Test method and system for detecting drone countermeasure system

Abstract

The application provides a test method and system for detecting an unmanned aerial vehicle countermeasure system, wherein the method comprises: controlling the unmanned aerial vehicle to execute a preset test scene; acquiring structured parameters, video monitoring streams and countermeasure platform monitoring data of the unmanned aerial vehicle, aligning the structured parameters, the video monitoring streams and the countermeasure platform monitoring data, extracting the position of the unmanned aerial vehicle in the video monitoring streams through a target detection model, and performing reliable comparison and verification on the position of the unmanned aerial vehicle and the structured parameters and the countermeasure platform monitoring data at the alignment time; fusing the position of the unmanned aerial vehicle after reliable comparison and verification with the structured parameters and the countermeasure platform monitoring data to obtain fused parameters; performing multidimensional compliance judgment on the fused parameters to obtain an evaluation result; and displaying the evaluation result through a visual interface and generating a test report. The application realizes cross verification of multi-source data by fusing the structured parameters, the video monitoring data and the countermeasure platform data returned by the unmanned aerial vehicle, and ensures that the fusion and compliance judgment are real and effective.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) surveillance and processing system technology, and in particular to a test method and system for detecting UAV countermeasure systems. Background Technology

[0002] With the development of the low-altitude economy and the widespread application of civilian drones, the requirements for the safety management of drone countermeasure systems are constantly increasing, in order to detect, identify, track and counter civilian drones entering specific airspaces and ensure low-altitude safety.

[0003] In related technologies, current industry testing of surveillance and response systems mainly adopts the following methods: using the UAV itself as the test object to verify unidirectional indicators such as flight parameters and anti-interference capabilities, reflecting basic countermeasure capabilities; using parameters such as GPS coordinates, speed, and altitude passively transmitted by the UAV as the main data source; outputting test results in the form of raw data or manually analyzed reports; and primarily using conventional flight conditions as test scenarios. However, the above testing methods have the following drawbacks: limited test objects, unable to comprehensively verify the true capabilities of the system, and failing to conduct systematic functional and performance testing of the entire surveillance and response system; single test data source, lacking multi-source fusion verification, resulting in low reliability of test results; lack of a direct comparison process with countermeasure platform data, making it impossible to assess the accuracy of the countermeasure system's own monitoring data; and non-standardized output results, with low efficiency and unintuitive presentation of manually analyzed data.

[0004] Based on the above analysis of the development status of this technology field, the existing technologies lack a detection scheme for drone countermeasure systems that aligns and integrates structured parameters, links video monitoring streams with countermeasure platform data for comparison. Summary of the Invention

[0005] The purpose of this invention is to provide a testing method and system for detecting unmanned aerial vehicle (UAV) countermeasure systems, aiming to solve the aforementioned problems in the prior art.

[0006] According to a first aspect of the present invention, a test method for detecting unmanned aerial vehicle (UAV) countermeasure systems is provided, comprising: Control the drone to execute a preset test scenario; The structured parameters of the UAV, video monitoring stream, and countermeasure platform monitoring data are obtained. The structured parameters, video monitoring stream, and countermeasure platform monitoring data are aligned. The UAV position in the video monitoring stream is extracted through the target detection model. The UAV position is reliably compared and verified with the structured parameters and countermeasure platform monitoring data at the alignment time. The drone's location, structured parameters, and countermeasure platform monitoring data, which have been reliably compared and verified, are fused together to obtain the fused parameters. The parameters after fusion are assessed for compliance in multiple dimensions to obtain the evaluation results; The evaluation results are displayed through a visual interface, and test reports are generated.

[0007] According to a second aspect of the present invention, a test system for detecting unmanned aerial vehicle (UAV) countermeasure systems is provided, comprising: The control and startup module is used to control the drone to execute preset test scenarios; The data acquisition and verification module is used to acquire the structured parameters of the UAV, the video monitoring stream, and the countermeasure platform monitoring data. It aligns the structured parameters, the video monitoring stream, and the countermeasure platform monitoring data, extracts the UAV position in the video monitoring stream through the target detection model, and reliably compares and verifies the UAV position with the structured parameters and the countermeasure platform monitoring data at the alignment time. The data fusion module is used to fuse the UAV location and structured parameters, as well as the monitoring data from the countermeasure platform, which have been reliably compared and verified, to obtain the fused parameters. The compliance assessment module is used to perform multi-dimensional compliance assessments on the merged parameters to obtain evaluation results. The visualization module is used to display evaluation results and generate test reports through a visual interface.

[0008] According to a third aspect of the present invention, an electronic device is provided, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the test method for detecting unmanned aerial vehicle countermeasure systems as provided in the first aspect of the present disclosure.

[0009] According to a fourth aspect of the present invention, a computer-readable storage medium is provided, on which an information transmission implementation program is stored, which, when executed by a processor, implements the steps of the test method for detecting unmanned aerial vehicle countermeasure systems provided in the first aspect of the present disclosure.

[0010] The technical solution provided by the embodiments of the present invention has the following beneficial effects: it integrates the structured parameters transmitted by the UAV, video monitoring data and countermeasure platform monitoring, and achieves cross-verification of multi-source data through sensor-level integration, feature-level alignment and decision-level dual verification. On this basis, the fused structured performance indicators are obtained to ensure that the fusion and compliance judgment are true and effective. The solution as a whole realizes full-process automation from data collection, fusion processing, performance evaluation and report generation, which significantly improves testing efficiency.

[0011] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in one or more embodiments of this specification or in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this specification. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 This is a flowchart of a test method for detecting a drone countermeasure system according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the test architecture according to an embodiment of the present invention; Figure 3 This is a schematic diagram of multi-source data fusion according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the overall testing process according to an embodiment of the present invention; Figure 5 This is a schematic diagram of a test comparison table according to an embodiment of the present invention; Figure 6 This is a schematic diagram illustrating the automated report generation according to an embodiment of the present invention; Figure 7 This is a schematic diagram of the hardware configuration connection according to an embodiment of the present invention; Figure 8 This is a schematic diagram of a test system for detecting unmanned aerial vehicle (UAV) countermeasure systems according to an embodiment of the present invention; Figure 9 This is a schematic diagram of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0014] To enable those skilled in the art to better understand the technical solutions in one or more embodiments of this specification, the technical solutions in one or more embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this specification, and not all of the embodiments. Based on one or more embodiments of this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of this document.

[0015] Method Implementation Examples According to embodiments of the present invention, a test method for detecting unmanned aerial vehicle (UAV) countermeasure systems is provided. Figure 1 This is a flowchart of a test method for detecting a drone countermeasure system according to an embodiment of the present invention, such as... Figure 1 As shown, the test method for detecting a drone countermeasure system according to an embodiment of the present invention specifically includes: In step S110, the drone is controlled to execute a preset test scenario, specifically including: In this embodiment of the invention, the reference source control module includes a MAVLink ground station and a video stream server, and controls the drone to execute a preset path in a preset test scenario through an SDK toolkit.

[0016] The drone uses a reference source drone with programmable parameters, which can inject abnormal parameters such as low battery (≤20%), fake GPS, and communication interruption. The recorded command delay is ≤10 seconds, and the deviation of the return path is ≤2m. It actively broadcasts standard messages that meet the specifications, including information such as unique identification code, position, altitude and speed, and supports multiple protocol interfaces such as MAVLink 2.0, GB / T28181, and SIP.

[0017] In step S120, the structured parameters of the UAV, the video monitoring stream, and the countermeasure platform monitoring data are acquired. The structured parameters are aligned with the video monitoring stream and the countermeasure platform monitoring data. The UAV position in the video monitoring stream is extracted using a target detection model. The UAV position is reliably compared and verified with the structured parameters and countermeasure platform monitoring data at the alignment time. Specifically, this includes: Enable multi-source data fusion mechanism; Sensor-level integration: Initial integration of structured parameters, raw video surveillance streams, and countermeasure platform monitoring data; The MAVLink ground station acquires GPS structured parameters generated by the drone, including GPS coordinates, speed, altitude, and other parameters, and the video monitoring stream captured by the drone is obtained through the video streaming server.

[0018] Feature-level alignment: Spatiotemporal alignment is achieved using Kalman filtering and interpolation algorithms, with an error ≤0.15 meters and a time error ≤30 ms, ensuring that the compared data are from the same time. Obtain GPS timestamps for structured parameters and UTC timestamps for each frame of the video monitoring stream. Output UAV parameters every 100ms with GPS timestamps, output video at 30fps with UTC timestamps, and output countermeasure platform data at fixed intervals with system timestamps. The alignment time is determined based on the GPS timestamp, UTC timestamp, and system timestamp. The obtained structured parameters, video monitoring stream, and countermeasure platform monitoring data are smoothed using the Kalman filter method. The video monitoring stream is converted into numerical state variables and then smoothed. The structured parameters can be directly smoothed. Then, the alignment is performed using the linear interpolation fill method.

[0019] Preferably, the noise parameters of the standard Kalman filter are fixed, but the noise of the UAV is low when hovering and high when flying at high speed. In this embodiment of the invention, an adaptive Kalman filter is used instead of the standard Kalman filter. A lightweight machine learning model is built into the filter to determine the flight state. When hovering, the filter is strengthened to eliminate all jitter, the standard filter is used in the uniform straight line state, and the filter is weakened when making sharp turns.

[0020] Decision-level judgment: Extract the location of the drone in the video and compare it with structured parameters and monitoring data from the countermeasure platform for verification; Use the YOLOv8 model to extract the drone position in each frame of the video monitoring stream; The purpose of comparative verification is to verify the consistency, filter anomalies, and confirm the credibility of the benchmark source output data, video perception data, and countermeasure platform monitoring. The aim is to eliminate invalid data caused by benchmark source drift, video occlusion, positioning jumps, and communication jitter, and to ensure that the data entering the subsequent evaluation is true, reliable, and without significant deviation, thereby avoiding distortion of test results.

[0021] To ensure verification is conducted in a unified spatial coordinate system, specifically by converting the drone's GPS latitude, longitude, and altitude to the world coordinate system; calibrating the camera's intrinsic and extrinsic parameters on video frames; detecting the drone's pixel bounding boxes using YOLOv8; and then calculating the visual position in the world coordinate system, thus uniformly converting the countermeasure platform data to the same world coordinate system.

[0022] The positional deviations of the UAV position and structured parameters, as well as the countermeasure platform data, are calculated in the horizontal and vertical directions respectively. If the comparison of the two types of data meets the preset deviation thresholds in both the horizontal and vertical directions, the data is judged to be consistent and passes the reliable comparison verification. In this embodiment of the invention, the horizontal deviation is ≤2m and the vertical deviation is ≤15m, and the data is judged to be consistent. Data that fails reliable comparison verification is discarded. If more than a preset number of frames are discarded consecutively, the entire test segment is discarded. In this embodiment of the invention, if three consecutive frames are abnormal, it is determined that the reference source, video or link is faulty, the test segment is invalidated, a retest is automatically triggered, and the type and frequency of data anomalies of the countermeasure platform are recorded.

[0023] If a reliable comparison verification fails, the data will not be integrated or further statistically and in compliance assessments. Verification failure indicates that the data is unreliable, and continuing the assessment will lead to distorted conclusions. At the same time, it can quickly locate and counteract platform data anomalies.

[0024] In step S130, the UAV's location and structured parameters, which have been reliably compared and verified, are fused with the monitoring data from the countermeasure platform to obtain fused parameters, specifically including: The three sequence characteristics are obtained by acquiring the UAV location sequence, structured parameter sequence, and countermeasure platform monitoring data sequence within a preset window; The three sequence features are input into a pre-trained temporal attention network model. The computational weights of the UAV position, structured parameters, and countermeasure platform monitoring data are adjusted through the temporal attention network model. The fused parameters are output by weighted summation based on the computational weights. The fused parameters are not the original images or original parameters, but rather structured performance indicators after alignment, verification, and fusion. These indicators, as measured values, can be used to determine the horizontal positioning error, vertical positioning error, response delay, return path deviation, recognition success rate, tracking stability, and data consistency deviation of the countermeasure platform after fusion.

[0025] In step S140, a multi-dimensional compliance assessment is performed on the fused parameters to obtain the evaluation results, specifically including: After cross-validation, the fused data is then used for performance evaluation. Obtain the preset rule base GB42590 clauses, including hard indicators such as horizontal error ≤2m, handling delay ≤10s, and return deviation ≤2m; The compliance of indicators is judged based on a preset rule base. Stability compliance is judged based on the compliance of indicators in consecutive frames. The stable output of the system is verified when the indicators in multiple consecutive frames meet the standards. The trend compliance is judged based on the convergence of the parameter error after fusion. Furthermore, the consistency compliance of fused data and countermeasure data can be compared. Preferably, the consistency can be judged again based on the continuous controllability of GPS and visual deviation and countermeasure platform data deviation to ensure high data credibility. Compliance assessment is not based on a single threshold, but rather on a comprehensive evaluation across multiple dimensions. In step S150, the evaluation results are displayed through a visual interface, and a test report is generated. Specifically, this includes automatically generating a structured comparison table and an interactive report, with support for automatic email distribution.

[0026] Further explanation of the architecture used in this method: The method is applied in a three-tier architecture of "end-edge-cloud", setting up a baseline source control module, a data acquisition module, a multi-source data fusion engine, a test analysis database, an automated report generator, and a visual web interface, which can be used as a test system for countermeasure platforms. The system comprises several components: a baseline source control module with a MAVLink ground station and video stream server, which uses an SDK to control the baseline UAV to execute preset test scenarios; a data acquisition module that collects UAV parameters, video stream data, countermeasure platform monitoring data, and response system feedback data; a multi-source data fusion engine that performs sensor-level, feature-level, and decision-level data processing, as well as data fusion logic; a countermeasure data comparison and analysis module that verifies the consistency of baseline, video, and countermeasure platform data; a test analysis rule base that incorporates GB42590 national standard thresholds and enterprise-defined standards; an automated report generator that automatically generates test reports in HTML / PDF / Excel formats; and a visual web interface that displays data, countermeasure comparison results, and monitors test status in real time.

[0027] Figure 2 This is a schematic diagram of the test architecture according to an embodiment of the present invention, as shown below. Figure 2 As shown, this demonstrates the framework for testing a three-tier architecture.

[0028] The above technical solutions of the embodiments of the present invention will be illustrated with reference to the following accompanying drawings.

[0029] Figure 3 This is a schematic diagram of multi-source data fusion according to an embodiment of the present invention, such as... Figure 3 As shown, the process involves data integration, feature alignment, comparative verification decision-making, countermeasure data consistency verification, and compliance determination after fusion. This includes original sensor alignment, feature-level spatiotemporal high-precision alignment, and decision-level comparison and verification of YOLOv8 / GPS data with the countermeasure platform, forming a four-layer closed-loop verification. Figure 4 This is a schematic diagram of the overall testing process according to an embodiment of the present invention, as shown below. Figure 4 As shown, the test method is demonstrated from start to finish. The monitoring function test refers to the reference source UAV flying along a preset path, the video system extracting the visual position, calculating the deviation between GPS and visual position, and the deviation between GPS / visual position and countermeasure platform position. The handling function test refers to injecting abnormal signals such as low battery to trigger return-to-home / hover commands, recording the command delay ≤10 seconds, and comparing the return-to-home path deviation ≤2m. The performance index evaluation refers to automatically comparing the measured values ​​with the standard values ​​according to GB42590 to determine compliance, and simultaneously outputting the data consistency evaluation results of the countermeasure platform.

[0030] Figure 5 This is a schematic diagram of the test comparison table of an embodiment of the present invention, as shown below. Figure 5 As shown, an example of the results of compliance assessment and countermeasure data comparison is presented; Figure 6 This is a schematic diagram illustrating the automated report generation of an embodiment of the present invention, such as... Figure 6 The image shows the report generation process; Figure 7 This is a schematic diagram of the hardware configuration connection according to an embodiment of the present invention, such as... Figure 7 As shown, this illustrates the architecture of a switching device connecting to a cloud server.

[0031] In summary, addressing the existing problems, this invention presents a testing method for detecting drone countermeasure systems. This method integrates structured parameters transmitted by the drone, video monitoring data, and countermeasure platform monitoring data, overcoming the limitation of existing tests that only reflect the drone's own performance. Through sensor-level integration, feature-level alignment, and decision-level verification, it achieves cross-validation of multi-source data. Reliable comparative verification ensures that data that fails verification is not included in the statistical analysis of measured values ​​and compliance assessment. Based on this, a fused structured performance index is obtained, ensuring the authenticity and effectiveness of the fusion and compliance judgment. The compliance judgment process adheres to national standards, making the testing process standardized, reproducible, and certifiable. The judgment method is intuitive and quantifiable, facilitating product iteration and qualification application. The overall solution automates the entire process from data acquisition, fusion processing, performance evaluation, to report generation, significantly improving testing efficiency.

[0032] System Implementation Examples According to embodiments of the present invention, a test system for detecting unmanned aerial vehicle (UAV) countermeasure systems is provided. Figure 8 This is a schematic diagram of a test system for detecting unmanned aerial vehicle (UAV) countermeasure systems according to an embodiment of the present invention, as shown below. Figure 8 As shown, the test system for detecting unmanned aerial vehicle (UAV) countermeasure systems according to an embodiment of the present invention specifically includes: The control startup module 80 is used to control the drone to execute preset test scenarios, specifically for: Control the drone to execute a preset path in a preset test scenario using the SDK toolkit.

[0033] The data acquisition and verification module 82 is used to acquire the structured parameters of the UAV, the video monitoring stream, and the countermeasures platform monitoring data; align the structured parameters, video monitoring stream, and countermeasures platform monitoring data; extract the UAV position from the video monitoring stream using a target detection model; and reliably compare and verify the UAV position with the structured parameters and countermeasures platform monitoring data at the alignment time. Specifically, it is used for: The system acquires GPS structured parameters generated by the UAV through the MAVLink ground station, obtains video monitoring streams captured by the UAV through the video streaming server, and synchronizes the monitoring data output by the countermeasure platform in real time through the communication interface.

[0034] Obtain the GPS timestamps of structured parameters, the UTC timestamps of each frame in the video monitoring stream, and the system timestamps of the countermeasures platform monitoring data; The alignment time is determined based on the GPS timestamp, UTC timestamp, and system timestamp. The obtained structured parameters and video monitoring stream are smoothed using the Kalman filter method, and then the alignment is performed using the linear interpolation fill method. The location of the drone in each frame of the video monitoring stream was extracted using the YOLOv8 model.

[0035] The positional deviations of the UAV position and structured parameters, and the countermeasure platform monitoring data in the horizontal and vertical directions are calculated respectively. If the preset deviation thresholds are met in both the horizontal and vertical directions, the data are judged to be consistent and verified by reliable comparison. Data that fails reliable comparison verification will be discarded. If more than a preset number of frames are discarded consecutively, the entire test segment will be discarded.

[0036] Data fusion module 84 is used to fuse the UAV position and structured parameters that have been reliably compared and verified with the monitoring data of the countermeasure platform to obtain fused parameters, specifically used for: The three-sequence features are obtained by acquiring the UAV location sequence, structured parameter sequence, and countermeasure platform monitoring data within a preset window; The three sequence features are input into a pre-trained temporal attention network model. The computational weights of the UAV position, structured parameters, and countermeasure platform monitoring data are adjusted through the temporal attention network model. The fused parameters are output based on the computational weights.

[0037] The compliance assessment module 86 is used to perform multi-dimensional compliance assessments on the fused parameters to obtain evaluation results. Specifically, it is used for: Obtain the preset rule base; The compliance of indicators is judged based on a preset rule base, the stability compliance is judged based on the compliance of indicators in continuous frames, the trend compliance is judged based on the convergence of parameter errors after fusion, and the consistency compliance of fused data and countermeasure platform data is compared.

[0038] The visualization module 88 is used to display evaluation results and generate test reports through a visual interface.

[0039] In summary, addressing the existing problems, this invention presents a testing system for detecting drone countermeasure systems. This system integrates structured parameters transmitted from the drone, video monitoring data, and countermeasure platform monitoring data, overcoming the limitation of existing tests that only reflect the drone's own performance. Through sensor-level integration, feature-level alignment, and decision-level verification, it achieves cross-validation of multi-source data. Reliable comparison verification ensures that data that fails verification is not included in the statistical analysis and compliance assessment. Simultaneously, it performs consistency comparisons between countermeasure platform data and benchmark / video data, accurately verifying the reliability of the countermeasure system monitoring. Based on this, it obtains fused structured performance indicators, ensuring the authenticity and effectiveness of the fusion and compliance judgments. The compliance judgment process adheres to national standards, making the testing process standardized, reproducible, and certifiable. The judgment methods are intuitive and quantifiable, facilitating product iteration and qualification applications. The overall solution automates the entire process from data acquisition, fusion processing, countermeasure data comparison, performance evaluation, and report generation, significantly improving testing efficiency.

[0040] Electronic device examples Figure 9 This is a schematic diagram of an electronic device according to an embodiment of the present invention. The electronic device 900 may include at least one processor 910 and a memory 920. The processor 910 can execute instructions stored in the memory 920. The processor 910 is communicatively connected to the memory 920 via a data bus. In addition to the memory 920, the processor 910 can also be communicatively connected to an input device 930, an output device 940, and a communication device 950 via the data bus.

[0041] Processor 910 can be any conventional processor, such as a commercially available CPU. Processors may also include graphics processing units (GPUs), field-programmable gate arrays (FPGAs), systems on chips (SOCs), application-specific integrated circuits (ASICs), or combinations thereof.

[0042] The memory 920 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.

[0043] In this embodiment of the present disclosure, the memory 920 stores executable instructions, and the processor 910 can read the executable instructions from the memory 920 and execute the instructions to implement all or part of the steps of the test method for detecting unmanned aerial vehicle countermeasure systems in any of the exemplary embodiments described above.

[0044] Computer-readable storage medium embodiments In addition to the methods and systems described above, exemplary embodiments of this disclosure may also be a computer program product or a computer-readable storage medium storing the computer program product, the computer product including computer program instructions that can be executed by a processor to implement all or part of the steps described in the test method for detecting any of the drone countermeasure systems in the exemplary embodiments described above.

[0045] Computer program products can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments of this application. Programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages, and scripting languages ​​(e.g., Python). The program code can be executed entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0046] Computer-readable storage media may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example,, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media include: static random access memory (SRAM) having one or more electrically connected wires; 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; or any suitable combination thereof.

[0047] 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. A test method for detecting unmanned aerial vehicle (UAV) countermeasure systems, characterized in that, include: Control the drone to execute a preset test scenario; The structured parameters of the UAV, the video monitoring stream, and the countermeasure platform monitoring data are obtained. The structured parameters, the video monitoring stream, and the countermeasure platform monitoring data are aligned. The UAV position in the video monitoring stream is extracted through a target detection model. The UAV position is reliably compared and verified with the structured parameters and the countermeasure platform monitoring data at the alignment time. The drone's location, structured parameters, and countermeasure platform monitoring data, which have been reliably compared and verified, are fused together to obtain the fused parameters. A multi-dimensional compliance assessment is performed on the fused parameters to obtain the evaluation results; The evaluation results are displayed through a visual interface, and a test report is generated.

2. The method according to claim 1, characterized in that, The control of the drone to execute the preset test scenario specifically includes: controlling the drone to execute the preset path in the preset test scenario through the SDK toolkit.

3. The method according to claim 1, characterized in that, The acquisition of the structured parameters, video monitoring stream, and countermeasure platform monitoring data of the UAV specifically includes: The system acquires GPS structured parameters generated by the UAV through the MAVLink ground station, obtains video monitoring streams captured by the UAV through the video streaming server, and synchronizes the monitoring data output by the countermeasure platform in real time through the communication interface.

4. The method according to claim 1, characterized in that, The step of aligning the structured parameters, the video monitoring stream, and the countermeasure platform monitoring data, and extracting the drone position from the video monitoring stream using a target detection model specifically includes: Obtain the GPS timestamp of structured parameters, the UTC timestamp of each frame of the video monitoring stream, and the system timestamp of the countermeasure platform monitoring data; The alignment time is determined based on the GPS timestamp, the UTC timestamp, and the system timestamp. The obtained structured parameters, video monitoring stream, and countermeasure platform monitoring data are smoothed using Kalman filtering, and then aligned using linear interpolation. The location of the drone in each frame of the video monitoring stream was extracted using the YOLOv8 model.

5. The method according to claim 1, characterized in that, The reliable comparison and verification of the UAV's position with the structured parameters at the alignment time and the countermeasure platform monitoring data specifically includes: The positional deviations of the UAV position and structured parameters, and the countermeasure platform monitoring data in the horizontal and vertical directions are calculated respectively. If the preset deviation thresholds are met in both the horizontal and vertical directions, the data are judged to be consistent and verified by reliable comparison. Data that fails the reliable comparison verification will be discarded. If more than a preset number of frames are discarded consecutively, the entire test segment will be discarded.

6. The method according to claim 1, characterized in that, The process of fusing the reliably verified UAV location with structured parameters and countermeasure platform monitoring data to obtain fused parameters specifically includes: The three sequence characteristics are obtained by acquiring the UAV location sequence, structured parameter sequence, and countermeasure platform monitoring data sequence within a preset window; The three sequence features are input into a pre-trained temporal attention network model. The calculation weights of the UAV position, structured parameters, and countermeasure platform monitoring data are adjusted through the temporal attention network model. The fused parameters are output based on the calculation weights.

7. The method according to claim 1, characterized in that, The multi-dimensional compliance assessment of the fused parameters to obtain the evaluation results specifically includes: Obtain the preset rule base; Based on the preset rule base, the compliance of the indicators is judged; based on the compliance of the indicators in consecutive frames, the stability compliance is judged; and based on the convergence degree of the parameter error after fusion, the trend compliance is judged.

8. A testing system for detecting unmanned aerial vehicle (UAV) countermeasure systems, characterized in that, include: The control and startup module is used to control the drone to execute preset test scenarios; The acquisition and verification module is used to acquire the structured parameters of the UAV, the video monitoring stream, and the countermeasure platform monitoring data, align the structured parameters, the video monitoring stream, and the countermeasure platform monitoring data, extract the UAV position in the video monitoring stream through a target detection model, and reliably compare and verify the UAV position with the structured parameters and the countermeasure platform monitoring data at the alignment time. The data fusion module is used to fuse the UAV location and structured parameters, as well as the monitoring data from the countermeasure platform, which have been reliably compared and verified, to obtain the fused parameters. The compliance assessment module is used to perform multi-dimensional compliance assessments on the fused parameters to obtain evaluation results. The visualization module is used to display the evaluation results and generate test reports through a visual interface.

9. An electronic device, characterized in that, include: The memory, the processor, and the computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the test method for detecting a drone countermeasure system as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores an information transmission implementation program, which, when executed by a processor, implements the steps of the test method for detecting a drone countermeasure system as described in any one of claims 1 to 7.

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