Unmanned aerial vehicle target track auxiliary association method, equipment and medium

Abstract

The invention discloses an unmanned aerial vehicle target track auxiliary association method, equipment and a medium, belongs to the technical field of radar target tracking, and is used for solving the problems that an existing track association method mainly depends on a fixed rule, when a plurality of trace points exist, wrong trace points are often associated due to the limitation of the rule, and the correlation efficiency is poor. And a relatively high error association risk exists. The method comprises the following steps: according to an echo signal in a target airspace, performing plot score calculation of related main peak plot micro-motion characteristics on Doppler dimension slices of each plot to obtain a main peak plot score; based on the unmanned aerial vehicle trace point membership degree corresponding to the main peak trace point score, non-coherent accumulation is carried out on the Doppler dimension signal of the target track, and a target marking unmanned aerial vehicle is discriminated and determined; and carrying out track correlation matching processing on the target marking unmanned aerial vehicle to obtain a latest Doppler signal under the correlation trace point.

Description

Technical Field

[0001] The present application relates to the field of radar target tracking technology, and in particular to a method, device and medium for assisting in associating the track of a UAV target. Background Art

[0002] In radar target tracking technology, track association is a core step in achieving continuous target tracking. Its accuracy directly impacts the reliability of target state estimation and trajectory prediction. However, in complex low-altitude environments, due to interference sources such as clutter and bird flocks, traditional track association methods often struggle to effectively distinguish true targets from interference, leading to tracking interruptions or incorrect track associations. Therefore, improving association accuracy has become a key research topic in radar target tracking.

[0003] The micro-Doppler effect, as an important physical characteristic, offers a new approach to track correlation. The micro-Doppler effect is a unique time-frequency signature created by Doppler frequency shift modulation caused by tiny vibrations, rotations, and other motions of a target or its components. For example, the periodic rotation of drones (UAVs) induces a micro-Doppler effect, resulting in a regular harmonic signature in the frequency spectrum. This characteristic can not only be used to distinguish UAVs from non-UAV targets but also provides additional discriminant evidence for track correlation.

[0004] Currently, the main technologies used for track association include the nearest neighbor (NN) method and the probabilistic data association (PDA) method. The nearest neighbor method sets a correlation gate for the target at the current moment, screens candidate tracks within the gate, and selects the track with the smallest statistical distance or the largest residual probability density as the association target. The probabilistic data association method uses a weighted fusion of the correlation probabilities of all candidate tracks to generate a composite measurement value for state updates.

[0005] However, existing track association methods rely primarily on fixed rules. When multiple tracks are present, incorrect tracks are often associated due to the limitations of these rules, resulting in a high risk of misassociation. For example, the nearest neighbor method only selects the track with the smallest statistical distance or the largest residual probability density as the association target. While the probabilistic data association method considers weighted fusion of multiple candidate tracks, it is computationally complex and highly dependent on prior information. Therefore, a new track association method is urgently needed to improve the accuracy and quality of track association. Summary of the Invention

[0006] The embodiments of the present application provide a method, device, and medium for assisting in associating the target track of a drone, which are used to solve the following technical problems: Existing track association methods mainly rely on fixed rules. When there are multiple track points, incorrect track points are often associated due to the limitations of the rules, resulting in a high risk of misassociation.

[0007] The embodiments of this application adopt the following technical solutions:

[0008] On the one hand, an embodiment of the present application provides a method for assisting the association of UAV target tracks, comprising: performing track score calculation on the Doppler dimension slices of each track related to the micro-motion characteristics of the main peak track according to the echo signal in the target airspace to obtain the main peak track score; performing non-coherent accumulation on the Doppler dimension signal of the target track based on the UAV track membership corresponding to the main peak track score to identify and determine the target marked UAV; performing track association matching processing on the target marked UAV to obtain the latest Doppler signal under the associated track.

[0009] By analyzing the Doppler slices of the echo signal in the target airspace, the embodiment of the present application can more accurately identify and calculate the micro-motion characteristics of the main peak point track, thereby improving the recognition accuracy of the target track. The non-coherent accumulation technology can effectively suppress interference signals in complex environments and improve the stability and reliability of the system under noise and interference conditions. It can also quickly process Doppler signals to achieve real-time target marking and track association, which is suitable for UAV target tracking in dynamic environments. At the same time, through the association matching process, the track marked as a drone can be more accurately matched with the actual point track, reducing the situation of mismatching and missed matching. It also helps to improve the safety of UAV operations and reduce the risk of misoperation and accidents.

[0010] In a feasible embodiment, before calculating the doppler dimension score of each dot trace for the micro-motion feature of the main peak dot trace according to the echo signal in the target airspace and obtaining the main peak dot trace score, the method further includes: performing a series of signal processing on the echo signal; Get the CFAR detection threshold T CFAR ; where α is the scaling factor, N ref is the reference window length, x k is the sample value of noise / clutter in the reference window, k is a mathematical constant; based on the interval between the drone body and the first harmonic, the corresponding harmonic interval is calculated and obtained; according to Δf min =f min ·N PRT PRT, to obtain the minimum Doppler unit Δf based on the harmonic spacing min ; Among them, f min is the minimum frequency interval, N PRTis the number of pulses in a coherent integration time, PRT is the pulse repetition period; according to Δf max =f max ·N PRT PRT, to obtain the maximum Doppler bin Δf based on the harmonic spacing max ; Among them, f max is the maximum frequency interval.

[0011] In a feasible implementation, based on the echo signal in the target airspace, the Doppler dimension slices of each point trace are subjected to the point trace score calculation related to the micro-motion characteristics of the main peak point trace to obtain the main peak point trace score, specifically including: based on the Doppler dimension slices of each point trace in the target airspace, if there are multiple detection points exceeding the CFAR detection threshold in the same Doppler dimension slice, the point with the largest amplitude is selected as the main peak point, and the corresponding frequency unit is determined; according to Δf i =|f main -f Hi |, obtain the frequency interval unit Δf between other over-detected points and the main peak point in the current slice i ; Among them, f main is the frequency unit, f Hi is the frequency unit corresponding to the i-th detection point; according to Get the main peak trace micro-motion feature score F main ; Wherein, I(·) is an indicator function, and when the indicator function meets the conditions, the value is 1, otherwise the indicator function is 0; N is the total number of other over-detection points in the current slice, k is the harmonic order; w k is the weight coefficient and is related to the harmonic order; when k is a mathematical constant, it is related to w k It is a one-to-one correspondence; based on the main peak trace micro-motion feature score, the main peak trace score is updated and obtained.

[0012] In a feasible embodiment, before performing non-coherent accumulation on the Doppler signal of the target track based on the drone track membership corresponding to the main peak track score, and discriminating and determining the target marked drone, the method also includes: if the harmonic interval of the track in the target airspace is within a preset range, then there is a positive correlation between the main peak track score and the membership attributable to the drone track; wherein, the higher the main peak track score, the higher the probability that the corresponding track is a drone track. Based on the positive correlation, when the track of the aerial target is batched, the Doppler signal of the associated track is synchronously saved; according to the established historical track target, the historical Doppler signal in the historical track target is extracted, and the frequency with the maximum amplitude in each frame is correspondingly extracted.

[0013] In a feasible implementation, the target track Doppler signal is non-coherently accumulated to discriminate and determine the target marker drone, specifically comprising: aligning the main peaks of multiple frames corresponding to the maximum amplitude frequency in each frame to the same frequency unit; Get the amplitude of the main peak Among them, s i is the Doppler signal after the alignment of the i-th main peak, N is the number of accumulated frames, and i is a mathematical constant; according to Get the average amplitude of the four largest peaks adjacent to the main peak Among them, p k is the kth peak amplitude, k is a mathematical constant; when When , the track corresponding to the main peak track score is identified as the target marked UAV; otherwise, the track is identified as a non-UAV; wherein, N avg is the average noise level; T is the adjustment threshold; the sum of the average noise level and the adjustment threshold is the UAV track detection threshold.

[0014] In a feasible embodiment, before performing track association matching processing on the target marking UAV to obtain the latest Doppler signal under the associated track, the method also includes: screening out all candidate tracks within the association range according to the target track corresponding to the target marking UAV; sorting the main peak track micro-motion feature scores corresponding to the candidate tracks to determine the track micro-motion feature score with the highest score; and performing track association processing on the track micro-motion feature score with the highest score to obtain first associated data; if there are multiple tracks with the highest and identical track micro-motion feature scores in the score sorting, then comparing the spatial distance between the track with the highest and identical track micro-motion feature score and the track predicted position to determine the track with the closest distance; and performing track association processing on the track with the closest distance to obtain second associated data.

[0015] In a feasible implementation, the target marking UAV is subjected to track association matching processing to obtain the latest Doppler signal under the associated track, specifically including: performing data association matching on the track of the current UAV according to the first association data and the second association data associated with the target marking UAV to obtain association result data; based on the association result data, updating the track data of the current UAV, and saving the latest Doppler signal under the completed associated track.

[0016] In a feasible implementation, if the track is determined to be a non-UAV track, the nearest neighbor algorithm is used to select the point track closest to the track prediction position for association processing to obtain third association data; based on the third association data, the current UAV track is associated and matched and updated accordingly to determine the latest Doppler signal under the associated point track.

[0017] In a second aspect, an embodiment of the present application also provides a drone target track auxiliary association device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor so that the at least one processor can execute a drone target track auxiliary association method described in any of the above embodiments.

[0018] In a third aspect, an embodiment of the present application further provides a non-volatile computer storage medium, which is a non-volatile computer-readable storage medium. The non-volatile computer-readable storage medium stores at least one program, each of which includes instructions. When the instructions are executed by a terminal, the terminal executes a drone target track auxiliary association method described in any of the above embodiments.

[0019] This application provides a method, device, and medium for assisting in associating a target track of a drone. Compared with the prior art, the embodiments of this application have the following beneficial technical effects:

[0020] 1. Improve target recognition accuracy: By analyzing the Doppler slice of the echo signal in the target airspace, the micro-motion characteristics of the main peak point track can be more accurately identified and calculated, thereby improving the recognition accuracy of the target track.

[0021] 2. Enhanced anti-interference capability: Non-coherent accumulation technology can effectively suppress interference signals in complex environments and improve the stability and reliability of the system under noise and interference conditions.

[0022] 3. Improved real-time performance: It can quickly process Doppler signals, realize real-time target marking and track association, and is suitable for UAV target tracking in dynamic environments.

[0023] 4. Improve track association accuracy: Through association matching processing, the tracks marked as drones can be more accurately matched with actual point tracks, reducing false matches and missed matches.

[0024] 5. Improve safety: By effectively tracking and correlating drone tracks, it helps improve the safety of drone operations and reduce the risk of misoperation and accidents.

[0025] 6. Improve data processing efficiency: By optimizing the data processing process, data processing efficiency is improved, allowing the system to respond to changes more quickly and adapt to dynamic environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments described in the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0027] Figure 1 A flow chart of a method for assisting the association of drone target tracks provided in an embodiment of the present application;

[0028] Figure 2 A flow chart of a track association method provided in an embodiment of the present application;

[0029] Figure 3 A schematic diagram of harmonic characteristics provided in an embodiment of the present application;

[0030] Figure 4 A schematic diagram of non-coherent accumulation of a drone provided in an embodiment of the present application;

[0031] Figure 5 A schematic diagram of non-UAV coherent accumulation provided in an embodiment of the present application;

[0032] Figure 6 A schematic diagram of a low-altitude detection radar display and control provided in an embodiment of the present application;

[0033] Figure 7 A schematic diagram of the structure of a drone target track auxiliary association device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0034] In order to enable those skilled in the art to better understand the technical solutions in this application, the following will clearly and completely describe the technical solutions in the embodiments of this application in conjunction with the drawings in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of this application.

[0035] It should be noted that compared with the traditional track association method, this application combines the micro-Doppler characteristics of the target UAV to calculate the point track micro-Doppler feature score and mark the track type. When associating the tracks, the corresponding method is selected according to different track types, thereby improving the accuracy of track association and track quality.

[0036] The present application embodiment provides a method for assisting the association of drone target tracks, such as Figure 1 As shown, the UAV target track auxiliary association method specifically includes steps S101-S103:

[0037] S101 , calculating the point score of the main peak point trace micro-motion feature on the Doppler dimension slices of each point trace according to the echo signal in the target airspace, and obtaining the main peak point trace score.

[0038] Specifically, a series of signal processing must be performed on the echo signals in the target airspace. The echo signals in the airspace may not only come from drones, but also from birds, clutter, etc.

[0039] As a feasible implementation method, Figure 2 A flow chart of a track association method provided in an embodiment of the present application is as follows: Figure 2 As shown in FIG, after a series of signal processing, the echo signal adopts CFAR (constant false alarm detection) to adaptively detect the traces, and the traces that exceed the threshold are given a basic score of 1.

[0040] That is, using Get the CFAR detection threshold T CFAR . Where α is the scaling factor, N ref is the reference window length, x k is the sample value of noise / clutter in the reference window, and k is a mathematical constant.

[0041] Furthermore, based on the interval between the drone body and the first harmonic, the corresponding harmonic interval is calculated and obtained.

[0042] In one embodiment, micro-Doppler characteristic parameters are loaded. According to the measured data of mainstream drones, the interval between the drone body and the first harmonic is 70 to 180 Hz. The corresponding harmonic interval Doppler unit is calculated in combination with radar parameters.

[0043] Furthermore, according to Δf min =f min ·N PRT PRT, to obtain the minimum Doppler unit Δf based on the harmonic spacing min Among them, f min is the minimum frequency interval, N PRT is the number of pulses within a coherent integration time, and PRT is the pulse repetition period.

[0044] Furthermore, according to Δf max =f max ·N PRT PRT, to obtain the maximum Doppler unit Δf based on the harmonic spacing max Among them, f max is the maximum frequency interval.

[0045] Furthermore, based on the Doppler slices of each point in the target airspace, if there are multiple detection points exceeding the CFAR detection threshold in the same Doppler slice, the point with the largest amplitude is selected as the main peak point, and the corresponding frequency unit is determined.

[0046] Further, if Figure 2 As shown in the figure, along the Doppler slices of each point trace, if there are multiple over-detection points in the same Doppler slice, the point with the largest amplitude is selected as the main peak point, and its corresponding frequency unit f is set to main , calculate the frequency interval unit Δf between other over-detection points and the main peak point in the current slice i : Get the frequency interval unit Δf between other detected points and the main peak point in the current slice i Among them, f main is the frequency unit, f Hi is the frequency unit corresponding to the i-th detection point.

[0047] Furthermore, it is necessary to verify Δf i Whether the harmonic interval is met and the micro-motion feature score of the main peak trace is calculated: Get the main peak trace micro-motion feature score F main Where I(·) is the indicator function, and when the indicator function meets the conditions, the value is 1, otherwise the indicator function is 0. N is the total number of other over-detection points in the current slice, and k is the harmonic order. k is the weight coefficient and is related to the harmonic order; when k is a mathematical constant, it is related to w k It is a one-to-one correspondence, that is, when k is 1, 2, and 3 respectively, w k The corresponding values ​​are 1, 2, and 3.

[0048] As a feasible implementation method, Figure 3 A harmonic characteristic diagram provided in an embodiment of the present application is shown in FIG. Figure 3 As shown, the UAV's rotor rotation induces micro-Doppler modulation, which exhibits harmonic characteristics. Its spectrum is characterized by the generation of harmonic components in the frequency domain that are integer multiples of the rotor fundamental frequency, centered on the UAV's main translational Doppler spectrum. In the range-Doppler domain, the target echo energy is dispersed into equally spaced harmonic clusters along the Doppler axis, forming a physically measurable harmonic signature that is strongly correlated with the rotor's motion period.

[0049] Furthermore, based on the main peak trace micro-motion feature score, the main peak trace score is finally updated and obtained.

[0050] S102: Based on the UAV point track membership corresponding to the main peak point track score, non-coherently accumulate the Doppler signal of the target track to identify and determine the target marked UAV.

[0051] Specifically, if the harmonic spacing of the traces in the target airspace is within a preset range, there is a positive correlation between the main peak trace score and the degree of membership to the drone trace. The higher the main peak trace score, the higher the probability that the corresponding trace is a drone trace. In other words, the positive correlation means that the higher the main peak trace score, the higher the degree of membership to the drone trace.

[0052] As a feasible implementation method, this application calculates the micro-motion feature score of the trace based on the harmonic interval. Since the speed of the drone in actual scenarios is affected by various environmental factors, the harmonic interval will vary to a certain extent. Therefore, this application sets a reasonable harmonic interval range and calculates whether the harmonic interval of each trace falls within the set range. If it falls within the preset range, it means that the trace is likely to belong to the drone, and the trace is assigned a higher score.

[0053] Furthermore, based on the positive correlation, when the track of the aerial target is batched, the Doppler signals of the associated points are saved synchronously.

[0054] Furthermore, according to the established historical track target, the historical Doppler signals in the historical track target are extracted, and the frequency with the maximum amplitude in each frame is correspondingly extracted.

[0055] Furthermore, the main peaks of multiple frames corresponding to the maximum amplitude frequency in each frame are aligned to the same frequency unit.

[0056] Furthermore, non-coherent accumulation is required. That is, the main peaks of multiple frames are aligned to the same frequency unit, amplitude superposition is performed, and normalization is performed to generate an energy-enhanced frequency domain profile. The amplitude is calculated as follows: Get the amplitude of the main peak Among them, s i is the Doppler signal after alignment of the i-th main peak, N is the number of accumulated frames, and i is a mathematical constant.

[0057] As a feasible implementation method, by performing non-coherent accumulation of multi-frame Doppler signals, the target micro-Doppler characteristics can be effectively enhanced while suppressing random noise and clutter.

[0058] Further, according to Get the average amplitude of the four largest peaks adjacent to the main peak Among them, p k is the amplitude of the kth peak, where k is a mathematical constant.

[0059] Furthermore, when When N is the peak point score, the track corresponding to the main peak point score is identified as the target marked UAV. Otherwise, the track is identified as non-UAV. avg is the average noise level; T is the adjustment threshold. The sum of the average noise level and the adjustment threshold is the drone track detection threshold.

[0060] In one embodiment, Figure 4 A schematic diagram of non-coherent accumulation of a drone provided in an embodiment of the present application is provided. Figure 5 A schematic diagram of non-UAV coherent accumulation provided in an embodiment of the present application is shown as follows: Figure 4 as well as Figure 5 As shown in the figure, after non-coherent integration, the micro-Doppler signature of drone targets is enhanced. The increase in harmonic energy manifests as regular, high-amplitude spikes in the Doppler dimension, significantly above the ambient background noise level. Non-drone targets, on the other hand, do not produce these regular spikes, and their amplitudes are typically similar to the noise level. By calculating the average amplitude of the spikes surrounding the main peak and comparing it with the noise level, it is possible to effectively identify drone targets and improve target recognition accuracy.

[0061] S103: Perform track correlation matching processing on the target marked UAV to obtain the latest Doppler signal under the correlation point track.

[0062] Specifically, according to the target track corresponding to the target-marked UAV, all candidate track points within the associated range are screened out.

[0063] Furthermore, the main peak point track micro-motion feature scores corresponding to the candidate points are ranked by scores to determine the point track micro-motion feature score with the highest score, and the point track micro-motion feature score with the highest score is subjected to track correlation processing to obtain first correlation data.

[0064] Furthermore, if there are multiple tracks with the same highest micro-motion feature score in the score sorting, the spatial distance between the track with the highest micro-motion feature score and the predicted track position is compared to determine the track closest to the track. The track closest to the track is then track-correlated to obtain second correlation data.

[0065] In one embodiment, all candidate tracks within the association range are screened using the track marked as a drone. The track with the highest micro-motion feature score is prioritized for association. If multiple tracks exist with the same micro-motion feature score, the spatial distances between these tracks and the predicted track location are further compared, and the track with the closest distance is selected for association.

[0066] As a feasible implementation, when multiple candidate tracks are present, tracks with high scores indicate that the target has micro-Doppler characteristics, and tracks marked as drones also have stronger micro-Doppler characteristics. Prioritizing association with the track with the highest score ensures a more accurate match between the drone track and the target track, thereby reducing the probability of false associations and missed associations, and improving the stability and reliability of target tracking.

[0067] Furthermore, based on the first and second association data associated with the target marked UAV, data association matching is performed on the current UAV's track to obtain association result data. Based on the association result data, the current UAV's track data is updated, and the latest Doppler signal of the associated point track is saved.

[0068] As a feasible implementation, if the track is determined to be non-UAV, a nearest neighbor algorithm is used to select the point closest to the predicted track location for correlation processing, generating third correlation data. Based on this third correlation data, the current UAV track is correlated and updated accordingly to determine the latest Doppler signal for the correlated point.

[0069] In one embodiment, Figure 6 A schematic diagram of a low-altitude detection radar display and control provided in an embodiment of the present application is shown as follows: Figure 6 As shown, the X-band phased array radar detects low-altitude targets, wherein the radar parameters are: carrier frequency: 9500MHz, bandwidth: 20MHz, sampling rate: 20MHz, pulse repetition period: 128μs, pulse width: 15μs, number of pulses: 512. For a set of measured data, the target moves back and forth between 3.5km and 4km. First, execute step S101 in this application: calculate the point track score, and the target point track is kept at around 3 points. Execute step S102: mark the track type. After multiple frames of non-coherent accumulation, the average peak value of the spike exceeds the detection threshold (set to 40dB), and it is marked as a drone. Execute step: 103: track correlation matching, the drone is stably tracked, and the track quality is high.

[0070] In addition, the present application also provides a drone target track auxiliary association device, such as Figure 7 As shown, the drone target track auxiliary association device 700 specifically includes:

[0071] At least one processor 701. And a memory 702 in communication with the at least one processor 701. The memory 702 stores instructions that can be executed by the at least one processor 701, so that the at least one processor 701 can execute:

[0072] According to the echo signal in the target airspace, the Doppler dimension slices of each point are used to calculate the point score of the main peak point trace micro-motion characteristics to obtain the main peak point trace score;

[0073] Based on the UAV track membership corresponding to the main peak track score, the Doppler signal of the target track is non-coherently accumulated to identify and determine the target marked UAV;

[0074] The target marked UAV is processed by track correlation matching to obtain the latest Doppler signal under the correlation point track.

[0075] By analyzing the Doppler slices in the drone echo signal, the embodiment of the present application can more accurately identify and calculate the micro-motion characteristics of the main peak point track, thereby improving the recognition accuracy of the target track. The non-coherent accumulation technology can effectively suppress interference signals in complex environments and improve the stability and reliability of the system under noise and interference conditions. It can also quickly process Doppler signals to achieve real-time target marking and track association, which is suitable for drone target tracking in dynamic environments. At the same time, through the association matching process, the track marked as a drone can be more accurately matched with the actual point track, reducing the situation of mismatching and missed matching. It also helps to improve the safety of drone operations and reduce the risk of misoperation and accidents.

[0076] The various embodiments in this application are described in a progressive manner. Similar portions between the various embodiments can be referred to in conjunction with each other. Each embodiment focuses on the differences between the other embodiments. In particular, the device and medium embodiments are generally similar to the method embodiments, so their descriptions are relatively simple. For relevant portions, refer to the descriptions of the method embodiments.

[0077] The devices and media provided in the embodiments of the present application correspond one-to-one to the methods. Therefore, the devices and media also have similar beneficial technical effects to their corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the devices and media will not be repeated here.

[0078] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0079] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0080] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0081] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0082] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0083] Memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0084] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0085] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0086] The foregoing is merely an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the specification of the present application.

Claims

1. A method for assisting the association of UAV target tracks, characterized in that: The method comprises: According to the echo signal in the target airspace, the Doppler dimension slices of each point are used to calculate the point score of the main peak point trace micro-motion characteristics to obtain the main peak point trace score; Based on the point membership corresponding to the main peak point score, the Doppler signal of the target track is non-coherently accumulated to identify and determine the target marked drone; The target marked UAV is subjected to track correlation matching processing to obtain the latest Doppler signal under the correlation point track.

2. The method for assisting the association of unmanned aerial vehicle target tracks according to claim 1, characterized in that: Before calculating the trace score of the main peak trace micro-motion feature on the Doppler dimension slices of each trace based on the echo signal in the target airspace to obtain the main peak trace score, the method further includes: performing a series of signal processing on the echo signal; according to Get the CFAR detection threshold T CFAR ; where α is the scaling factor, N ref is the reference window length, x k is the sample value of noise / clutter in the reference window, k is a mathematical constant; Based on the interval between the drone body and the first harmonic, the corresponding harmonic interval is calculated and obtained; According to Δf min =f min ·N PRT PRT, to obtain the minimum Doppler unit Δf based on the harmonic spacing min ; Among them, f min is the minimum frequency interval, N PRT is the number of pulses in a coherent integration time, and PRT is the pulse repetition period; According to Δf max =f max ·N PRT PRT, to obtain the maximum Doppler bin Δf based on the harmonic spacing max ; Among them, f max is the maximum frequency interval.

3. The method for assisting the association of UAV target tracks according to claim 2, characterized in that: According to the echo signal in the target airspace, the Doppler dimension slices of each point are sliced ​​to calculate the point score of the main peak point trace micro-motion characteristics to obtain the main peak point trace score, which specifically includes: Based on the Doppler slices of each point in the target airspace, if there are multiple detection points exceeding the CFAR detection threshold in the same Doppler slice, the point with the largest amplitude is selected as the main peak point and the corresponding frequency unit is determined; according to Get the frequency interval unit Δf between other detected points and the main peak point in the current slice i ; Among them, f main is the frequency unit, is the frequency unit corresponding to the i-th detection point; according to Get the main peak trace micro-motion feature score F main ; Wherein, I(·) is an indicator function, and when the indicator function meets the conditions, the value is 1, otherwise the indicator function is 0; N is the total number of other over-detection points in the current slice, k is the harmonic order; w k is the weight coefficient and is related to the harmonic order; when k is a mathematical constant, it is related to w k It is a one-to-one correspondence; Based on the main peak trace micro-motion feature score, the main peak trace score is updated and obtained.

4. The method for assisting the association of unmanned aerial vehicle target tracks according to claim 1, characterized in that: Before performing non-coherent accumulation of the Doppler signal of the target track based on the point track membership corresponding to the main peak point track score to discriminate and determine the target marking drone, the method further includes: If the harmonic interval of the trace in the target airspace is within a preset range, then there is a positive correlation between the main peak trace score and the degree of membership attributable to the drone trace; wherein, the higher the main peak trace score, the higher the probability that the corresponding trace is a drone trace; Based on the positive correlation, when the track of the aerial target is batched, the Doppler signal of the associated point track is synchronously saved; According to the established historical track target, the historical Doppler signal in the historical track target is extracted, and the frequency with the maximum amplitude in each frame is correspondingly extracted.

5. The method for assisting the association of unmanned aerial vehicle target tracks according to claim 4, characterized in that: Perform non-coherent accumulation of the Doppler signal of the target track to identify and determine the target marking drone, specifically including: Aligning the main peaks of multiple frames corresponding to the maximum amplitude frequency in each frame to the same frequency unit; according to Get the amplitude of the main peak Among them, s i is the Doppler signal after alignment of the i-th main peak, N is the number of accumulated frames, and i is a mathematical constant; according to Get the average amplitude of the four largest peaks adjacent to the main peak Among them, p k is the amplitude of the kth peak, k is a mathematical constant; When satisfied When , the track corresponding to the main peak track score is identified as the target marked UAV; otherwise, the track is identified as a non-UAV; wherein, N avg is the average noise level; T is the adjustment threshold; the sum of the average noise level and the adjustment threshold is the UAV track detection threshold.

6. The method for assisting the association of UAV target tracks according to claim 1, characterized in that: Before performing track correlation matching processing on the target marking UAV to obtain the latest Doppler signal under the correlation point track, the method further includes: According to the target track corresponding to the target marking UAV, all candidate track points within the associated range are screened out; Sorting the main peak point track micro-motion feature scores corresponding to the candidate point tracks to determine the point track micro-motion feature score with the highest score; and performing track correlation processing on the point track micro-motion feature score with the highest score to obtain first correlation data; If there are multiple tracks with the highest and identical track micro-motion feature scores in the score ranking, then the spatial distance between the track with the highest and identical track micro-motion feature scores and the track prediction position is compared to determine the track with the closest distance; Perform track association processing on the point track with the closest distance to obtain second association data.

7. The method for assisting the association of unmanned aerial vehicle target tracks according to claim 6, characterized in that: The target marked UAV is subjected to track correlation matching processing to obtain the latest Doppler signal under the correlation point track, specifically including: Performing data association matching on the track of the current UAV based on the first association data and the second association data associated with the target marked UAV to obtain association result data; Based on the association result data, the track data of the current UAV is updated, and the latest Doppler signal under the completed association point track is saved.

8. The method for assisting the association of unmanned aerial vehicle target tracks according to claim 1, characterized in that: If the track is determined to be a non-UAV track, the nearest neighbor algorithm is used to select the point track with the closest spatial distance to the track prediction position for association processing to obtain the third association data; Based on the third correlation data, the track of the current UAV is correlated and matched and updated accordingly to determine the latest Doppler signal under the correlated point track.

9. A drone target track auxiliary association device, characterized in that: The device comprises: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, so that the at least one processor can execute the UAV target track auxiliary association method according to any one of claims 1-8.

10. A non-volatile computer storage medium, characterized in that The storage medium is a non-volatile computer-readable storage medium, which stores at least one program. Each of the programs includes instructions. When the instructions are executed by the terminal, the terminal executes the UAV target track auxiliary association method according to any one of claims 1 to 8.

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