Adaptive parameter adjustment radar two-dimensional constant false alarm rate detection method

Abstract

The invention provides an adaptive parameter adjustment radar two-dimensional constant false alarm rate detection method, and belongs to the field of radar data processing, and the method comprises the steps: obtaining an RD image of a radar; extracting a plurality of points to be detected in the RD image; constructing a detection area of each to-be-detected point to obtain a plurality of detection areas; and determining a target window of the detection area according to the signal amplitude of each detection area, and performing CFAR detection on the detection area according to the target window. According to the adaptive parameter adjustment radar two-dimensional constant false alarm detection method provided by the invention, the reliability of radar two-dimensional constant false alarm detection can be improved.

Description

Technical Field

[0001] This application belongs to the field of radar data processing technology, and more specifically, relates to an adaptive parameter adjustment radar two-dimensional constant false alarm rate detection method. Background Technology

[0002] Multi-target detection is a crucial step in radar signal processing. The classic Cell-Averaging Constant False Alarm Rate (CA-CFAR) method typically determines the detection threshold by calculating the mean signal strength of windows surrounding the target window. However, this method may set the detection threshold too high in environments with uneven clutter intensity, leading to missed detections of weak targets.

[0003] The Order Statistic Constant False Alarm Rate (OS-CAFR) algorithm can address some of the shortcomings of CA-CFAR. It sorts the background window signal data and then selects the k-th sample as the detection threshold based on the statistics, which improves the algorithm's anti-clutter capability to a certain extent.

[0004] However, the traditional OS-CFAR algorithm uses a fixed-size window when performing background data statistics, which makes it difficult for the detection window to cover the entire detection area, resulting in a decrease in the reliability of constant false alarm rate detection. Summary of the Invention

[0005] The purpose of this application is to provide an adaptive parameter adjustment method for radar two-dimensional constant false alarm rate (CFAR) detection, so as to improve the reliability of radar two-dimensional CFAR detection.

[0006] A first aspect of this application provides an adaptive parameter adjustment radar two-dimensional constant false alarm rate (CFAR) detection method, comprising:

[0007] Obtain the radar's RD map;

[0008] Extract multiple points to be detected from the RD map;

[0009] Construct the detection area for each point to be detected, resulting in multiple detection areas;

[0010] The target window for each detection area is determined based on the signal amplitude of each detection area, and CFAR detection is performed on the detection area based on the target window.

[0011] A second aspect of this application provides an adaptive parameter adjustment radar two-dimensional constant false alarm rate (CFAR) detection device, comprising:

[0012] The data acquisition module is used to acquire the radar's RD map;

[0013] The data extraction module is used to extract multiple points to be detected in the RD diagram;

[0014] The detection construction module is used to construct the detection region for each point to be detected, resulting in multiple detection regions.

[0015] The detection module is used to determine the target window of each detection area based on the signal amplitude of each detection area, and to perform CFAR detection on the detection area based on the target window.

[0016] A third aspect of this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the steps of the above-described adaptive parameter adjustment radar two-dimensional constant false alarm rate detection method.

[0017] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the adaptive parameter adjustment radar two-dimensional constant false alarm rate detection method described above.

[0018] The beneficial effects of the adaptive parameter adjustment radar two-dimensional constant false alarm rate detection method and device, electronic device, and readable storage medium provided in this application embodiment are as follows:

[0019] The embodiments of this application adopt a closed-loop process of "RD map extraction - detection area construction - adaptive window CFAR detection". The adaptive adjustment not only ensures that the CFAR detection of each detection point is based on the optimal reference background, effectively reducing the false alarm rate and false detection rate, but also adapts to the complex signal distribution differences in the RD map. It maintains stable detection performance in multi-target and clutter fluctuation scenarios, which greatly improves the environmental adaptability and result reliability of radar constant false alarm detection. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 A flowchart illustrating an adaptive parameter adjustment radar two-dimensional constant false alarm rate (CFAR) detection method provided in an embodiment of this application;

[0022] Figure 2A flowchart illustrating an adaptive parameter adjustment radar two-dimensional constant false alarm rate (CFAR) detection method provided in another embodiment of this application;

[0023] Figure 3 A schematic diagram illustrating target detection and target density determination provided for embodiments of this application;

[0024] Figure 4 A schematic diagram of window size adjustment based on target density provided for an embodiment of this application;

[0025] Figure 5 This is a diagram illustrating the traditional OS-CFAR detection effect provided in an embodiment of this application.

[0026] Figure 6 The detection effect diagram of the adaptive parameter adjustment radar two-dimensional constant false alarm rate detection method provided in the embodiments of this application;

[0027] Figure 7 This is a structural block diagram of an adaptive parameter adjustment radar two-dimensional constant false alarm rate (CFAR) detection device provided in an embodiment of this application;

[0028] Figure 8 This is a schematic block diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0029] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0030] To make the objectives, technical solutions, and advantages of this application clearer, the following description will be provided in conjunction with the accompanying drawings and specific embodiments.

[0031] Generally, the traditional OS-CFAR algorithm uses a fixed-size window when performing background data statistics. In densely populated detection areas, if the detection window is too small, it will affect the sorting results, raise the detection threshold, and ultimately misjudge some targets with weak signal amplitudes as noise, resulting in missed alarms. In sparsely populated areas, if the detection window is too large, it will include a lot of clutter data in the calculation, causing the final detection threshold to be lowered, resulting in false alarms.

[0032] Furthermore, when performing edge region detection in the RD map, the traditional OS-CFAR algorithm fails to obtain complete background window data and directly abandons the detection of these regions, resulting in missed edge targets and reducing the completeness of target detection by the radar system.

[0033] Therefore, existing radar multi-target detection algorithms have obvious limitations. When facing multi-target distributed scenarios, it is difficult to dynamically adjust the detection parameters according to the target density, and the insufficient parameter adaptability can easily lead to missed targets. At the same time, there is a lack of effective data completion methods to address the data missing problem in the edge region of the radar signal, which often results in edge targets being missed. It is impossible to meet the detection needs of both multi-target scenarios and edge regions. This application provides an adaptive parameter adjustment radar two-dimensional constant false alarm rate detection method to solve the above problems. The embodiments of this application are described in detail below.

[0034] Please refer to Figure 1 , Figure 1 This is a flowchart illustrating an adaptive parameter adjustment radar two-dimensional constant false alarm rate (CFAR) detection method according to an embodiment of this application. The method may include steps S101 to S104.

[0035] S101: Obtain the radar's RD map.

[0036] The RD diagram, or Range-Doppler spectrum, is a core two-dimensional visualization and analysis tool in radar signal processing and target detection, used to simultaneously characterize the range and radial velocity information of a target.

[0037] This application embodiment can perform pulse compression processing on the large raw echo signal received by the radar, and after filtering out interference, an RD map reflecting the signal distribution can be obtained. Simultaneously, detection parameters can be initialized, including the number of protection units, the size of the local target statistical region, two background window specifications adapted to different scenarios, and a fixed false alarm rate, to facilitate subsequent detection.

[0038] S102: Extract multiple points to be detected from the RD map.

[0039] The embodiments of this application can traverse the RD diagram in sequence to extract multiple points to be detected in the RD diagram.

[0040] S103: Construct the detection area for each point to be detected, resulting in multiple detection areas.

[0041] In some embodiments of this application, the target detection point can be determined to belong to an edge region or a non-edge region based on its location; the target detection point can be any one of a plurality of detection points.

[0042] Specifically, edge regions or non-edge regions can be distinguished based on the position of the target point in the velocity dimension. Edge regions can include left and right edge regions.

[0043] Subsequently, multiple detection zones can be constructed based on the different locations of the target points to be detected.

[0044] S104: Determine the target window of each detection area based on the signal amplitude of each detection area, and perform CFAR detection on the detection area based on the target window.

[0045] In this embodiment, after obtaining multiple detection regions, the size of the target window for each detection region can be determined based on the signal amplitude at the center of each region. Specifically, the signal amplitude at the center of each detection region can be used to determine whether the region is sparse or dense. For dense regions, the size of the target window can be reduced to improve detection accuracy. For sparse regions, the size of the target window can be kept constant or increased to improve detection efficiency while maintaining accuracy.

[0046] After obtaining the target window for each detection area, CFAR detection can be performed on that detection area based on the target window corresponding to each detection area.

[0047] As can be seen from the above, the embodiments of this application, through the closed-loop process of "RD map extraction - detection area construction - adaptive window CFAR detection", adaptively adjust not only to ensure that the CFAR detection of each detection point is based on the optimal reference background, effectively reducing the false alarm rate and false detection rate, but also to adapt to the complex signal distribution differences in the RD map, maintaining stable detection performance in multi-target and clutter fluctuation scenarios, and greatly improving the environmental adaptability and result reliability of radar constant false alarm detection.

[0048] In one embodiment of this application, a detection region is constructed for each point to be detected, resulting in multiple detection regions, including:

[0049] The location of the target detection point determines whether it belongs to the edge region or the non-edge region; the target detection point can be any one of multiple detection points.

[0050] If the target point to be detected belongs to a non-edge region, then the detection area corresponding to the target point is constructed by taking the target point to be detected as the center and the region within a preset distance of the target point to be detected.

[0051] If the target detection point belongs to the edge region, then the region within a preset distance of the target detection point in the RD map is the first region to be combined, and the detection region corresponding to the target detection point is constructed based on the first region to be combined.

[0052] In this embodiment of the application, after traversing all the points to be detected in the RD diagram in sequence, a local area of ​​fixed size can be extracted with each point to be detected as the detection area corresponding to that point.

[0053] For non-edge regions, the detection region can be directly constructed. Since only general regions exist in the RD map, non-edge regions can be expanded using mirrored data.

[0054] Specifically, the detection area corresponding to the target detection point is constructed based on the first region to be combined, including:

[0055] The first region to be combined is symmetrically divided to obtain the second region to be combined. The first and second regions to be combined are then combined to construct the detection region corresponding to the target detection point.

[0056] In this embodiment, the edge region first extracts one side of the valid signal to form a half-window (i.e., the first region to be combined), and then obtains the other half-window (i.e., the second region to be combined) by mirroring, thus symmetrically completing the missing data. Subsequently, the first region to be combined and the second region to be combined can be combined to obtain the detection area of ​​the edge region.

[0057] Non-edge regions can directly extract a complete background window centered on the point to be detected, ensuring that no background data is missing.

[0058] In some embodiments of this application, the preset amplitude threshold is half of the maximum signal amplitude at each point in the corresponding detection area.

[0059] This application embodiment can count the number of points within the detection area whose signal amplitude exceeds half of the maximum amplitude of the area, and adjust the background window size, i.e., the target window, accordingly. For detection areas with dense targets, a smaller target window can be used and the protection units reduced; for detection areas with sparse targets, a larger target window can be used and the protection units increased.

[0060] Figure 2 A flowchart illustrating an adaptive parameter adjustment radar two-dimensional constant false alarm rate (CFAR) detection method according to another embodiment of this application is shown below. Figure 2 As shown, the complete execution process of this application embodiment can be as follows:

[0061] S1: Radar echo data preprocessing and parameter preparation.

[0062] The raw echo signal received by the radar is pulse-compressed to filter out redundant interference and generate a range-velocity two-dimensional matrix (RD map) that reflects the signal distribution. At the same time, detection parameters are initialized, including the number of protection units, the size of the local target statistical region, two background window specifications adapted to different scenarios, and a fixed false alarm rate, to facilitate subsequent detection.

[0063] S2: Adaptive window adjustment based on target density.

[0064] Traverse all points to be detected in the RD diagram sequentially. Extract a local region of fixed size centered on each point. Count the number of points within the region whose signal amplitude exceeds half of the maximum amplitude of the region. Adjust the background window size accordingly. Use a smaller window and reduce the protection unit in densely populated areas, and use a larger window and increase the protection unit in sparsely populated areas.

[0065] S3: Adaptive window adjustment based on target density.

[0066] Traverse all points to be detected in the RD diagram sequentially. Extract a local region of fixed size centered on each point. Count the number of points within the region whose signal amplitude exceeds half of the maximum amplitude of the region. Adjust the background window size accordingly. Use a smaller window and reduce the protection unit in densely populated areas, and use a larger window and increase the protection unit in sparsely populated areas.

[0067] S4: Background statistics and target detection decision.

[0068] The signal data within the background window is sorted, and a detection threshold is calculated based on the preset false alarm rate. The signal amplitude of the point to be detected is compared with the threshold; if it exceeds the threshold, it is determined to be a target; otherwise, it is determined to be noise or clutter. After traversing all the points to be detected, the target location is marked on the RD diagram, and the final detection result is output.

[0069] For example, this application may mainly include the following two parts:

[0070] T1: Adaptive window adjustment based on target density.

[0071] T11: Before performing CFAR detection, the RD diagram must first be processed.

[0072] First, each detection point can be assigned a detection area (e.g., a 3×3 area). Then, a simple threshold judgment is performed on the detection area to obtain the number of suspected targets.

[0073] Specifically, the number of points within a statistical area whose signal amplitude exceeds half of the maximum amplitude in the area is counted, and these points are marked as suspected targets.

[0074] If the number of suspected targets is less than 2, the detection area is considered a sparse area. In this case, to accurately estimate the background noise level, a standard large window size, such as a 21×21 window, should be used for CFAR detection, while ensuring that the number of protection units is appropriate.

[0075] If the number of suspected targets is greater than or equal to 2, the detection area is considered a dense area. In this case, to reduce mutual interference between targets, the size of the CFAR detection window should be reduced, for example, from the original standard 21×21 window to an 11×11 window. Correspondingly, the number of protection units should also be changed accordingly to ensure that the window to be detected is always located at the center of the CFAR detection area.

[0076] T2: Half-window compensation process for edge regions.

[0077] When the point to be detected is located in the edge region of the RD diagram (the beginning and end of the distance and velocity dimensions), edge targets will be missed because the background window data cannot be obtained.

[0078] When the point to be detected is located in the left edge region (i.e., the index value of the velocity dimension is less than or equal to the number of protection units), the window data of the right part of the detection point is first obtained, and then the data is mirrored and symmetrically expanded into complete window data, thereby avoiding the problem of missed detection due to missing edge data.

[0079] When the point to be detected is located in the right edge region (i.e., the index value of the velocity dimension is greater than or equal to the number of protection units), the window data of the left part of the detection point is first obtained, and then the data is mirrored and symmetric to expand it into complete window data.

[0080] Figure 3 This is a schematic diagram illustrating target detection and target density determination provided in an embodiment of this application. Figure 4 This is a schematic diagram of window size adjustment based on target density provided in an embodiment of this application. Figure 5 This is a diagram illustrating the traditional OS-CFAR detection effect provided in an embodiment of this application. Figure 6 The detection effect diagram of the adaptive parameter adjustment radar two-dimensional constant false alarm rate detection method provided in the embodiments of this application is shown in the figure. Figures 3 to 6 As shown, this application embodiment verifies the multi-target detection performance of a typical radar echo scenario containing multiple targets and edge regions through simulation, comparing the multi-target detection performance of the method provided in this application embodiment with that of the traditional OS-CFAR algorithm. Simulation results show that:

[0081] 1. In dense areas (with ≥2 suspected targets in some areas), traditional OS-CFAR is prone to missing some weak targets due to interference from nearby targets because of the fixed window. However, the embodiment of this application successfully detects all targets in dense areas by adaptively switching small windows, thus eliminating the problem of missing targets in dense areas in traditional algorithms.

[0082] 2. In edge regions, traditional OS-CFAR cannot detect edge targets due to missing background data. This application's embodiment achieves complete detection of edge targets through half-window mirror compensation, solving the edge detection blind spot problem of traditional algorithms.

[0083] The simulation results above intuitively demonstrate that the method provided in this application embodiment outperforms the traditional OS-CFAR in detection performance in multi-target and edge region scenarios, fully verifying its effectiveness and superiority.

[0084] The embodiments of this application employ an adaptive window adjustment strategy based on target density. This strategy adjusts the window size according to the target distribution density, reducing mutual interference between targets in densely populated areas, improving the accuracy of detection threshold estimation, and reducing the false alarm rate. In sparsely populated areas, the window can be appropriately enlarged to achieve more accurate estimation of background noise, effectively reducing the false alarm rate and significantly improving the radar's detection performance in different target density scenarios.

[0085] This application provides a half-window compensation method for edge regions. By performing a mirror symmetry operation on the edge data, the detection data is made more complete, effectively solving the problem of detection blind spots in edge regions, improving the completeness of target detection, and expanding the detection coverage of the radar.

[0086] In summary, the method provided in this application is a radar multi-target two-dimensional constant false alarm rate (CFAR) detection method based on adaptive parameter adjustment. By optimizing window size and edge region processing, it comprehensively improves the radar's detection capability in multi-target scenarios and edge regions.

[0087] Corresponding to the adaptive parameter adjustment radar two-dimensional constant false alarm rate detection method in the above embodiment, Figure 7 This is a structural block diagram of an adaptive parameter adjustment radar two-dimensional constant false alarm rate (CFAR) detection device according to an embodiment of this application. For ease of explanation, only the parts relevant to the embodiment of this application are shown. References Figure 7 The adaptive parameter adjustment radar two-dimensional constant false alarm rate detection device 20 includes:

[0088] Data acquisition module 201 is used to acquire the radar's RD map;

[0089] Data extraction module 202 is used to extract multiple points to be detected in the RD diagram;

[0090] The detection construction module 203 is used to construct the detection region for each point to be detected, resulting in multiple detection regions.

[0091] The detection module 204 is used to determine the target window of each detection area based on the signal amplitude of each detection area, and to perform CFAR detection on the detection area based on the target window.

[0092] In one embodiment of this application, the detection construction module 203 is used to determine whether the target detection point belongs to an edge region or a non-edge region based on the location of the target detection point; the target detection point is any one of a plurality of detection points; if the target detection point belongs to a non-edge region, then the detection region corresponding to the target detection point is constructed with the target detection point as the center and the region within a preset distance of the target detection point.

[0093] In one embodiment of this application, the detection construction module 203 is used to construct a detection area corresponding to the target detection point if the target detection point belongs to the edge region, with the target detection point as the center and the area within a preset distance of the target detection point in the RD map as the first to be combined region, and construct the detection area corresponding to the target detection point according to the first to be combined region.

[0094] In one embodiment of this application, the detection construction module 203 is used to symmetrically divide the first region to be combined to obtain the second region to be combined, and combine the first region to be combined and the second region to be combined to construct the detection region corresponding to the target detection point.

[0095] In one embodiment of this application, the detection module 204 is used to determine the signal amplitude of each point in each detection area, and to determine the target window of the detection area based on the comparison between the signal amplitude of each point and a preset amplitude threshold.

[0096] In one embodiment of this application, the detection module 204 is configured to, for each detection area, if the number of points in the detection area exceeding a preset amplitude threshold exceeds a preset number, use a window of a first size as the target window; if the number of points in the detection area exceeding the preset amplitude threshold does not exceed the preset number, use a window of a second size as the target window; wherein, the first size is smaller than the second size.

[0097] In one embodiment of this application, the preset amplitude threshold is half of the maximum signal amplitude at each point in the corresponding detection area.

[0098] See Figure 8 , Figure 8 This is a schematic block diagram of an electronic device provided according to an embodiment of this application. Figure 8The electronic device 300 in this embodiment may include one or more processors 301, one or more input devices 302, one or more output devices 303, and one or more memories 304. The processors 301, input devices 302, output devices 303, and memories 304 communicate with each other via a communication bus 305. The memories 304 store computer programs, including program instructions. The processors 301 execute the program instructions stored in the memories 304. For example, the functions of the data acquisition module 201, data extraction module 202, detection construction module 203, and detection module 204 can be implemented.

[0099] It should be understood that, in the embodiments of this application, the processor 301 may be a central processing unit (CPU), but it may also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0100] Input device 302 may include a touchpad, a fingerprint sensor (for collecting the user's fingerprint information and fingerprint orientation information), a microphone, etc., and output device 303 may include a display (LCD, etc.), a speaker, etc.

[0101] The memory 304 may include read-only memory and random access memory, and provides instructions and data to the processor 301. A portion of the memory 304 may also include non-volatile random access memory.

[0102] In specific implementations, the processor 301, input device 302, and output device 303 described in the embodiments of this application can execute the implementation method described in the adaptive parameter adjustment radar two-dimensional constant false alarm rate detection method provided in the embodiments of this application, or they can execute the implementation method of the electronic device described in the embodiments of this application, which will not be repeated here.

[0103] In another embodiment of this application, a computer-readable storage medium is provided. This computer-readable storage medium stores a computer program, which includes program instructions. When executed by a processor, the program instructions implement all or part of the processes in the methods described above. Alternatively, the computer program can instruct related hardware to complete the process. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include any entity or device capable of carrying computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0104] The computer-readable storage medium can be an internal storage unit of the electronic device in any of the foregoing embodiments, such as a hard disk or memory of the electronic device. The computer-readable storage medium can also be an external storage device of the electronic device, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on the electronic device. Furthermore, the computer-readable storage medium can include both internal and external storage units of the electronic device. The computer-readable storage medium is used to store computer programs and other programs and data required by the electronic device. The computer-readable storage medium can also be used to temporarily store data that has been output or will be output.

[0105] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this application.

[0106] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the electronic devices and units described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0107] In the several embodiments provided in this application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and other division methods may exist in actual implementation. Furthermore, the mutual couplings or direct couplings or communication connections shown or discussed can be indirect couplings or communication connections through some interfaces or units, or they can be electrical, mechanical, or other forms of connection.

[0108] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of the embodiments of this application, depending on actual needs.

[0109] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and such modifications or substitutions should all be covered 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 self-adaptive parameter adjustment radar two-dimensional constant false alarm rate detection method, characterized in that, include: Obtain the radar's RD map; Extract multiple points to be detected from the RD diagram; Construct the detection area for each point to be detected, resulting in multiple detection areas; The target window of each detection area is determined based on the signal amplitude of each detection area, and CFAR detection is performed on the detection area based on the target window.

2. The adaptive parameter adjustment radar two-dimensional constant false alarm rate (CFAR) detection method as described in claim 1, characterized in that, The process involves constructing a detection region for each point to be detected, resulting in multiple detection regions, including: The target detection point is determined to belong to an edge region or a non-edge region based on its location; the target detection point is any one of the plurality of detection points. If the target detection point belongs to a non-edge region, then the detection area corresponding to the target detection point is constructed with the target detection point as the center and the region within a preset distance of the target detection point.

3. The adaptive parameter adjustment 2D CFAR detection method of radar according to claim 2, wherein, Also includes: If the target detection point belongs to the edge region, then the region within a preset distance of the target detection point in the RD map is the first region to be combined, and the detection region corresponding to the target detection point is constructed based on the first region to be combined.

4. The adaptive parameter adjustment 2D CFAR detection method of radar according to claim 3, wherein, The step of constructing the detection region corresponding to the target detection point based on the first region to be combined includes: The first region to be combined is symmetrically divided to obtain the second region to be combined. The first region to be combined and the second region to be combined are combined to construct the detection region corresponding to the target detection point.

5. The adaptive parameter adjustment radar two-dimensional constant false alarm rate detection method as described in claim 1, characterized in that, Determining the target window of a detection area based on the signal amplitude of each detection area includes: Determine the signal amplitude at each point in each detection area, and determine the target window for that detection area based on the comparison between the signal amplitude at each point and the preset amplitude threshold.

6. The adaptive parameter adjustment radar two-dimensional constant false alarm rate detection method as described in claim 5, characterized in that, Determining the target window of the detection area based on the comparison between the signal amplitude at each point and a preset amplitude threshold includes: For each detection area, if the number of points in the detection area that exceed the preset amplitude threshold exceeds the preset number, then a window of the first size is used as the target window; if the number of points in the detection area that exceed the preset amplitude threshold does not exceed the preset number, then a window of the second size is used as the target window; wherein, the first size is smaller than the second size.

7. The adaptive parameter adjustment radar two-dimensional constant false alarm rate detection method as described in claim 5 or 6, characterized in that, The preset amplitude threshold is half of the maximum signal amplitude at each point in the corresponding detection area.

8. A two-dimensional constant false alarm rate (CFAR) detection device with adaptive parameter adjustment, characterized in that, include: The data acquisition module is used to acquire the radar's RD map; The data extraction module is used to extract multiple points to be detected in the RD diagram; The detection construction module is used to construct the detection region for each point to be detected, resulting in multiple detection regions. The detection module is used to determine the target window of each detection area based on the signal amplitude of each detection area, and to perform CFAR detection on the detection area based on the target window.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 9. When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 7.

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