Multi-mode reconfigurable unmanned aerial vehicle prevention and control system test evaluation method and system

Abstract

The application discloses a multi-mode reconfigurable unmanned aerial vehicle prevention and control system test evaluation method and system, which comprises the following steps: constructing a unified space-time reference system and initializing a test environment; adaptively networking and signal configuring a multi-mode reconfigurable target aircraft cluster, realizing target aircraft cluster state monitoring and control; realizing high-precision and high-reliability positioning and data backhaul of the target aircraft in a multi-mode navigation signal environment; through test area gridding and sampling point optimized layout, comprehensively and accurately evaluating the performance of the prevention and control system, designing a multi-point sampling verification algorithm; and according to the evaluation result, realizing standardized test report generation and parameter iterative optimization. Through test reference system construction, the space-time reference system is formulated, system error is reduced, evaluation accuracy is improved, a positioning backhaul algorithm is designed, unmanned aerial vehicle original data is efficiently acquired, and an automatic test evaluation algorithm is designed, so that the evaluation efficiency is improved.

Description

Technical Field

[0001] This invention belongs to the field of drone prevention and control and testing and evaluation technology, and particularly relates to a testing and evaluation method and system for drone prevention and control systems based on multi-standard reconfigurable systems. Background Technology

[0002] With the rapid popularization of drone technology and the continuous reduction in costs, small drones have been widely used in the civilian sector, but this has also brought serious safety hazards. To address this challenge, various drone control systems have emerged, including radar detection systems, radio jamming systems, photoelectric tracking systems, and laser countermeasure systems. However, the current market for drone control equipment is diverse, with numerous manufacturers and varying performance indicators, lacking unified testing and evaluation standards and methods. Traditional drone control system testing methods mainly rely on manual operation, using a single type of target drone for simple flight tests. This can only roughly assess the basic functions of the control system and cannot comprehensively and accurately reflect the system's actual performance in complex real-world environments. This testing method is not only inefficient and costly, but also yields results with poor reliability and comparability, making it difficult to meet the needs of large-scale, standardized equipment screening and system acceptance.

[0003] Existing testing and evaluation technologies for UAV (Unmanned Aerial Vehicle) defense systems suffer from several technical shortcomings. First, the lack of a unified spatiotemporal reference system leads to significant distortions in test data due to time synchronization errors and coordinate system differences between different testing equipment, making it impossible to accurately calculate key performance indicators such as detection range, tracking accuracy, and reaction time. Second, most test target drones can only simulate a single navigation signal standard, failing to simulate complex scenarios in real-world environments where multiple navigation signals (BDS, GPS, GLONASS, etc.) coexist, or where multiple UAVs coordinate attacks, resulting in substantial discrepancies between test results and actual combat conditions. Third, the limited number and uneven distribution of test data sampling points, coupled with a lack of effective multi-point sampling verification mechanisms, makes the data susceptible to unpredictable factors and unable to comprehensively reflect the performance distribution of the defense system across the entire protected area, severely hindering the development and widespread application of UAV defense technology. Given the current needs for defense against low-speed, small UAVs in core areas, establishing a comprehensive UAV defense system is costly and lacks a standardized technical evaluation system, making it difficult to select suitable facilities and equipment. Therefore, a new testing and evaluation method for UAV defense systems is urgently needed. Summary of the Invention

[0004] This invention provides a testing and evaluation method and system for a multi-mode reconfigurable UAV prevention and control system, which solves the technical problems of large differences in time synchronization during testing and evaluation of current multi-mode reconfigurable UAV prevention and control systems, difficulty in dealing with multiple signal types, weak test data sampling, and poor test and evaluation results.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: On the one hand, this invention provides a testing and evaluation method for a multi-mode reconfigurable unmanned aerial vehicle (UAV) defense system, the method comprising: S1: Utilize the Coordinated Universal Time System (UTC) and WGS84 coordinate system to construct a unified spatiotemporal reference system and complete the test environment initialization and error calibration. S2: Based on a unified spatiotemporal reference system, adaptive networking and signal configuration of multi-standard reconfigurable target drone clusters are carried out. Multi-standard navigation signal reconfigurable configuration algorithm and target drone cluster adaptive networking algorithm are designed to realize target drone cluster status monitoring and control; the multi-standard reconfigurable target drone cluster consists of multiple test drones that are compatible with multiple communication signals for testing on the same hardware platform. S3: Design a multi-frequency, multi-system positioning and backhaul algorithm to obtain the target drone's positioning location; establish a navigation signal quality assessment model based on the target drone's positioning location, and design an adaptive switching algorithm to realize the backhaul of the target drone's positioning data; S4: Perform multi-point sampling verification and quantitative evaluation of the performance of the control system based on the returned target drone positioning data; S5: Based on the evaluation results, generate standardized test reports and iteratively optimize parameters.

[0006] Further, step S1 includes: A high-precision atomic clock is deployed at the testing center as the main time source. The main time source obtains Coordinated Universal Time (UTC) by receiving UTC signals from a satellite time receiver. The network time protocol and precise time protocol of the test center are synchronized with the main time source; the network time protocol is obtained through an NTP server; the precise time protocol is obtained through a PTP master clock. The WGS84 coordinate system is adopted as a unified coordinate system, and all position data of the target drone are converted to the WGS84 coordinate system for processing; After completing the construction of the unified spatiotemporal benchmark, remove electromagnetic interference sources and obstacles from the test area; The target drone is placed on a calibration point with known coordinates. Through multiple calibrations, the static positioning error of the target drone navigation system is controlled within a preset error threshold range. Based on the testing requirements and the application scenario of the prevention and control system, the scope and shape of the testing area are determined and the flight parameters of the target drone are configured.

[0007] Further, step S2 includes: Based on a unified spatiotemporal reference system, a multi-standard reconfigurable target drone hardware platform is designed. The target drone hardware platform includes a target drone navigation system. The positioning and feedback module of the target drone navigation system integrates radio frequency front-ends and baseband processing chips for multiple frequency points, including BDS B1I, BDS B2I, BDS B3I, GPSL1, GPS L2, GLONASS G1, and GLONASS G2, for simultaneously receiving and processing navigation signals of multiple standards. Design a reconfigurable algorithm for multi-standard navigation signals to reconfigure the target drone navigation system, supporting dynamic configuration of the received signal standard and frequency; specifically: Obtain the configuration parameter set of the target drone's navigation system, including the navigation system standard, operating frequency set, number of receiving channels, signal power, and positioning update cycle; the value of the navigation system standard is... , , , , , , BDS represents the signal of the BeiDou Navigation Satellite System, GPS represents the signal of the Global Positioning System, and GLONASS represents the signal of the GLONASS system. Calculate the position error of the navigation solution for: ; in, To locate the output position of the feedback module, This represents the actual location of the target drone. For navigation system error, is a constant obtained through calibration; The random error follows a normal distribution N(0,σ²), where σ is the standard deviation of the random error. Design an adaptive networking algorithm for the target machine cluster, specifically as follows: The target drone cluster consists of multiple test drones. The network of the target drone cluster is initialized by the ground station sending a network broadcast packet to all target drones to be networked. Upon receiving the broadcast packet, each target drone sends a response packet to the ground station. Based on the response information, the ground station establishes a node list for the target drone cluster and elects a master node. A weighted election algorithm is used to elect the master node, with each target drone having a specific weight. Due to its power Communication quality and computing power Joint decision: α, β, and γ are weighting coefficients that satisfy α + β + γ = 1. This is the target drone's maximum power. For maximum communication quality, To maximize computing power, the target machine with the highest weight is selected as the master node. If the master node fails or runs out of power, the ground station will re-elect a new master node to ensure the stable operation of the cluster. Design a target drone cluster status monitoring and control system. Each target drone sends status data packets to the ground station at a fixed frequency. These status data packets include the target drone's ID, timestamp, position, speed, acceleration, attitude angle, battery level, signal strength, and mission progress information. The target drone's health index is... Used to assess the operational status of the target drone: , , , , These are the weighting coefficients. , This represents the positioning error of the target drone. This is the positioning error threshold. For the speed error of the target drone, This is the speed error threshold; When the health index of the target drone falls below the set threshold, the ground station will issue a warning signal.

[0008] Further, step S3 includes: Design a multi-frequency, multi-system positioning backhaul algorithm and an adaptive handover algorithm, specifically as follows: Pseudorange and carrier phase observations are extracted from navigation signals of various systems. After obtaining the observations of various systems, all observations are combined to establish a unified observation equation. The weighted least squares method is used to solve for the optimal estimate of the parameter vector to be estimated, and the target drone's positioning position is obtained. Establish a navigation signal quality assessment model, the first A set of signal quality evaluation indicators for each standard , For the first The average carrier-to-noise ratio of all visible satellites of each standard. For the first Number of visible satellites in each system For the first Geometric precision factor of each standard. For the first The standard deviation of the positioning error for each standard For the first Signal lockout time for each standard; The signal quality evaluation index is normalized, and the normalized index value is... for: ; in, For the first The first standard The original values ​​of each indicator and The first The maximum and minimum values ​​of each indicator are defined as follows: positive indicators are those with larger values, indicating better signal quality; negative indicators are those with smaller values, indicating better signal quality. The rationality of the normalized indicator values ​​is verified through a consistency test, and the consistency ratio is [not specified]. for , As a consistency indicator, As the average random consistency index, when If the normalized index values ​​are considered to have satisfactory consistency, then the normalized index values ​​need to be adjusted. Calculate the first Comprehensive signal quality score for each standard ; in, The value range is [0,1]. The higher the score, the better the quality of the navigation signal of the system. Based on the navigation signal quality assessment model, an adaptive handover decision algorithm is designed, specifically: handover triggering conditions and handover recovery conditions are designed, assuming the currently used system is... Other candidate formats are The switch is triggered when the following conditions are met: (1) Overall signal quality score of the current standard ,in To switch the trigger threshold; (2) There is at least one candidate system Its overall signal quality score ,in To switch the recovery threshold, and ; (3) Candidate format Comprehensive signal quality score A higher overall signal quality score than the current standard And the difference is greater than , To switch the difference threshold; When all the above conditions are met, the system will switch from the current standard. Switch to the candidate standard with the highest overall signal quality score. .

[0009] Further, step S4 includes: The returned data is subjected to multi-point sampling verification and quantitative evaluation of the prevention and control system performance, specifically as follows: First, the test area is divided into grids, and the sampling points are laid out. After completing the layout of sampling points, for each sampling point, the number of tests shall not be less than 3, and the average value shall be taken as the test result of the sampling point. The multi-point sampling verification algorithm verifies the accuracy and reliability of the test results by calculating the consistency and credibility of the test results of multiple sampling points. Calculate the statistical characteristics of the test results for all sampling points, including the mean. Standard deviation Maximum value Minimum value and range : , , , , , For the first Test results for each sampling point This represents the number of sampling points; Outliers were removed using the Grubbs test. The cumulative error and maximum deviation of the test results for the remaining sampling points were calculated. , , This represents the number of sampling points after removing outliers. The nominal performance value of the control system; maximum deviation The maximum absolute error between the test results at all sampling points and the nominal performance value: ; If both the cumulative error and the maximum deviation are within the allowable range, the test results are considered consistent and reliable; otherwise, the cause of the error needs to be analyzed and the test needs to be repeated. After completing multi-point sampling verification, a quantitative evaluation model for the performance of the prevention and control system was established. The evaluation model divides the performance of the prevention and control system into four primary indicators: detection performance, tracking performance, interference performance and countermeasure performance. Each primary indicator is further divided into multiple secondary indicators. The weights of each performance index of the control system were determined using the analytic hierarchy process (AHP). The test results of each secondary index were then normalized to obtain the weights of the second-level indices. The first-level indicator Scores of each secondary indicator The value of i ranges from [1, 4]; the value of j ranges from [1, 4]. ], This represents the total number of secondary indicators. Calculate the score for each primary indicator. ;in, Let be the weight of the j-th secondary indicator within the i-th primary indicator; Calculate the overall performance score of the prevention and control system ;in Let be the weight of the i-th primary indicator; The performance of the prevention and control system is classified into different levels based on its overall performance score: When S ≥ 90 points, it is considered excellent; When 80 ≤ S < 90, it is considered good; When 60 ≤ S < 80 points, it is considered passing; If S < 60 points, it is considered unqualified.

[0010] Further, step S5 includes: Based on the quantitative evaluation results of the prevention and control system performance, a unified test database is established. This test database combines relational and non-relational databases and generates standardized test reports. Specifically: Retrieve basic information about the test tasks from the test database and populate it into the report cover and test overview section; retrieve test environment information and populate it into the test environment section; retrieve test equipment information and populate it into the test equipment section; retrieve test results and performance index data for each test item, generate test result tables and performance curves, and populate them into the test item and result section; generate a performance evaluation report based on the results of the systemic performance quantification evaluation model and populate it into the performance evaluation section; generate a PDF test report, save it to the specified path, and store the report information in the test report table. The content and format of the test report strictly follow relevant national and industry standards, and support user-defined test report templates, allowing users to adjust the content and format of the test report according to their needs.

[0011] On the other hand, the present invention also provides a test and evaluation system for a multi-mode reconfigurable unmanned aerial vehicle (UAV) defense system, the system comprising: The test control component includes a scenario management module, a data acquisition module, a positioning data feedback module, a drone prevention and control system, a baseline control module, and an NTP server. The scenario management module stores basic information about the test scenario and builds a unified spatiotemporal baseline system. The data acquisition module acquires the positioning feedback data of the target drone and interacts with the drone prevention and control system to perform adaptive networking and signal configuration of a multi-standard reconfigurable target drone cluster. The positioning data feedback module transmits the positioning data of the harassing drone back from the target drone. The drone prevention and control system is the drone prevention and control system used for testing. The baseline control module implements baseline control of the data through the NTP server, which is located in the baseline control module and is used for data interaction between the target drone, the harassing drone, the drone prevention and control system test system, and the drone prevention and control system. The test management component includes a system information management module, an external information management module, a raw data management module, and a report information management module. The system information management module manages the internal parameters of the UAV prevention and control system test system. The external information management module manages the test manufacturers, participating equipment models, and test item data of the UAV prevention and control system. The raw data management module associates the theoretical test values, scene parameters, and raw collected data of the UAV prevention and control system during the test process. The report information management module is used to generate standardized test reports for the UAV prevention and control system test system during testing. The analysis and evaluation components include a data processing module, a report generation module, and an evaluation result display module. The data processing module has functions for data extraction, evaluation calculation, and evaluation result storage, and establishes a navigation signal quality evaluation model to evaluate the test status of the UAV prevention and control system. The report generation module generates standardized test reports and iteratively optimizes parameters. The evaluation result display module displays the generated test results.

[0012] The beneficial effects of the technical solution provided by this invention include at least the following: 1. This invention constructs a unified spatiotemporal reference system based on UTC time and WGS84 coordinate system. Through NTP / PTP layered time synchronization, the coordinate transformation error is controlled within a certain range, fundamentally eliminating spatiotemporal system errors between different test equipment. This provides a high-precision spatiotemporal reference for testing and evaluation. At the same time, a multi-standard reconfigurable target drone hardware platform and signal configuration algorithm are designed, which can simulate multiple navigation signal standards such as BDS, GPS, and GLONASS, as well as UAV navigation systems of different accuracy levels. It supports the cooperative networking flight of up to 32 target drones, realistically reproducing the scenario of multi-UAV cooperative harassment in complex electromagnetic environments. Compared with traditional single-standard target drones, the test scenario of this invention is more realistic and comprehensive, which can effectively test the actual combat capability of the defense system in complex environments and solve the problems of single scenario and disconnect from reality in existing test methods.

[0013] 2. The multi-frequency, multi-system positioning backhaul algorithm and adaptive switching algorithm designed in this invention comprehensively utilize navigation signals from multiple systems and frequencies for weighted least squares positioning calculation, significantly improving positioning accuracy and reliability. The navigation signal quality assessment model based on the analytic hierarchy process (AHP) can accurately evaluate the quality of signals from various systems in real time, achieving seamless switching between different systems and avoiding positioning jumps and interruptions during the switching process. The positioning data can be integrated into the intelligent testing and evaluation system for UAV control systems. Furthermore, the multi-point sampling verification algorithm, through gridded sampling and optimized layout, combined with Grubbs outlier testing and cross-validation, effectively eliminates random errors, ensuring the consistency and reliability of test results. Compared with traditional single-point testing methods, the test results of this invention are more accurate and reliable, comprehensively reflecting the performance distribution of the control system throughout the entire protected area.

[0014] 3. This invention employs standardized and automated testing and evaluation technologies, achieving full-dimensional standardization of test scenarios, interfaces, and processes. Test reports are automatically generated in accordance with national and industry standards, facilitating horizontal comparisons between different prevention and control systems and user equipment selection. The fully automated testing process automatically completes the entire process from test initialization to report generation, significantly improving testing efficiency and reducing labor costs and human error. The parameter iteration optimization mechanism automatically adjusts and optimizes system parameters based on the results of each test, ensuring the test system is always in optimal working condition. Furthermore, the system of this invention adopts a modular design, possessing excellent scalability and scenario adaptability, enabling rapid configuration and adjustment to meet the testing needs of different application scenarios. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the overall execution flow of a test and evaluation method for a multi-mode reconfigurable unmanned aerial vehicle (UAV) control system provided in an embodiment of the present invention.

[0016] Figure 2 This is a schematic diagram of the software composition of the intelligent testing and evaluation system provided in an embodiment of the present invention.

[0017] Figure 3 This is a schematic diagram of the composition of the test and evaluation system for the drone prevention and control system provided in an embodiment of the present invention.

[0018] Figure 4 This is a schematic diagram illustrating the positioning and feedback principle provided in an embodiment of the present invention. Detailed Implementation

[0019] The present invention will be further described below with reference to the accompanying drawings, but this is not intended to limit the present invention in any way. Any modifications or substitutions made based on the teachings of the present invention shall fall within the protection scope of the present invention.

[0020] Example 1:

[0021] This embodiment provides a test and evaluation method for a multi-mode reconfigurable unmanned aerial vehicle (UAV) defense system. This method can be developed by... Figure 1 Implementation. Specifically, the method in this embodiment includes the following steps: S1: Utilizing the Coordinated Universal Time (UTC) system and WGS84 coordinate system transformation, a unified spatiotemporal reference system is constructed to complete the test environment initialization and error calibration; specifically: A high-precision atomic clock is deployed at the testing center as the main time source. The main time source obtains Coordinated Universal Time (UTC) by receiving UTC signals from a satellite time receiver. The network time protocol and precise time protocol of the test center are synchronized with the main time source; the network time protocol is obtained through an NTP server; the precise time protocol is obtained through a PTP master clock. The WGS84 coordinate system is adopted as a unified coordinate system, and all position data of the target drone are converted to the WGS84 coordinate system for processing; After completing the construction of the unified spatiotemporal benchmark, remove electromagnetic interference sources and obstacles from the test area; The target drone is placed on a calibration point with known coordinates. Through multiple calibrations, the static positioning error of the target drone navigation system is controlled within a preset error threshold range. Based on the testing requirements and the application scenario of the prevention and control system, determine the scope and shape of the testing area, and configure the flight parameters of the target drone, the sampling parameters of the test, the evaluation indicators and thresholds of the test.

[0022] Step S2: Based on a unified spatiotemporal reference system, perform adaptive networking and signal configuration for multi-standard reconfigurable target drone clusters, design multi-standard navigation signal reconfigurable configuration algorithms and target drone cluster adaptive networking algorithms, and realize target drone cluster status monitoring and control. A target drone cluster consists of multiple test drones, while a multi-standard reconfigurable target drone cluster consists of multiple test drones that are compatible with multiple communication signals on the same hardware platform. Based on a unified spatiotemporal reference system, a multi-standard reconfigurable target drone hardware platform is designed. This invention adopts a miniaturized and modular design concept to design a multi-standard reconfigurable target drone hardware platform. The body structure is made of carbon fiber composite material, the power system is driven by an electric propeller, and the target drone navigation system adopts a multi-standard reconfigurable design scheme. The positioning and feedback module integrates radio frequency front-ends and baseband processing chips for multiple frequencies such as BDS B1I, B2I, B3I, GPS L1, L2, and GLONASS G1, G2, which can simultaneously receive and process navigation signals of multiple standards. Design a reconfigurable configuration algorithm for multi-standard navigation signals to reconfigure the target drone navigation system, supporting dynamic configuration of the received signal standard and frequency. The target drone navigation system has a configuration parameter set C. M represents the navigation system standard, with a value of [value missing]. F represents the set of operating frequencies; N represents the number of receiving channels, ranging from 1 to 32; P represents the signal power; and T represents the positioning update cycle. The position error model for navigation calculation is defined as follows: , To locate the output position of the feedback module, This represents the actual location of the target drone. For systematic error, Random error; systematic error Constants, obtained through calibration; random errors It follows a normal distribution N(0,σ2), where σ is the standard deviation of the random error, which is determined according to the navigation system performance of different types of UAVs; Design an adaptive networking algorithm for the target drone cluster and initialize the network of the target drone cluster. The ground station sends a network broadcast packet to all target drones to be networked. After receiving the broadcast packet, the target drone sends a response packet to the ground station, containing its own ID, battery level, location, and speed information. Based on the response information of the target drones, the ground station establishes a node list for the target drone cluster and elects a master node. The master node is responsible for task allocation, time synchronization, and data fusion of the target drone cluster. Other nodes act as slave nodes, receiving instructions from the master node and executing corresponding tasks. The election of the master node uses a weighted election algorithm, with each target machine having a weight. Due to its power Communication quality and computing power Joint decision: α, β, and γ are weighting coefficients that satisfy α + β + γ = 1. This is the target drone's maximum power. For maximum communication quality, Maximum computing power; The target machine with the highest weight is selected as the master node. If the master node fails or has insufficient power, the ground station will re-elect a new master node to ensure the stable operation of the cluster. After the network is completed, the master node performs task allocation and route planning according to the requirements of the test task; Design a target drone cluster status monitoring and control system. Each target drone sends status data packets to the ground station at a fixed frequency. These status data packets include the target drone's ID, timestamp, position, speed, acceleration, attitude angle, battery level, signal strength, and mission progress information. Define a target drone health index. Used to assess the operational status of the target drone: , , , , Let be the weighting coefficient, satisfying , This represents the positioning error of the target drone. This is the positioning error threshold. For the speed error of the target drone, This is the speed error threshold; When the target drone's health index falls below a set threshold, the ground station will issue an early warning signal and take appropriate measures as needed.

[0023] It should be noted that this invention designs a multi-standard reconfigurable target drone hardware platform and signal configuration algorithm, which can simulate multiple navigation signal standards such as BDS, GPS, and GLONASS, as well as UAV navigation systems of different accuracy levels. It supports the coordinated networking flight of up to 32 target drones, realistically reproducing the scenario of multi-UAV coordinated harassment in a complex electromagnetic environment. Compared with traditional single-standard target drones, the test scenario of this invention is more realistic and comprehensive, which can effectively test the actual combat capability of the defense system in complex environments and solve the problems of existing test methods having a single scenario and being out of touch with reality.

[0024] Step S3: Design a multi-frequency, multi-system positioning and backhaul algorithm to obtain the target drone's positioning location; establish a navigation signal quality assessment model based on the target drone's positioning location, and design an adaptive switching algorithm to realize the backhaul of the target drone's positioning data; Design multi-frequency, multi-system positioning backhaul algorithms and adaptive handover algorithms, such as Figure 3 and Figure 4 As shown, in Figure 3 In this scheme, the accompanying drones serve as target drones, but unlike the target drones, the actual accompanying drones can form a cluster. Through the positioning and transmission modules on the accompanying drones, the detected drone signals are transmitted to the drone control system testing and evaluation system. This enables the drone control system testing and evaluation system to acquire drone data and coordinate with the drone control system, improving testing accuracy. Figure 4 In the test, the target drone is equipped with a positioning and feedback module that acquires control commands and transmits the positioning signal of the harassing drone to the drone defense system test and evaluation system via wireless communication. It supports point-to-point mode and one-to-many networking mode. Through miniaturization and modular design, it can be mounted on conventional drone platforms and features stable transmission, strong anti-interference, and full-duplex data transmission. It ensures that the target drone can maintain continuous and stable positioning and communication even when a single signal is interfered with or blocked, providing complete and accurate raw data for testing. Specifically, pseudorange and carrier phase observations are extracted from navigation signals of various systems. After obtaining the combined observations of various systems, all observations are combined to establish a unified observation equation. The weighted least squares method is used to solve for the optimal estimate of the parameter vector to be estimated, and the target drone's positioning position is obtained. Establish a navigation signal quality assessment model, the first A set of signal quality evaluation indicators for each standard , For the first The average carrier-to-noise ratio of all visible satellites of each standard. For the first Number of visible satellites in each system For the first Geometric precision factor of each standard. For the first The standard deviation of the positioning error for each standard For the first Signal lockout time for each standard; The signal quality evaluation index is normalized, and the normalized index value is... for: ; in, For the first The first standard The original values ​​of each indicator and The first The maximum and minimum values ​​of each indicator are defined as follows: positive indicators are those with larger values, indicating better signal quality; negative indicators are those with smaller values, indicating better signal quality. The rationality of the normalized indicator values ​​is verified through a consistency test, and the consistency ratio is [not specified]. for , As a consistency indicator, As the average random consistency index, when If the normalized index values ​​are considered to have satisfactory consistency, then the normalized index values ​​need to be adjusted. Calculate the first Comprehensive signal quality score for each standard ; in, The value range is [0,1]. The higher the score, the better the quality of the navigation signal of the system. Based on the navigation signal quality assessment model, an adaptive handover decision algorithm is designed, specifically: handover triggering conditions and handover recovery conditions are designed, assuming the currently used system is... Other candidate formats are The switch is triggered when the following conditions are met: (1) Overall signal quality score of the current standard ,in To switch the trigger threshold; (2) There is at least one candidate system Its overall signal quality score ,in To switch the recovery threshold, and ; (3) Candidate format Comprehensive signal quality score A higher overall signal quality score than the current standard And the difference is greater than , To switch the difference threshold; When all the above conditions are met, the system will switch from the current standard. Switch to the candidate standard with the highest overall signal quality score. .

[0025] Furthermore, to ensure reliable transmission of target drone positioning data, a highly reliable positioning data transmission mechanism based on multi-link redundant transmission is designed. Each target drone encapsulates its positioning data into data packets. These data packets undergo cyclic redundancy check (CRC) for error detection and forward error correction coding (FEC) for error control, improving the anti-interference capability of data transmission. The target drone simultaneously transmits the same data packets through both 2.4GHz and 5.8GHz links. The ground station receives the data packets from both links, performs data parsing and verification. The ground station numbers the received data packets and checks for any lost packets. If a data packet loss is detected, the ground station sends a retransmission request to the target drone. Upon receiving the retransmission request, the target drone retransmits the lost data packet. The maximum number of retransmissions is three. If the data packet is still not successfully received after three retransmissions, it is considered lost.

[0026] Step S4: Perform multi-point sampling verification and quantitative evaluation of the prevention and control system performance based on the returned positioning data; The test area is divided into grids. Sampling points are placed on the nodes of the grid, and the location of the sampling points is the coordinate of the grid node. By optimizing the layout method, the number of sampling points is reduced while ensuring test accuracy.

[0027] After completing the sampling point layout, each sampling point is tested at least three times, and the average value is taken as the final test result for that sampling point. The multi-point sampling verification algorithm verifies the accuracy and reliability of the test results by calculating the consistency and reliability of the test results across multiple sampling points. First, the statistical characteristics of the test results for all sampling points are calculated, including the average value. Standard deviation Maximum value Minimum value and range : , , , , ,in, For the first Test results for each sampling point This represents the number of sampling points; Outliers were removed using the Grubbs' test. The cumulative error and maximum deviation of the test results for the remaining sampling points were calculated to verify the consistency of the test results. : , This represents the number of sampling points after removing outliers. The nominal performance value of the control system; maximum deviation The maximum absolute error between the test results at all sampling points and the nominal performance value: ; If both the cumulative error and the maximum deviation are within the allowable range, the test results are considered consistent and reliable; otherwise, the cause of the error needs to be analyzed and the test needs to be repeated. After completing multi-point sampling verification, a quantitative evaluation model for the performance of the prevention and control system was established. The evaluation model divides the performance of the prevention and control system into four primary indicators: detection performance, tracking performance, interference performance and countermeasure performance. Each primary indicator is further divided into multiple secondary indicators. The performance evaluation index system for the prevention and control system is as follows: 1. Detection performance, including the following indicators: maximum detection range, minimum detection range, detection probability, false alarm rate, missed alarm rate, and reaction time; 2. Tracking performance, including the following indicators: tracking range, tracking accuracy, tracking duration, tracking success rate, and anti-maneuverability; 3. Jamming performance, including the following indicators: maximum jamming range, minimum jamming range, jamming success rate, jamming duration, and multi-target jamming capability; 4. Countermeasure performance, including the following indicators: maximum kill distance, minimum kill distance, kill probability, countermeasure reaction time, and multi-target countermeasure capability. The weights of each indicator are determined using the analytic hierarchy process (AHP). Let the weight of the first indicator be... The weight of each primary indicator is ( ),satisfy ;No. The first primary indicator The weights of the secondary indicators are: ,satisfy ,in For the first The number of secondary indicators under each primary indicator; The test results for each secondary indicator are normalized to obtain the indicator score. For positive indicators, the higher the value, the better. The normalization formula is: ; For negative indicators, the smaller the value, the better. The normalization formula is: ; in, For the first The first-level indicator The test results of each secondary indicator, and These are the maximum and minimum values ​​of the indicator, respectively. Calculate the score for each primary indicator. ; Calculate the overall performance score of the prevention and control system ; Based on the comprehensive performance score, the performance level of the prevention and control system is divided into four levels: excellent, good, qualified, and unqualified. When it is excellent, S≥90 points; when it is good, 80≤S<90 points; when it is qualified, 60≤S<80 points; and when it is unqualified, S<60 points.

[0028] Step S5: Based on the evaluation results, generate a standardized test report and iteratively optimize the parameters.

[0029] To achieve effective management and utilization of test data, a unified test database is established. The test database adopts a combination of relational and non-relational databases, incorporating both logically related and non-directly logically related data. The main data tables in the test database include: 1. Test Task Table: Stores basic information about test tasks, such as task ID, task name, test time, test location, test personnel, prevention and control system information, and test items. 2. Equipment Information Table: Stores basic information about test equipment, such as equipment ID, equipment name, equipment model, manufacturer, calibration time, and status. 3. Sampling Point Information Table: Stores basic information about sampling points, such as sampling point ID, sampling point number, coordinates, test items, and number of tests. 4. Raw Data Table: Stores raw data collected during the test, such as timestamps, target drone positions, radar detection data, photoelectric tracking data, interference data, and countermeasure data. 5. Performance Index Table: Stores calculated performance index data, such as detection range, detection probability, tracking accuracy, interference success rate, and knockdown probability. 6. Test Report Table: Stores generated test report information, such as report ID, task ID, report generation time, and report path.

[0030] It should be further noted that, for ease of representation, the data tables in this solution have been merged with the actual data tables and management methods used in the implementation. For example, in actual implementation, such as... Figure 2 As shown, the entire test database is divided into test control components, test management components, and analysis and evaluation components, and each component contains different modules. The analysis and evaluation component has an intelligent evaluation system for the prevention and control system. To ensure the security and integrity of the test data, a data backup and recovery mechanism is adopted. The test database is automatically backed up every day. The backup data is stored in an independent storage device and has a retention period of no less than 3 years. When the database fails, it can be quickly recovered through the backup data. At the same time, the test data is encrypted to prevent data leakage and tampering. The system designs an automated test report generation algorithm to automatically generate test reports that conform to national and industry standards based on data from the test database. Standardized test reports primarily reflect standardization in the test evaluation scenarios, software interfaces, and process testing. The automated test evaluation design includes automated test process management, automated test execution scheduling, automated operation monitoring, data acquisition, test data analysis, and report generation. The basic steps of the automatic test report generation algorithm are shown in the figure below: 1. Retrieve basic information about the test tasks from the test database and fill it into the cover and test overview sections of the report; 2. Retrieve test environment information, including test time, test location, weather conditions, electromagnetic environment, etc., and fill it into the test environment section; 3. Retrieve test equipment information, including the names, models, manufacturers, calibration times, etc. of each subsystem of the prevention and control system and the test equipment, and fill it into the test equipment section; 4. Retrieve test results and performance index data for each test item, generate test result tables and performance curves, and fill them into the test item and result sections; 5. Based on the results of the performance quantitative evaluation model of the prevention and control system, generate a performance evaluation report, including a comprehensive performance score and performance level, and fill it into the performance evaluation section; 6. Generate a PDF format test report, save it to the specified path, and store the report information in the test report table.

[0031] Furthermore, it should be noted that test reports can also be manually controlled. For example, an operator can start the test through a human-machine interface, initialize the test equipment and automatic calibrator according to the test parameters, generate simulation data, display the test process on the human-machine interface, continuously send test data to the user, and automatically evaluate the synchronously collected data until the test ends. Then, the next test item is started, and so on until all test items are completed. The test data is stored, the test result set is analyzed and evaluated, and then the report is printed.

[0032] The content and format of the test reports strictly adhere to relevant national and industry standards to ensure their standardization and authority. Furthermore, user-defined report templates are supported, allowing users to adjust the content and format of the reports according to their needs.

[0033] To continuously improve the performance and accuracy of the testing system, a parameter iterative optimization mechanism is designed. Parameter iterative optimization mainly includes the following aspects: 1. Optimization of spatiotemporal reference parameters: Based on the reference station data collected during the test, the time synchronization error of coordinate transformation is recalculated to optimize the accuracy of the spatiotemporal reference; 2. Navigation signal parameter optimization: Based on the target drone's positioning error data, adjust the error model parameters of the navigation signal reconfigurable configuration algorithm to improve the accuracy of navigation signal simulation; 3. Adaptive handover parameter optimization: Based on the positioning error and the number of handovers during the handover process, the threshold parameters of the adaptive handover algorithm are adjusted to improve the stability and smoothness of the handover. 4. Sampling point layout optimization: Adjust the number and layout of sampling points based on the error distribution of the test results; 5. Evaluation index weight optimization: Based on actual test results and user feedback, adjust the weights of each index in the performance quantification evaluation model to make the evaluation results more in line with actual needs.

[0034] The parameter iterative optimization employs a closed-loop control approach, automatically triggering the parameter optimization process after each test. First, the sources of error in the test results are analyzed to determine the parameters requiring optimization. Then, the particle swarm optimization algorithm is used to find the optimal values ​​for these parameters. Finally, the optimized parameters are updated in the control system for the next round of testing. Through continuous iterative optimization, the performance of the testing system is continuously improved.

[0035] To improve testing efficiency, reduce labor costs, and achieve full automation of the testing process, the testing system supports remote control. Testers can remotely access the system via the internet to configure test tasks, monitor the test process, view test data, and download test reports. Remote control employs encrypted communication and authentication technologies to ensure system security.

[0036] Example 2:

[0037] This embodiment provides a test and evaluation system for a multi-standard reconfigurable UAV control system. This multi-standard reconfigurable UAV control system test and evaluation system is as follows: Figure 2 As shown, it includes the following modules: The test control component includes a scenario management module, a data acquisition module, a positioning data feedback module, a drone prevention and control system, a baseline control module, and an NTP server. The scenario management module stores basic information about the test scenario and builds a unified spatiotemporal baseline system. The data acquisition module acquires the positioning feedback data of the target drone and interacts with the drone prevention and control system to perform adaptive networking and signal configuration of a multi-standard reconfigurable target drone cluster. The positioning data feedback module transmits the positioning data of the harassing drone back from the target drone. The drone prevention and control system is the drone prevention and control system used for testing. The baseline control module implements baseline control of the data through the NTP server. The NTP server is located in the baseline control module and is used for data interaction between the target drone, the harassing drone, the drone prevention and control system test system, and the drone prevention and control system. The test management component includes a system information management module, an external information management module, a raw data management module, and a report information management module. It is associated with theoretical test values, scenario parameters, raw collected data, test manufacturers, participating equipment models, and test item data items. The system information management module manages the parameters of the UAV prevention and control system test system itself. The external information management module manages the test manufacturers, participating equipment models, and test item data items of the UAV prevention and control system. The raw data management module associates the theoretical test values, scenario parameters, and raw collected data of the UAV prevention and control system during the test process. The report information management module generates standardized test reports during the test of the UAV prevention and control system test system. The analysis and evaluation components include a data processing module, a report generation module, and an evaluation result display module. The data processing module has functions for data extraction, evaluation calculation, and evaluation result storage, and establishes a navigation signal quality evaluation model to evaluate the test status of the UAV prevention and control system. The report generation module generates standardized test reports and iteratively optimizes parameters. The evaluation result display module displays the generated test results.

[0038] As used herein, the term "preferred" is meant as an example, illustration, or illustration. Any aspect or design described herein as "preferred" need not be construed as being more advantageous than other aspects or designs. Rather, the use of the term "preferred" is intended to present the concept in a specific manner. As used in this application, the term "or" is intended to mean an inclusive "or" rather than an exclusionary "or." That is, unless otherwise specified or clear from the context, "X uses A or B" naturally includes either of the permutations. That is, if X uses A; X uses B; or X uses both A and B, then "X uses A or B" is satisfied in any of the foregoing examples.

[0039] Furthermore, although this disclosure has been shown and described with respect to one or more implementations, equivalent variations and modifications will occur to those skilled in the art based on a reading and understanding of this specification and the accompanying drawings. This disclosure includes all such modifications and variations and is limited only by the scope of the appended claims. In particular, with respect to the various functions performed by the aforementioned components (e.g., elements, etc.), the terminology used to describe such components is intended to correspond to any component (unless otherwise indicated) that performs the specified function of said component (e.g., is functionally equivalent to it), even if structurally not equivalent to the disclosed structure performing the functions in the exemplary implementations of this disclosure shown herein. Moreover, although specific features of this disclosure have been disclosed with respect to only one of several implementations, such features may be combined with one or more features of other implementations that may be desirable and advantageous for a given or particular application. Furthermore, with regard to the use of the terms “comprising,” “having,” “containing,” or variations thereof in the Detailed Description or claims, such terms are intended to be included in a manner similar to the term “including.”

[0040] The functional units in this invention embodiment can be integrated into a processing module, or each unit can exist physically separately, or multiple units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. The aforementioned devices or systems can execute the storage methods in the corresponding method embodiments.

[0041] In summary, the above embodiments are one implementation of the present invention, but the implementation of the present invention is not limited to the embodiments described above. Any changes, modifications, substitutions, combinations, or simplifications made that deviate from the spirit and principle of the present invention should be considered equivalent substitutions and are included within the protection scope of the present invention.

Claims

1. A test and evaluation method for a multi-mode reconfigurable unmanned aerial vehicle (UAV) defense system, characterized in that: Includes the following steps: S1: Utilize the Coordinated Universal Time System (UTC) and WGS84 coordinate system to construct a unified spatiotemporal reference system and complete the test environment initialization and error calibration. S2: Based on a unified spatiotemporal reference system, adaptive networking and signal configuration of multi-standard reconfigurable target drone clusters are carried out. Multi-standard navigation signal reconfigurable configuration algorithm and target drone cluster adaptive networking algorithm are designed to realize target drone cluster status monitoring and control; the multi-standard reconfigurable target drone cluster consists of multiple test drones that are compatible with multiple communication signals for testing on the same hardware platform. S3: Design a multi-frequency, multi-system positioning and backhaul algorithm to obtain the target drone's positioning location; establish a navigation signal quality assessment model based on the target drone's positioning location, and design an adaptive switching algorithm to realize the backhaul of the target drone's positioning data; S4: Perform multi-point sampling verification and quantitative evaluation of the performance of the control system based on the returned target drone positioning data; S5: Based on the evaluation results, generate standardized test reports and iteratively optimize parameters.

2. The test and evaluation method for a multi-mode reconfigurable unmanned aerial vehicle (UAV) control system according to claim 1, characterized in that, Step S1 includes: A high-precision atomic clock is deployed at the testing center as the main time source. The main time source obtains Coordinated Universal Time (UTC) by receiving UTC signals from a satellite time receiver. The network time protocol and precise time protocol of the test center are synchronized with the main time source; the network time protocol is obtained through an NTP server; the precise time protocol is obtained through a PTP master clock. The WGS84 coordinate system is adopted as a unified coordinate system, and all position data of the target drone are converted to the WGS84 coordinate system for processing; After completing the construction of the unified spatiotemporal benchmark, remove electromagnetic interference sources and obstacles from the test area; The target drone is placed on a calibration point with known coordinates. Through multiple calibrations, the static positioning error of the target drone navigation system is controlled within a preset error threshold range. Based on the testing requirements and the application scenario of the prevention and control system, the scope and shape of the testing area are determined and the flight parameters of the target drone are configured.

3. The test and evaluation method for a multi-mode reconfigurable unmanned aerial vehicle (UAV) control system according to claim 2, characterized in that, Step S2 includes: Based on a unified spatiotemporal reference system, a multi-standard reconfigurable target drone hardware platform is designed. The target drone hardware platform includes a target drone navigation system. The positioning and feedback module of the target drone navigation system integrates radio frequency front-ends and baseband processing chips for multiple frequency points, including BDS B1I, BDS B2I, BDS B3I, GPS L1, GPS L2, GLONASS G1, and GLONASS G2, for simultaneously receiving and processing navigation signals of multiple standards. Design a reconfigurable algorithm for multi-standard navigation signals to reconfigure the target drone navigation system, supporting dynamic configuration of the received signal standard and frequency; specifically: Obtain the configuration parameter set of the target drone's navigation system, including the navigation system standard, operating frequency set, number of receiving channels, signal power, and positioning update cycle; the value of the navigation system standard is... , , , , , , BDS represents the signal of the BeiDou Navigation Satellite System, GPS represents the signal of the Global Positioning System, and GLONASS represents the signal of the GLONASS system. Calculate the position error of the navigation solution for: ； in, To locate the output position of the feedback module, This represents the actual location of the target drone. For navigation system error, denoted as a constant, obtained through calibration; The random error follows a normal distribution N(0,σ²), where σ is the standard deviation of the random error. Design an adaptive networking algorithm for the target machine cluster, specifically as follows: The target drone cluster consists of multiple test drones. The network of the target drone cluster is initialized by the ground station sending a network broadcast packet to all target drones to be networked. Upon receiving the broadcast packet, each target drone sends a response packet to the ground station. Based on the response information, the ground station establishes a node list for the target drone cluster and elects a master node. A weighted election algorithm is used to elect the master node, with each target drone having a specific weight. Due to its power Communication quality and computing power Joint decision: α, β, and γ are weighting coefficients that satisfy α + β + γ = 1. This is the target drone's maximum power. For maximum communication quality, To maximize computing power, the target machine with the highest weight is selected as the master node. If the master node fails or runs out of power, the ground station will re-elect a new master node to ensure the stable operation of the cluster. Design a target drone cluster status monitoring and control system. Each target drone sends status data packets to the ground station at a fixed frequency. These status data packets include the target drone's ID, timestamp, position, speed, acceleration, attitude angle, battery level, signal strength, and mission progress information. The target drone's health index is... Used to assess the operational status of the target drone: , , , , These are the weighting coefficients. , This represents the positioning error of the target drone. This is the positioning error threshold. For the speed error of the target drone, This is the speed error threshold; When the health index of the target drone falls below the set threshold, the ground station will issue a warning signal.

4. The test and evaluation method for a multi-mode reconfigurable unmanned aerial vehicle (UAV) control system according to claim 3, characterized in that, Step S3 includes: Design a multi-frequency, multi-system positioning backhaul algorithm and an adaptive handover algorithm, specifically as follows: Pseudorange and carrier phase observations are extracted from navigation signals of various systems. After obtaining the observations of various systems, all observations are combined to establish a unified observation equation. The weighted least squares method is used to solve for the optimal estimate of the parameter vector to be estimated, and the target drone's positioning position is obtained. Establish a navigation signal quality assessment model, the first A set of signal quality evaluation indicators for each standard , For the first The average carrier-to-noise ratio of all visible satellites of each standard. For the first Number of visible satellites in each system For the first Geometric precision factor of each standard. For the first The standard deviation of the positioning error for each standard For the first Signal lockout time for each standard; The signal quality evaluation index is normalized, and the normalized index value is... for: ; in, For the first The first standard The original values ​​of each indicator and The first The maximum and minimum values ​​of each indicator are defined as follows: positive indicators are those with larger values, indicating better signal quality; negative indicators are those with smaller values, indicating better signal quality. The rationality of the normalized indicator values ​​is verified through a consistency test, and the consistency ratio is [not specified]. for , As a consistency indicator, As the average random consistency index, when If the normalized index values ​​are considered to have satisfactory consistency, then the normalized index values ​​need to be adjusted. Calculate the first Comprehensive signal quality score for each standard ; in, The value range is [0,1]. The higher the score, the better the quality of the navigation signal of the system. Based on the navigation signal quality assessment model, an adaptive handover decision algorithm is designed, specifically: handover triggering conditions and handover recovery conditions are designed, assuming the currently used signal standard is... Other candidate formats are The switch is triggered when the following conditions are met: (1) Overall signal quality score of the current standard ,in To switch the trigger threshold; (2) There is at least one candidate system Its overall signal quality score ,in To switch the recovery threshold, and ; (3) Candidate format Comprehensive signal quality score A higher overall signal quality score than the current standard And the difference is greater than , To switch the difference threshold; When all the above conditions are met, the system will switch from the current standard. Switch to the candidate standard with the highest overall signal quality score. .

5. The test and evaluation method for a multi-mode reconfigurable unmanned aerial vehicle (UAV) control system according to claim 1, characterized in that, Step S4 includes: The returned data is subjected to multi-point sampling verification and quantitative evaluation of the prevention and control system performance, specifically as follows: First, the test area is divided into grids, and the sampling points are laid out. After completing the layout of sampling points, for each sampling point, the number of tests shall not be less than 3, and the average value shall be taken as the test result of the sampling point. The multi-point sampling verification algorithm verifies the accuracy and reliability of the test results by calculating the consistency and credibility of the test results of multiple sampling points. Calculate the statistical characteristics of the test results for all sampling points, including the mean. Standard deviation Maximum value Minimum value and range : , , , , , For the first Test results for each sampling point This represents the number of sampling points; Outliers were removed using the Grubbs test. The cumulative error and maximum deviation of the test results for the remaining sampling points were calculated. , , This represents the number of sampling points after removing outliers. The nominal performance value of the control system; maximum deviation The maximum absolute error between the test results at all sampling points and the nominal performance value: ; If both the cumulative error and the maximum deviation are within the allowable range, the test results are considered consistent and reliable; otherwise, the cause of the error needs to be analyzed and the test needs to be repeated. After completing multi-point sampling verification, a quantitative evaluation model for the performance of the prevention and control system was established. The evaluation model divides the performance of the prevention and control system into four primary indicators: detection performance, tracking performance, interference performance and countermeasure performance. Each primary indicator is further divided into multiple secondary indicators. The weights of each performance index of the control system were determined using the analytic hierarchy process (AHP). The test results of each secondary index were then normalized to obtain the weights of the second-level indices. The first primary indicator Scores of each secondary indicator The value of i ranges from [1, 4]; the value of j ranges from [1, 4]. ], This represents the total number of secondary indicators. Calculate the score for each primary indicator. ;in, Let be the weight of the j-th secondary indicator within the i-th primary indicator; Calculate the overall performance score of the prevention and control system ;in Let be the weight of the i-th primary indicator; The performance of the prevention and control system is classified into different levels based on its overall performance score: When S ≥ 90 points, it is considered excellent; When 80 ≤ S < 90, it is considered good; When 60 ≤ S < 80 points, it is considered passing; If S < 60 points, it is considered unqualified.

6. The test and evaluation method for a multi-mode reconfigurable unmanned aerial vehicle (UAV) control system according to claim 1, characterized in that, Step S5 includes: Based on the quantitative evaluation results of the prevention and control system performance, a unified test database is established. This test database combines relational and non-relational databases and generates standardized test reports. Specifically: Retrieve basic information about the test tasks from the test database and populate it into the report cover and test overview section; retrieve test environment information and populate it into the test environment section; retrieve test equipment information and populate it into the test equipment section; retrieve test results and performance index data for each test item, generate test result tables and performance curves, and populate them into the test item and result section; generate a performance evaluation report based on the results of the systemic performance quantification evaluation model and populate it into the performance evaluation section; generate a PDF test report, save it to the specified path, and store the report information in the test report table. The content and format of the test report strictly follow relevant national and industry standards, and support user-defined test report templates, allowing users to adjust the content and format of the test report according to their needs.

7. A test and evaluation system for a multi-mode reconfigurable unmanned aerial vehicle (UAV) control system, characterized in that, include: The test control component includes a scenario management module, a data acquisition module, a positioning data feedback module, a drone prevention and control system, a baseline control module, and an NTP server. The scenario management module stores basic information about the test scenario and builds a unified spatiotemporal baseline system. The data acquisition module acquires the positioning feedback data of the target drone and interacts with the drone prevention and control system to perform adaptive networking and signal configuration of a multi-standard reconfigurable target drone cluster. The positioning data feedback module transmits the positioning data of the harassing drone back from the target drone. The drone prevention and control system is the drone prevention and control system used for testing. The baseline control module implements baseline control of the data through the NTP server, which is located in the baseline control module and is used for data interaction between the target drone, the harassing drone, the drone prevention and control system test system, and the drone prevention and control system. The test management component includes a system information management module, an external information management module, a raw data management module, and a report information management module. The system information management module manages the parameters of the drone prevention and control system test system itself; the external information management module manages the test manufacturers, test equipment models, and test item data of the drone prevention and control system; the raw data management module associates the test theoretical values, scene parameters, and raw collected data of the drone prevention and control system during the test process; and the report information management module is used to generate standardized test reports when the drone prevention and control system test system is being tested. The analysis and evaluation components include a data processing module, a report generation module, and an evaluation result display module. The data processing module has functions for data extraction, evaluation calculation, and evaluation result storage, and establishes a navigation signal quality evaluation model to evaluate the test performance of the UAV control system. The report generation module generates standardized test reports and iteratively optimizes parameters. The evaluation result display module displays the generated test results. Implement a test and evaluation method for a multi-mode reconfigurable unmanned aerial vehicle (UAV) control system as described in any one of claims 1-6.

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