Adaptive clutter filtering method and device fusing multi-radar detection data, equipment and storage medium

Abstract

The invention provides an adaptive clutter filtering method and device fusing multi-radar detection data, equipment and a storage medium. Relates to the technical field of clutter filtering. According to the method, detection data of multiple radars are integrated, clutter filtering is automatically achieved on the basis of target coordinate calculation, specifically, on the basis of a multi-radar cooperative detection network, all the radars independently collect and process aerial target data, and the target data are gathered through the detection network. Sequentially and periodically calculating the distance between detection points formed by the radar for a target, and judging the effectiveness of the distance through a preset threshold value. And then calculating a mean value and a variance of the distances within a period of time, and realizing clutter filtering by combining data analysis and variance threshold judgment. Practice proves that the clutter interference can be remarkably inhibited, the misjudgment probability of the target is effectively reduced, and a more robust and reliable detection result is provided for target detection and tracking tasks in a complex environment.

Description

Technical Field

[0001] This invention relates to the field of clutter filtering technology, and in particular to an adaptive clutter filtering method, apparatus, device, and storage medium that integrates multiple radar detection data. Background Technology

[0002] With the rapid development and widespread application of emerging technologies such as the Internet of Things, big data, and artificial intelligence, radar technology is also constantly innovating and upgrading, gradually moving towards intelligence and digitalization. For example, the successive emergence of new technologies such as phased array radar and synthetic aperture radar has greatly improved the resolution and accuracy of radar detection, enabling radar to exhibit extremely high accuracy and efficiency in target detection, and providing more reliable technical support for various application scenarios.

[0003] However, despite significant advancements in radar technology, existing single-radar detection systems still exhibit limitations in certain environments. Specifically, these systems are susceptible to interference from various ground and airborne clutter sources, including buildings, terrain, and weather conditions, leading to false alarms. These false alarms not only affect the real-time performance of target identification, slowing down the response time, but also reduce the accuracy and reliability of identification, posing a considerable challenge to practical applications. Therefore, effectively addressing clutter interference and improving the performance of single-radar detection systems in complex environments is a pressing technical problem that needs to be solved. Summary of the Invention

[0004] This invention provides an adaptive clutter filtering method, apparatus, device, and storage medium that integrates multi-radar detection data. The aim is to improve the accuracy and reliability of radar data by enabling multiple radars in a multi-radar cooperative detection network to independently collect, process, and converge target data, and to achieve clutter filtering based on distance calculation and variance algorithm.

[0005] In a first aspect, the present invention provides an adaptive clutter filtering method that fuses multiple radar detection data, comprising: Data is acquired, including the distance, azimuth, and elevation angle between the target and the corresponding radar; wherein, the data is collected through a cooperative detection network, which includes at least two radars, each independently detecting the target and collecting data; The data detected by each radar is stored in its corresponding data list; Periodically extract N target data points for the same target from the data lists of each radar and store them in the corresponding preprocessing list, where N is a positive integer; Iterate through each preprocessing list and calculate the distance between the detection points formed by the radar for the target in sequence. dt and compare the distance with a preset distance threshold. d th Comparison: If d t > d th If the corresponding data is invalid, then the data is considered invalid. d t ≤ d th If the data is valid, it is determined to be valid and stored in the distance list. Based on the valid data in the distance list, calculate the mean of the distances over a period of time, and then calculate the variance of the distances based on the mean. s 2 ; Variance s 2 Compared with the preset variance threshold s th Comparison: If s 2 ≤ s th If so, the corresponding target data is deemed valid; if s 2 > s th If so, the corresponding target data is determined to be clutter and filtered.

[0006] In one possible design, all radars have the same detection cycle.

[0007] In one possible design, the mean distance is calculated based on the valid data in the distance list using the following formula:

[0008] In the formula, The mean of the distances; N The number of selected observation points is the logarithm. yes For the first i The distance between different radar targets.

[0009] In one possible design, the variance of the distance is calculated based on the mean. s 2 The formula is: .

[0010] In one possible design, the distance between different radar detection data at the same reception time is calculated using the distance formula in polar coordinates. d t ,include: The polar coordinates of the radar detection data are converted to rectangular coordinates, and the Euclidean distance between the corresponding rectangular coordinates of different radars is calculated to obtain the distance. d tThe polar coordinates of each radar detection data include range, azimuth, and elevation.

[0011] In one possible design, the method is applicable to target detection and tracking tasks in urban low-altitude or airport facility scenarios.

[0012] Secondly, the present invention provides an adaptive clutter filtering device that fuses multiple radar detection data, the device comprising: The data acquisition module is configured to acquire data, including the distance, azimuth, and elevation angle between the target and the corresponding radar; wherein, the data is collected through a cooperative detection network, which includes at least two radars, each radar independently detecting the target and collecting data; The data storage module is configured to store the data detected by each radar into a corresponding data list; The data extraction module is configured to extract N target data at the same reception time from the data lists of each radar and store them into the corresponding preprocessing list, where N is a positive integer. The first judgment module is configured to traverse each preprocessing list and calculate the distance between different radar detection data at the same reception time according to the distance formula in polar coordinates. d t and compare the distance with a preset distance threshold. d th Comparison: If d t > d th If the corresponding data is invalid, then the data is considered invalid. d t ≤ d th If the data is valid, it is determined to be valid and stored in the distance list. The second judgment module is configured to calculate the mean of the distances based on the valid data in the distance list, and then calculate the variance of the distances based on the mean. s 2 ; Variance s 2 Compared with the preset variance threshold s th Comparison: If s 2 ≤ s th If so, the corresponding target data is deemed valid; if s 2 > s th If so, the corresponding target data is determined to be clutter and filtered.

[0013] Thirdly, embodiments of the present invention provide an electronic device, comprising: at least one processor and a memory; the memory storing computer execution instructions; the at least one processor executing the computer execution instructions stored in the memory, causing the at least one processor to perform the adaptive clutter filtering method for fusing multi-radar detection data as described in the first aspect and various possible designs of the first aspect.

[0014] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing computer-executable instructions. When a processor executes the computer-executable instructions, it implements the adaptive clutter filtering method for fusing multi-radar detection data as described in the first aspect and various possible designs of the first aspect.

[0015] Fifthly, embodiments of the present invention provide a computer program product, including a computer program that, when executed by a processor, implements the adaptive clutter filtering method for fusing multi-radar detection data as described in the first aspect and various possible designs of the first aspect.

[0016] The adaptive clutter filtering method, apparatus, device, and storage medium that fuses multi-radar detection data provided by this invention have at least the following beneficial effects: 1) This invention effectively identifies and filters clutter by using multi-radar data collaborative verification and a dual judgment mechanism based on distance and variance, which greatly improves the accuracy of target data and reduces the interference of false data, laying a solid data foundation for subsequent target detection, tracking and other applications.

[0017] 2) Based on the statistical characteristics of the data itself, namely the distance mean and variance, the present invention dynamically adjusts the filtering judgment criteria to adapt to the noise characteristics in different scenarios, without the need for frequent manual parameter adjustments, thereby enhancing the versatility and environmental adaptability of the method.

[0018] 3) This invention expands the detection coverage and data dimensions through multi-radar collaborative operation, and reduces the risk of missed detection and false detection caused by environmental and equipment factors when using single radar detection by combining a strict data validity judgment process, thereby significantly improving the reliability of target detection results.

[0019] 4) This invention preprocesses data according to the data reception time, systematically organizing the correlation between multiple radar data, making the clutter filtering process clear and traceable, which is beneficial for engineering implementation. While ensuring filtering effectiveness, it also guarantees high data processing efficiency, meeting the needs of real-time detection tasks. Attached Figure Description

[0020] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0021] Figure 1 This is a schematic diagram illustrating an application scenario of the adaptive clutter filtering method for fusing multi-radar detection data provided in an embodiment of the present invention. Figure 2 A flowchart of an adaptive clutter filtering method that fuses multiple radar detection data is provided in an embodiment of the present invention; Figure 3 A flowchart illustrating the adaptive clutter filtering method for fusing multi-radar detection data provided in this embodiment of the invention when deploying two radars; Figure 4 This is a structural diagram of an adaptive clutter filtering device that fuses multiple radar detection data, provided in an embodiment of the present invention.

[0022] The accompanying drawings have illustrated specific embodiments of the invention, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the invention in any way, but rather to illustrate the concept of the invention to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0023] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatuses and methods consistent with some aspects of the invention as detailed in the appended claims.

[0024] The collection, storage, use, processing, transmission, provision, and disclosure of financial data or user data and other information involved in the technical solution of this invention all comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0025] It should be noted that in the embodiments of the present invention, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of the present invention. However, they do not mean that the inventor has used or necessarily used the solution.

[0026] The technical solution of the present invention and how the technical solution of the present invention solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of the present invention will now be described with reference to the accompanying drawings.

[0027] This invention provides an adaptive clutter filtering method that fuses data from multiple radar detection systems. For example... Figure 1 The diagram illustrates an application scenario of the adaptive clutter filtering method for fusing multi-radar detection data provided in this embodiment of the invention. This application scenario includes a cooperative detection network 100 and a data processing module 200. The cooperative detection network 100 includes at least two radars; the diagram shows n radars, designated as radar 1, radar 2, and radar n. Data collected by each radar is fed into the data processing module 200, which performs adaptive clutter filtering based on the radar detection data. Further, as... Figure 2 The diagram shows an adaptive clutter filtering flowchart for fused multi-radar detection data provided in an embodiment of the present invention. This adaptive clutter filtering method for fused multi-radar detection data includes the following steps S10-S50. It should be noted that step S10 can be executed through the cooperative detection network 100, while steps S20-S50 can be executed through the data module 200.

[0028] S10: Acquire data, including the distance, azimuth, and elevation angle between the target and the corresponding radar.

[0029] In step S10, raw detection data from multiple radars are acquired through a collaborative detection network. The principle behind this is to utilize the spatial distribution of different radars to observe the same airspace from multiple perspectives, providing a foundation for subsequent data fusion and cross-validation. In this embodiment, the data is collected through a collaborative detection network, such as... Figure 1 As shown, the collaborative detection network includes at least two radars, each independently detecting targets and collecting data.

[0030] It should be noted that the protection scope of step S10 is not limited to a specific type of radar, but also includes any detection device that can provide the spatial coordinates of the target, as well as various wired or wireless detection network topologies.

[0031] S20: Store the data detected by each radar into the corresponding data list.

[0032] Step S20 stores the radar data in separate lists. The principle behind this is to maintain data independence and traceability, avoid data confusion, and provide structured input for subsequent time alignment and data association. The scope of this step covers any form of temporary storage, caching, or structured storage of data, regardless of the specific storage medium or data structure implementation.

[0033] In one exemplary embodiment, such as Figure 3 The diagram shown illustrates the process of deploying two radars using the adaptive clutter filtering method that fuses multi-radar detection data, as provided in this embodiment of the invention. Taking two radars as an example, two radars are deployed (radars...). A and radar B(To form a collaborative observation network. Radar) A and radar B Targets are detected and data is acquired separately. (Radar) A Single target data obtained from detection ItemA Including the distance between the target and the radar R A azimuth i A Pitch angle f A And store this data in a list. ListA China; radar B Single target data obtained from detection ItemB The data includes the distance between the target and the radar. R B azimuth i B Pitch angle f B And store this data in a list. ListB In the middle. Every... Tms Detect a target once and acquire radar data. A and radar B Data ItemA and ItemB .

[0034] S30: Periodically extract N target data corresponding to the same target from the data list of each radar and store them into the corresponding preprocessing list, where N is a positive integer.

[0035] Step S30 extracts data corresponding to the same target based on the reception time. Its core principle is based on the time synchronization assumption that detection data from different radars targeting the same physical target should arrive almost simultaneously, thus completing the initial data association. The protection scope of this step can be extended to any method that merges multi-source sensing data into a single target dataset based on time windows, target identifiers, or other association logic.

[0036] In one exemplary embodiment, such as Figure 3 As shown, taking two radars as an example, the list can be used to... ListA and ListB Take out from each N Data, of which ItemA i ( i =1,2,..., N )and ItemB j ( j =1,2,..., N () refers to data received at the same receiving time. The retrieved data is stored in separate lists. DataListA and DataListBAmong them, the list DataListA and DataListB This is the preprocessing list.

[0037] S40: Traverse each preprocessing list and calculate the distance between the radar detection points of the target in sequence. d t and compare the distance with a preset distance threshold. d th Comparison: If d t > d th If the corresponding data is invalid, then the data is considered invalid. d t ≤ d th If the data is valid, it will be stored in the distance list.

[0038] Step S40 calculates the spatial distance between different radar detection points and performs a threshold judgment. The principle is that the projected positions of real targets in different radar coordinate systems should be highly consistent, and their spatial distance should be small; while clutter or false targets, due to their randomness, exhibit larger spatial distances. A preset distance threshold is used. d th A coarse screening process can quickly remove obviously abnormal data. This step protects against any spatial distance metric, including but not limited to Euclidean distance, Mahalanobis distance, and other similar methods, as well as judgment strategies such as static thresholds and dynamic adaptive thresholds. Polar to Cartesian coordinate conversion is the preferred method for achieving a unified spatial metric, but it is not the only one.

[0039] In one exemplary embodiment, such as Figure 3 As shown, taking two radars as an example, the list is traversed. DataListA and DataListB Calculate the radar range at the same reception time using the range formula in polar coordinates. A and radar B Detected data ItemA and ItemB Distance between d t ( t =1,2,..., N If the calculated distance d t Greater than the predetermined threshold d th If the distance is [missing information], then the current data is considered invalid; if the distance is [missing information], then the current data is considered invalid. d t Less than the threshold d th Then store the distance value in the distance data list. Distance .

[0040] In one exemplary embodiment, the polar coordinates of each radar detection data are converted to rectangular coordinates, and the Euclidean distance between the corresponding rectangular coordinates of different radars is calculated to obtain the distance. d t The polar coordinates of each radar detection data include range, azimuth, and elevation.

[0041] S50: Based on the valid data in the distance list, calculate the mean of the distances, and calculate the variance of the distances based on the mean. s 2 ; Variance s 2 Compared with the preset variance threshold s th Comparison: If s 2 ≤ s th If so, the corresponding target data is deemed valid; if s 2 > s th If so, the corresponding target data is determined to be clutter and filtered.

[0042] Step S50 calculates the mean and variance of the effective range and performs a variance threshold judgment. The principle is that, over a period of time, due to the regularity of the movement and the consistency of detection, the range deviation sequence of a real target under different radars should remain stable with a small variance; while clutter exhibits randomness and instability, resulting in larger fluctuations in the range deviation sequence and a significantly higher variance. Using variance as a statistical characteristic for secondary fine-tuning can effectively distinguish between real targets and clutter. The protection scope of this step covers stability assessment and filtering decisions based on any statistical characteristics of the range deviation sequence (such as standard deviation, range, root mean square, etc.).

[0043] In one exemplary embodiment, such as Figure 3 As shown, traversing the list Distance Calculate the mean of all valid distances. The formula is .in, N The number of selected observation points is the logarithm. d i For the first i right ItemA i and ItemB i The distance between them.

[0044] Further calculate the variance of the distance between the observed coordinates. s 2 The variance formula is If the calculated variance s2 Less than the predetermined threshold s th If the variance is positive, then the current data is considered valid; if the variance is negative, then the current data is considered valid. s 2 Greater than the threshold s th If the current data is considered noise, it will be filtered out.

[0045] This invention also provides an adaptive clutter filtering device that fuses multiple radar detection data, such as... Figure 4 As shown, the adaptive clutter filtering device that fuses multi-radar detection data includes: The data acquisition module 401 is configured to acquire data, including the distance, azimuth angle, and elevation angle between the target and the corresponding radar; wherein, the data is collected through a cooperative detection network, which includes at least two radars, each radar independently detecting the target and collecting data; Data storage module 402 is configured to store the data detected by each radar into a corresponding data list; The data extraction module 403 is configured to extract N target data at the same reception time from the data lists of each radar and store them into the corresponding preprocessing list, where N is a positive integer. The first judgment module 404 is configured to traverse each preprocessing list and calculate the distance between different radar detection data at the same reception time according to the distance formula in polar coordinates. d t and compare the distance with a preset distance threshold. d th Comparison: If d t > d th If the corresponding data is invalid, then the data is considered invalid. d t ≤ d th If the data is valid, it is determined to be valid and stored in the distance list. The second judgment module 405 is configured to calculate the mean of the distances based on the valid data in the distance list, and calculate the variance of the distances based on the mean. s 2 ; Variance s 2 Compared with the preset variance threshold s th Comparison: If s 2 ≤ s th If so, the corresponding target data is deemed valid; if s 2 > sth If so, the corresponding target data is determined to be clutter and filtered.

[0046] In some embodiments, the detection cycles of each radar are the same.

[0047] In some embodiments, the second determining module is further configured to calculate the mean distance based on valid data in the distance list using the following formula:

[0048] In the formula, The mean of the distances; N The number of selected observation points is the logarithm. d i For the first i right ItemA i and ItemB i The distance between them ItemA i and ItemB j The target data received at the same reception time.

[0049] In some embodiments, the variance of the distance is calculated based on the mean. s 2 The formula is: .

[0050] In some embodiments, the first determining module is further configured to: The polar coordinates of the radar detection data are converted to rectangular coordinates, and the Euclidean distance between the corresponding rectangular coordinates of different radars is calculated to obtain the distance. d t The polar coordinates of each radar detection data include range, azimuth, and elevation.

[0051] In some embodiments, the device is suitable for target detection and tracking tasks in urban low-altitude or airport facility scenarios.

[0052] This invention provides an electronic device. The electronic device may include a processor and a memory, wherein the processor and the memory can communicate; exemplarily, the processor and the memory communicate via a communication bus.

[0053] The processor executes computer execution instructions stored in memory, causing the processor to perform the scheme in the above embodiments. The processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0054] The communication bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The system bus can be divided into address bus, data bus, control bus, etc. Transceivers are used to enable communication between database access devices and other computers (e.g., clients, read-write libraries, and read-only libraries). Memory may include random access memory (RAM) and may also include non-volatile memory.

[0055] The electronic device provided in this embodiment of the invention can be the terminal device described in the above embodiments.

[0056] This invention also provides a computer-readable storage medium storing computer instructions. When these computer instructions are executed on a computer, the computer performs the adaptive clutter filtering method for fusing multiple radar detection data as described in the above embodiments.

[0057] This invention also provides a computer program product, which includes a computer program stored in a computer-readable storage medium. At least one processor can read the computer program from the computer-readable storage medium. When the at least one processor executes the computer program, it can implement the technical solution of the adaptive clutter filtering method for fusing multiple radar detection data in the above embodiments.

[0058] In the several embodiments provided by this invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or modules, and may be electrical, mechanical, or other forms.

[0059] The modules described as separate components may or may not be physically separate. The components shown as modules 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 modules can be selected to implement the solution of this embodiment according to actual needs.

[0060] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each module can exist physically separately, or two or more modules can be integrated into one unit. The unit composed of the above modules can be implemented in hardware or in the form of hardware plus software functional units.

[0061] The integrated modules described above, implemented as software functional modules, can be stored in a computer-readable storage medium. These software functional modules, stored in a storage medium, include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute certain steps of the methods of the various embodiments of the present invention.

[0062] It should be understood that the aforementioned processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. A general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly manifested as execution by a hardware processor, or execution by a combination of hardware and software modules within the processor.

[0063] The memory may include high-speed RAM, and may also include non-volatile storage (NVM), such as at least one disk storage device, and may also be a USB flash drive, external hard drive, read-only memory, disk or optical disc, etc.

[0064] Buses can be Industry Standard Architecture (ISA) buses, Peripheral Component Interconnect (PCI) buses, or Extended Industry Standard Architecture (EISA) buses, etc. Buses can be categorized into address buses, data buses, control buses, etc.

[0065] The aforementioned storage medium 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. The storage medium can be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0066] An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Alternatively, the storage medium can be an integral part of the processor. The processor and storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and storage medium can exist as discrete components in an electronic control unit or main control device.

[0067] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0068] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. An adaptive clutter filtering method that fuses multiple radar detection data, characterized in that, The method includes: Data is acquired, including the distance, azimuth, and elevation angle between the target and the corresponding radar; wherein, the data is collected through a cooperative detection network, which includes at least two radars, each independently detecting the target and collecting data; The data detected by each radar is stored in its corresponding data list; Periodically extract N target data points for the same target from the data lists of each radar and store them in the corresponding preprocessing list, where N is a positive integer; Iterate through each preprocessing list and calculate the distance between the detection points formed by the radar for the target in sequence. d t and compare the distance with a preset distance threshold. d th Comparison: If d t > d th If the corresponding data is invalid, then the data is considered invalid. d t ≤ d th If the data is valid, it is determined to be valid and stored in the distance list. Based on the valid data in the distance list, calculate the mean of the distances over a period of time, and then calculate the variance of the distances based on the mean. σ 2 ; Variance σ 2 Compared with the preset variance threshold σ th Comparison: If σ 2 ≤ σ th If so, the corresponding target data is deemed valid; if σ 2 > σ th If so, the corresponding target data is determined to be clutter and filtered.

2. The adaptive clutter filtering method for fusing multi-radar detection data according to claim 1, characterized in that, The detection cycles of all radars are the same.

3. The adaptive clutter filtering method for fusing multi-radar detection data according to claim 1, characterized in that, Based on the valid data in the distance list, the mean distance is calculated using the following formula: ； In the formula, The mean of the distances; N The number of selected observation points is the logarithm. d i For the first i The distance between different radar targets.

4. The adaptive clutter filtering method for fusing multi-radar detection data according to claim 3, characterized in that, The variance of the distance is calculated based on the mean. σ 2 The formula is: .

5. The adaptive clutter filtering method for fusing multi-radar detection data according to claim 1, characterized in that, Calculate the distance between different radar detection data at the same reception time using the distance formula in polar coordinates. d t ,include: The polar coordinates of the radar detection data are converted to rectangular coordinates, and the Euclidean distance between the corresponding rectangular coordinates of different radars is calculated to obtain the distance. d t The polar coordinates of each radar detection data include range, azimuth, and elevation.

6. The adaptive clutter filtering method for fusing multi-radar detection data according to any one of claims 1 to 5, characterized in that, The method is applicable to target detection and tracking tasks in urban low-altitude or airport facility scenarios.

7. An adaptive clutter filtering device that integrates multi-radar detection data, characterized in that, The device includes: The data acquisition module is configured to acquire data, including the distance, azimuth, and elevation angle between the target and the corresponding radar; wherein, the data is collected through a cooperative detection network, which includes at least two radars, each radar independently detecting the target and collecting data; The data storage module is configured to store the data detected by each radar into a corresponding data list; The data extraction module is configured to extract N target data at the same reception time from the data lists of each radar and store them into the corresponding preprocessing list, where N is a positive integer. The first judgment module is configured to traverse each preprocessing list and calculate the distance between different radar detection data at the same reception time according to the distance formula in polar coordinates. d t and compare the distance with a preset distance threshold. d th Comparison: If d t > d th If the corresponding data is invalid, then the data is considered invalid. d t ≤ d th If the data is valid, it is determined to be valid and stored in the distance list. The second judgment module is configured to calculate the mean of the distances based on the valid data in the distance list, and then calculate the variance of the distances based on the mean. σ 2 ; Variance σ 2 Compared with the preset variance threshold σ th Comparison: If σ 2 ≤ σ th If so, the corresponding target data is deemed valid; if σ 2 > σ th If so, the corresponding target data is determined to be clutter and filtered.

8. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the adaptive clutter filtering method for fusing multi-radar detection data as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the adaptive clutter filtering method for fusing multi-radar detection data as described in any one of claims 1-6.

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