A target detection method and a target detection device

Abstract

The application provides a target detection method and a target detection device, and solves the problems of low target search precision, low data rate and easy loss of targets when a search radar detects low, slow and small targets. The target detection method comprises the following steps: predicting a predicted distance of a tracking target based on a target distance and a target speed issued by a search radar; performing target point detection in a preset distance section based on the predicted distance; matching the detected target points based on the target distance and the target speed to obtain a best matching point; performing reliable continuous processing on the best matching point; and performing sliding window filtering processing on the data of the continuously processed best matching point.

Description

Technical Field

[0001] This invention relates to the field of target detection technology, and specifically to a target detection method and a target detection device. Background Technology

[0002] In existing low-speed and small target detection and handling systems, search radar is usually used to search for and detect low-speed and small targets. When a low-speed and small target is detected, the target information is directly guided to the photoelectric tracking. However, search radar has problems such as low target search accuracy, low data rate, and easy target loss when detecting low-speed and small targets, which affects the photoelectric tracking effect. Summary of the Invention

[0003] In view of this, embodiments of the present invention provide a target detection method and a target detection device, which solves the problems of low target search accuracy, low data rate, and easy target loss when search radar detects low, slow, and small targets.

[0004] In a first aspect, an embodiment of the present invention provides a target detection method, comprising:

[0005] Based on the target distance and target speed transmitted by the search radar, the distance to the tracked target is predicted to obtain the predicted distance;

[0006] Target point detection is performed within a preset distance range based on the predicted distance;

[0007] The detected target points are matched based on the target distance and the target speed to obtain the optimal matching point;

[0008] The optimal matching point is reliably and continuously processed;

[0009] Sliding window filtering is applied to the best matching point data after continuous processing.

[0010] In one embodiment, the target point detection within a preset distance range based on the predicted distance includes: radar signal processing for moving target detection by a microwave flow detector followed by constant false alarm rate (CFAR) detection to detect the target point.

[0011] In one embodiment, the step of detecting a target point within a preset distance range based on the predicted distance includes: using the predicted distance as the center point, performing target detection within the preset distance range.

[0012] In one implementation, matching the detected target points based on the target distance and the target speed to obtain the optimal matching point includes:

[0013] The Doppler channel of the search target is obtained based on the target distance and the target velocity;

[0014] Based on the predicted distance, target point detection is performed within a preset distance range to obtain the target point distance and the Doppler channel of the target point;

[0015] The target distance is matched with the target point distance, and the Doppler channel of the search target is matched with the Doppler channel of the target point for the first time.

[0016] If the first match is successful, a second match is performed based on the target distance obtained after the first match to obtain the actual distance of the currently tracked target.

[0017] In one embodiment, the first matching of the target distance with the target point distance, and the Doppler channel of the search target with the Doppler channel of the target point, includes:

[0018] Determine whether the difference between the target distance and the target point distance is less than a preset distance;

[0019] Determine whether the difference between the Doppler channel of the search target and the Doppler channel of the target point is less than a preset value;

[0020] If the difference between the target distance and the target point distance is less than a preset distance, and the difference between the Doppler channel of the search target and the Doppler channel of the target point is less than a preset value, then the first match is successful.

[0021] In one implementation, if the first match is successful, performing a second match based on the target distance obtained after the first match includes:

[0022] If the number of targets after the first match is Nt1, then Nt1 > 0; and the target distances after the first match are Dis1, Dis2, ..., Dis... n Then, a second matching will be performed, and the matching criteria for the second matching are as follows:

[0023] Dis_cha(n) = ABS(Dis n -Dis)

[0024] Where Dis_cha(n) is the absolute value of the difference between the target distance after each first match and the target distance; the Dis value at min(Dis_cha(1), Dis_cha(2), ... Dis_cha(n)) is the absolute value of the difference between the target distance and the target distance after each first match. n The actual distance to the target being tracked.

[0025] In one implementation, the reliable and continuous processing of the optimal matching point includes:

[0026] After the second successful match, if Cnt < 10, then Cnt = Cnt + 1;

[0027] If the first match fails, and if Cnt > 0, then Cnt = Cnt - 1;

[0028] Where Cnt is the number recorded by the target continuous reliable counter.

[0029] In one implementation, the reliable continuous processing of the optimal matching point includes: when the first matching fails, if Cnt>5, then re-predicting the distance to the tracked target; if Cnt<5, then stopping the prediction of the distance to the tracked target.

[0030] In one implementation, the sliding window filtering process on the continuously processed best matching point data includes:

[0031] The azimuth and elevation miss distances of the current target are obtained by using the sum-difference amplitude angle measurement method in azimuth and elevation.

[0032] Determine if the number of frames in the radar signal is greater than the preset number of frames;

[0033] If so, the preset number of frames is used as a time interval for sliding window processing to obtain the average miss distance;

[0034] If not, cover based on the existing data average or using the first value.

[0035] Secondly, an embodiment of the present invention provides a target detection device, comprising:

[0036] The range prediction module is used to predict the distance to the tracked target based on the target distance and target speed sent by the search radar.

[0037] The target point detection module is used to detect target points within a preset distance range based on the predicted distance;

[0038] A matching module is used to match the detected target points based on the target distance and the target speed to obtain the optimal matching point;

[0039] The data processing module is used to reliably and continuously process the optimal matching points; and to perform sliding window filtering on the continuously processed optimal matching point data.

[0040] This invention provides a target detection method and apparatus. The method involves predicting the target distance based on target distance and velocity data from a search radar; detecting target points within a preset distance range based on the predicted distance; matching the detected target points with the target distance and velocity data to obtain an optimal matching point; performing reliable continuous processing on the optimal matching point; and applying sliding window filtering to the continuously processed optimal matching point data. This target detection method has the following advantages: it increases the target detection probability by lowering the signal processing detection threshold based on target distance and velocity information from the search radar; it uses the Doppler channel of a Doppler radar and distance information to achieve optimal matching of the tracked target; it employs a reliable continuous processing method to achieve continuous target tracking even when no target is detected for multiple consecutive frames; and it filters the target azimuth and elevation miss distances, improving the smoothness of azimuth and elevation. Attached Figure Description

[0041] Figure 1 The diagram shown is a flowchart of a target detection method provided in an embodiment of the present invention.

[0042] Figure 2 The diagram shown is a schematic diagram of a tracking distance segment threshold setting according to an embodiment of the present invention.

[0043] Figure 3 The diagram shown is a schematic flowchart of a target matching process provided by an embodiment of the present invention.

[0044] Figure 4 The diagram shown is a schematic diagram of a sliding window filter for azimuth and pitch miss distance provided in an embodiment of the present invention.

[0045] Figure 5 The image shown is a test image of target tracking provided by an embodiment of the present invention.

[0046] Figure 6 The diagram shown is a structural schematic of a target detection device according to an embodiment of the present invention. Detailed Implementation

[0047] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0048] This embodiment provides a target detection method, such as Figure 1 As shown, the target detection method includes:

[0049] Step 01: Based on the target distance and target speed sent by the search radar, predict the distance to the tracked target to obtain the predicted distance.

[0050] Because the search radar has a lower data rate than the tracking radar, when the search radar sends the target distance and speed of the target to the tracking radar, the tracking radar needs to perform distance prediction based on this target distance and speed to obtain the predicted distance Dis.

[0051] When the target is flying relative to the tracking radar towards the station: Dis = Dis_x - V_x*t;

[0052] When the target is flying relative to the tracking radar back station: Dis = Dis_x + V_x*t;

[0053] Where Dis_x is the target distance; V_x is the target velocity; and t is the target flight time.

[0054] Step 02: Detect target points within a preset distance range based on the predicted distance.

[0055] Furthermore, the target point detection within a preset distance range based on the predicted distance includes: radar signal processing for moving target detection by a microwave flow detector followed by constant false alarm rate (CFAR) detection to detect the target point.

[0056] Furthermore, the step of detecting target points within a preset distance range based on the predicted distance includes: using the predicted distance as the center point, performing target detection within the preset distance range.

[0057] The tracking radar in this invention is a pulse Doppler radar with an operating frequency of F, a pulse repetition period PRI of T, and uses N-point accumulation. The processing range is from Dmin to Dmax. The radar signal processing performs MTD (Microwave Flow Detector) moving target detection followed by constant false alarm rate (CFAR) detection to detect the target point. If a fixed threshold is used for detection across the entire range, it is easy to miss the target when it is in a range with strong interference. To improve the detection probability of the tracked target, the predicted distance Dis of the currently predicted tracked target point is used as the center point, and the detection threshold is set from Dis-150 to Dis+150. Figure 2 As shown.

[0058] Step 03: Match the detected target points based on the target distance and the target speed to obtain the optimal matching point.

[0059] Furthermore, such as Figure 3 As shown, the step of matching the detected target points based on the target distance and the target speed to obtain the optimal matching point includes:

[0060] Step 031: Obtain the Doppler channel of the search target based on the target distance and the target velocity.

[0061] As can be seen from step 02 above, the radar's velocity resolution is... Where λ is the wavelength, c is the speed of light, F is the operating frequency, and T is the pulse repetition period, using N-point accumulation. According to the above formula, without considering velocity ambiguity, if the target's velocity is V, then when the target moves away from the tracking radar, the target's Doppler channel is... When the target approaches the tracking radar, the target's Doppler channel Each optimal match is performed based on the calculated NT and Dis and the detection results from step 02 above.

[0062] Step 032: Based on the predicted distance, perform target point detection within a preset distance range to obtain the target point distance and the Doppler channel of the target point.

[0063] The process of detecting the target point within a preset distance range based on the predicted distance to obtain the target point distance and the Doppler channel of the target point is the same as step 031 above, and will not be repeated here.

[0064] Step 033: Perform the first matching of the target distance with the target point distance, and the Doppler channel of the search target with the Doppler channel of the target point.

[0065] The first matching of the target distance with the target point distance, and the Doppler channel of the search target with the Doppler channel of the target point, includes:

[0066] Determine whether the difference between the target distance and the target point distance is less than a preset distance;

[0067] Determine whether the difference between the Doppler channel of the search target and the Doppler channel of the target point is less than a preset value;

[0068] If the difference between the target distance and the target point distance is less than a preset distance, and the difference between the Doppler channel of the search target and the Doppler channel of the target point is less than a preset value, then the first match is successful.

[0069] Specifically, the first matching criterion is:

[0070] ABS(Dis-Dis_J)<30........1)

[0071] ABS(N T -N_J)<5........2)

[0072] Where ABS(Dis-Dis_J) < 30 indicates that the difference between the target distance transmitted by the tracking radar and the target distance at the current detection point is less than 30 meters; ABS(N T -N_J)<5 indicates that the difference between the Doppler channel corresponding to the speed sent by the tracking radar and the actual detected Doppler channel is less than 5; if both 1) and 2) above are satisfied, it means that the first match is successful.

[0073] Step 034: If the first match is successful, perform a second match based on the target distance obtained after the first match to obtain the actual distance of the currently tracked target.

[0074] Wherein, if the first match is successful, a second match is performed based on the target distance obtained after the first match, including:

[0075] If the number of targets after the first match is Nt1, then Nt1 > 0; and the target distances after the first match are Dis1, Dis2, ..., Dis... n Then, a second matching will be performed, and the matching criteria for the second matching are as follows:

[0076] Dis_cha(n) = ABS(Dis n -Dis)

[0077] Where Dis_cha(n) is the absolute value of the difference between the target distance after each first match and the target distance; the Dis value at min(Dis_cha(1), Dis_cha(2), ... Dis_cha(n)) is the absolute value of the difference between the target distance and the target distance after each first match. n The actual distance to the target being tracked.

[0078] Step 04: Perform reliable continuous processing on the optimal matching point.

[0079] When a tracking radar detects a target, it will not only receive the echo signal of the real target, but also be affected by clutter signals and noise. At certain distances, it may not be able to detect the target to be tracked. In this case, the target information needs to be processed as a continuous process.

[0080] Furthermore, the reliable continuous processing of the optimal matching point includes:

[0081] After the second successful match, if Cnt < 10, then Cnt = Cnt + 1;

[0082] If the first match fails, and if Cnt > 0, then Cnt = Cnt - 1;

[0083] Where Cnt is the number recorded by the target continuous reliable counter.

[0084] The reliable continuous processing of the optimal matching point includes: when the first matching fails, if Cnt > 5, then the distance to the tracked target is re-predicted; if Cnt < 5, then the distance to the tracked target is stopped from being predicted.

[0085] Step 05: Perform sliding window filtering on the continuously processed optimal matching point data. The tracking radar uses the sum-difference amplitude angle measurement method to obtain the current target's azimuth and elevation miss distances. Due to the high data rate of the tracking radar, the azimuth and elevation miss distances after a single detection may fluctuate frequently, placing significant operational pressure on the turntable that rotates with the tracking radar. To reduce the frequency of radar miss distance fluctuations and ensure stable target tracking, sliding window filtering is performed on the target data to obtain the target position.

[0086] like Figure 4 As shown, the sliding window filtering process for the continuously processed best matching point data includes:

[0087] Step 051: Use the sum-difference amplitude angle measurement method to obtain the current target's azimuth and elevation miss distance in azimuth and elevation.

[0088] Step 052: Determine whether the number of radar signal frames is greater than the preset number of frames;

[0089] If so, the preset number of frames is used as a time interval for sliding window processing to obtain the average miss distance;

[0090] If not, cover based on the average of existing data or by using the first value.

[0091] In pulse Doppler radar signal processing, one frame represents one accumulation period. For example, if the pulse repetition period of a pulse Doppler radar is 100µs and the number of accumulation points is 128, then the time of one frame = 100µs * 128. If the misses in the first 25 frames are a1-a25, then the average value is (a1 + a2... + a25) / 25. If there are not enough data for 25 frames, i.e., not 25 miss values, then use as many as possible; for example, if there are only 10 values, use 10 misses. The values ​​are arranged in the order they appear, a1, a2, a3, a4... with the first value being the most recent.

[0092] Actual target tracking test results are shown below. Figure 5 As shown.

[0093] This embodiment provides a target detection device 100, such as Figure 6 As shown, the target detection device 100 includes: a distance prediction module 10, a target point detection module 20, a matching module 30, and a data processing module 40.

[0094] in,

[0095] The range prediction module 10 is used to predict the distance to the tracked target based on the target distance and target speed sent by the search radar.

[0096] The target point detection module 20 is used to detect target points within a preset distance range based on the predicted distance;

[0097] The matching module 30 is used to match the detected target points based on the target distance and the target speed to obtain the optimal matching point;

[0098] The data processing module 40 is used to perform reliable continuous processing on the optimal matching point; and to perform sliding window filtering on the continuously processed optimal matching point data.

[0099] This embodiment provides an electronic device, which may be a mobile phone, computer, or tablet computer, etc., including a memory and a processor. The memory stores a computer program, which, when executed by the processor, implements the method for real-time storage and monitoring of cloud-based intelligent node status files as described in Embodiment 1. It is understood that the electronic device may further include an input / output (I / O) interface and communication components.

[0100] The processor is used to execute all or part of the steps in the method for real-time storage and monitoring of cloud-based intelligent node status files, as described in Embodiment 1. The memory is used to store various types of data, which may include, for example, instructions for any application or method in the electronic device, as well as application-related data.

[0101] The processor can be implemented as an Application Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Programmable Logic Device (PLD), Field Programmable Gate Array (FPGA), controller, microcontroller, microprocessor, or other electronic components, and is used to execute the method for real-time storage and monitoring of cloud-based intelligent node status files in Embodiment 1 above.

[0102] The memory can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0103] This embodiment also provides a computer-readable storage medium. The functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. If the functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium.

[0104] Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention.

[0105] The aforementioned storage media include: flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, server, APP application store, and other media that can store program verification codes, on which computer programs are stored.

[0106] 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, or a combination of computer software and electronic hardware. 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 implementation should not be considered beyond the scope of this invention. It will be clearly understood by those skilled in the art that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes 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 systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the shown or discussed mutual couplings, direct couplings, or communication connections may be through some interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms. 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, i.e., 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 this embodiment according to actual needs.

[0108] The basic principles of this application have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this application are merely examples and not limitations, and should not be considered as essential features of each embodiment of this application. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the application to the necessity of employing the aforementioned specific details for implementation.

[0109] The block diagrams of devices, apparatuses, devices, and systems involved in this application are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner.

[0110] It should also be noted that in the apparatus, equipment, and methods of this application, the components or steps can be disassembled and / or recombined. These disassemblies and / or recombinations should be considered as equivalent solutions of this application.

[0111] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of this application. Therefore, this application is not intended to be limited to the aspects shown herein, but rather to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0112] In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified. All directional indications (such as up, down, left, right, front, back, top, bottom, etc.) in the embodiments of this application are only used to explain the relative positional relationships and movement of the components in a specific posture (as shown in the figures). If the specific posture changes, the directional indication will also change accordingly. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices.

[0113] Furthermore, the reference to "embodiment" herein means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0114] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention 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 the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims. The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A target detection method, characterized in that, include: Based on the target distance and target speed transmitted by the search radar, the distance to the tracked target is predicted to obtain the predicted distance; Target point detection is performed within a preset distance range based on the predicted distance; The detected target points are matched based on the target distance and the target speed to obtain the optimal matching point; The optimal matching point is reliably and continuously processed; The target location is obtained by performing sliding window filtering on the continuously processed best matching point data; The step of matching the detected target points based on the target distance and the target speed to obtain the optimal matching point includes: The Doppler channel of the search target is obtained based on the target distance and the target velocity; Based on the predicted distance, target point detection is performed within a preset distance range to obtain the target point distance and the Doppler channel of the target point; The target distance is matched with the target point distance, and the Doppler channel of the search target is matched with the Doppler channel of the target point for the first time. If the first match is successful, a second match is performed based on the target distance obtained after the first match to obtain the actual distance of the currently tracked target.

2. The target detection method according to claim 1, characterized in that, The target point detection within a preset distance range based on the predicted distance includes: radar signal processing for moving target detection by a microwave flow detector, followed by constant false alarm rate (CFAR) detection, in order to detect the target point.

3. The target detection method according to claim 1, characterized in that, The step of detecting a target point within a preset distance range based on the predicted distance includes: using the predicted distance as the center point, performing target detection within the preset distance range.

4. The target detection method according to claim 1, characterized in that, The first matching of the target distance with the target point distance, and the Doppler channel of the search target with the Doppler channel of the target point, includes: Determine whether the difference between the target distance and the target point distance is less than a preset distance; Determine whether the difference between the Doppler channel of the search target and the Doppler channel of the target point is less than a preset value; If the difference between the target distance and the target point distance is less than a preset distance, and the difference between the Doppler channel of the search target and the Doppler channel of the target point is less than a preset value, then the first match is successful.

5. The target detection method according to claim 1, characterized in that, If the first match is successful, a second match is performed based on the target distance obtained after the first match, including: If the number of targets after the first match is Nt1, then Nt1 > 0; the target distances after the first match are respectively... Then, a second matching will be performed, and the matching criteria for the second matching are as follows: in, This is the absolute value of the difference between the target distance after each first match and the target distance itself; Dis of the time n The actual distance to the target being tracked.

6. The target detection method according to claim 1, characterized in that, The reliable and continuous processing of the optimal matching point includes: After the second match is successful, if ; If the first match fails, ; Where Cnt is the number recorded by the target continuous reliable counter.

7. The target detection method according to claim 6, characterized in that, The reliable continuous processing of the optimal matching point includes: when the first matching fails, if Cnt>5, then the distance to the tracked target is re-predicted; if Cnt<5, then the distance to the tracked target is stopped from being predicted.

8. The target detection method according to claim 1, characterized in that, The sliding window filtering process for the continuously processed best matching point data includes: The azimuth and elevation miss distances of the current target are obtained by using the sum-difference amplitude angle measurement method in azimuth and elevation. Determine if the number of frames in the radar signal is greater than the preset number of frames; If so, the preset number of frames is used as a time interval for sliding window processing to obtain the average miss distance; If not, cover based on the average of existing data or by using the first value.

9. A target detection device, characterized in that, include: The range prediction module is used to predict the distance to the tracked target based on the target distance and target speed sent by the search radar. The target point detection module is used to detect target points within a preset distance range based on the predicted distance; A matching module is used to match the detected target points based on the target distance and the target speed to obtain the optimal matching point; The data processing module is used to reliably and continuously process the optimal matching points; and to perform sliding window filtering on the continuously processed optimal matching point data. The matching module is further configured to: The Doppler channel of the search target is obtained based on the target distance and the target velocity; Based on the predicted distance, target point detection is performed within a preset distance range to obtain the target point distance and the Doppler channel of the target point; The target distance is matched with the target point distance, and the Doppler channel of the search target is matched with the Doppler channel of the target point for the first time. If the first match is successful, a second match is performed based on the target distance obtained after the first match to obtain the actual distance of the currently tracked target.

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