-noise ratio in the array; S203, weighted fusion: weighted fusion of the verified target point clouds is performed in combination with the detection strength and confidence of each array to generate a final target point cloud list; S3, post-processing and tracking, use density clustering to cluster the target point cloud to eliminate isolated points, use the Kalman filter to track the target point cloud, and continue to eliminate false alarm points through multi-frame fusion.

2. The method for eliminating false alarm points based on radar array subarray combination according to claim 1, characterized in that: Signal processing is performed on the divided S sub-arrays and one virtual full array, and the signal processing includes the following steps: S101, frequency modulated continuous wave (FMCW) processing: using FMCW technology to process the received signal of each subarray, including first mixing the received signal, then filtering the signal, and converting the received signal of each subarray into a beat frequency signal; S102, Fast Fourier Transform (FFT) calculation: The beat frequency signal performs Fast Fourier Transform calculation on the distance and Doppler dimensions respectively to obtain a distance-Doppler two-dimensional graph; S103, constant false alarm rate CFAR detection: using a constant false alarm rate CFAR detection algorithm to detect echo points on the range-Doppler two-dimensional graph to obtain potential targets; S104, radar arrival angle estimation DOA calculation: perform DOA estimation on the potential target, obtain angle information, and generate a point cloud list ,in, Indicates the number of point clouds, Indicates The horizontal coordinate of the point cloud, Indicates The vertical coordinate of the point cloud, Indicates The speed of the point cloud, Indicates The signal-to-noise ratio of a point cloud.

3. The method for eliminating false alarm points based on radar array subarray combination according to claim 1, characterized in that: The final target point cloud list generated after weighted fusion is: ； ； in, represents the number of target point clouds that have passed verification, Indicates The horizontal coordinate of the array target point cloud, Indicates The ordinate of the array target point cloud, Indicates The speed of the array target point cloud, Indicates The signal-to-noise ratio of the array target point cloud, Represents the weights of different arrays.

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