An unmanned aerial vehicle countermeasure method optimized by using a genetic algorithm

Abstract

The present application relates to the technical field of unmanned aerial vehicle countermeasure, in particular to an unmanned aerial vehicle countermeasure method optimized by genetic algorithm; the present application realizes accurate prediction of the flight trajectory of the unmanned aerial vehicle by fusing multi-source data through Kalman filtering algorithm, solves the interference problem caused by the multipath propagation effect by optimizing the antenna parameter combination using the genetic algorithm, and significantly improves the directivity and stability of the interference beam by coordinating the multipath path compensation through the particle swarm optimization algorithm; combined with the minimum injury distribution and the real-time environment feedback mechanism, the present method can dynamically adjust the countermeasure strategy, minimize the injury risk to the surrounding equipment, and at the same time ensure the continuous optimization of the interference effect.

Description

Technical Field

[0001] This invention relates to the field of drone countermeasures technology, and more specifically to a drone countermeasures method optimized using a genetic algorithm. Background Technology

[0002] With the rapid development of drone technology, its application in modern defense and public security is becoming increasingly widespread. However, drones may carry potential threats when performing reconnaissance, transport, or attack missions, posing challenges to airspace security and equipment protection. Especially in urban or complex environments, drone activity is frequent and highly maneuverable; any mishandling could amplify the risks. Therefore, developing effective drone countermeasure strategies has become a key requirement for ensuring social stability.

[0003] Currently, most drone countermeasures rely on fixed-parameter equipment settings or simple manual adjustments. While these methods can achieve basic interference, they often ignore the impact of dynamic environmental changes. For example, some traditional methods cover the target area by pre-setting signal transmission power, but when drones maneuver rapidly, this coverage method is prone to failure due to uneven signal attenuation, failing to accurately lock onto the target path, resulting in wasted energy or excessive radiation to unrelated areas. Furthermore, existing technologies have significant shortcomings in the coordination and optimization of pointing control and configuration parameters of countermeasure equipment. Pointing control involves adjusting the azimuth and elevation angles of the antenna in three-dimensional space to ensure the signal reaches the target directly; configuration parameters include polarization direction and gain distribution, which directly determine the propagation path and intensity distribution of the interference wave. Due to the complexity of three-dimensional space, antenna pointing deviation significantly amplifies the sensitivity of configuration parameters, making it difficult for a single adjustment to meet the compensation requirements for multipath propagation effects. Multipath propagation effects refer to the multiple paths a signal takes to reach the target after reflection and refraction in the environment. This not only increases the difficulty of predicting the target trajectory but may also lead to coherent superposition or destructive interference of the interference signals, causing the core interference beam to deviate from the expected path. For example, when the countermeasure system fails to optimize the antenna pointing and configuration parameters in sync, the interference signals may cancel each other out on the multipath path, causing the interference intensity at the target to decrease sharply, while it may unexpectedly increase at the peripheral equipment, creating a risk of accidental damage.

[0004] Therefore, achieving unified and coordinated optimization of parameters such as pointing angle, pitch angle, and polarization direction of the countermeasure antenna while considering UAV flight trajectory prediction and multipath propagation effects has become a key issue in improving countermeasure accuracy. Existing technologies have significant shortcomings in dynamic environment adaptability, multipath propagation compensation, and accidental damage control, necessitating a solution that can combine advanced algorithms for real-time optimization to improve the efficiency and reliability of UAV countermeasure systems. Summary of the Invention

[0005] The purpose of this invention is to address the aforementioned shortcomings in the prior art by providing a drone countermeasure method optimized using a genetic algorithm.

[0006] The objective of this invention is achieved through the following technical solution: a drone countermeasure method optimized using a genetic algorithm, comprising the following steps:

[0007] S1. Collect real-time position data and environmental reflection path information of the UAV through a sensor network, and use the Kalman filter algorithm to fuse multi-source inputs to obtain the predicted flight trajectory;

[0008] S2. Based on the predicted flight trajectory, calculate the initial adjustment values ​​of the antenna azimuth angle and elevation angle, and use a genetic algorithm to optimize the combination of antenna azimuth angle, elevation angle and polarization direction to determine the coordination parameter set;

[0009] S3. Based on the polarization direction distribution in the coordination parameter set, perform signal strength simulation for the multipath propagation path. If the simulated intensity is lower than a preset threshold on the target path, adjust the gain distribution to obtain an enhanced interference beam.

[0010] S4. Extract coherent superposition features from the enhanced interference beam, and use the particle swarm optimization algorithm to coordinate multipath path compensation to obtain a unified optimized pointing configuration.

[0011] S5. By simulating the signal propagation scenario through the unified and optimized pointing configuration, if the radiation in the surrounding area exceeds the preset threshold, the polarization direction is iteratively adjusted to determine the minimum accidental damage distribution.

[0012] S6. Verify the adaptability of the predicted flight trajectory based on the minimum error distribution, obtain real-time environmental feedback data, and if the deviation of the feedback data is greater than the preset threshold, update the input of the Kalman filter algorithm to obtain a refined trajectory prediction.

[0013] S7. Derive the dynamic adjustment sequence of antenna gain distribution from refined trajectory prediction, and use a genetic algorithm to integrate the sequence with multipath propagation compensation to determine the final coordinated optimization parameters.

[0014] S8. Deploy the final coordinated optimization parameters to the countermeasures device, obtain the signal monitoring data after deployment, and determine if the monitoring data shows that the interference intensity is attenuated at the target. Then, backtrack the particle swarm optimization algorithm to obtain the correction configuration set.

[0015] The present invention is further configured such that: the predicted flight trajectory includes UAV three-dimensional spatial path modeling, flight speed change trend and environmental reflection path analysis; the coordination parameter set includes antenna azimuth distribution, pitch distribution and polarization direction optimization strategy; the enhanced interference beam specifically includes multipath path signal superposition intensity assessment and target path signal enhancement; the unified optimized pointing configuration includes antenna pointing angle error correction and multipath path compensation results; the minimum false alarm distribution specifically includes surrounding area radiation intensity control and false alarm risk assessment; the refined trajectory prediction includes real-time environmental feedback correction and dynamic path prediction optimization; the final coordinated optimization parameters include antenna gain distribution dynamic adjustment sequence and multipath propagation compensation strategy; and the correction configuration set includes signal strength attenuation compensation and interference beam re-optimization.

[0016] The present invention is further configured such that the steps of collecting real-time position data and environmental reflection path information of the UAV through a sensor network, and fusing multi-source inputs using a Kalman filter algorithm to obtain the predicted flight trajectory are as follows:

[0017] Based on the real-time location data and environmental reflection path information of the UAV collected by the sensor network, time synchronization technology is used to align the time axis of the multi-source data, and spatial coordinate calibration is used to map the data into a unified three-dimensional spatial model to generate the initial data fusion result.

[0018] Based on the initial data fusion results, the Kalman filter algorithm is used to estimate the state of the UAV's position data. Combining historical trajectory information and current observation data, the flight path of the UAV in the future period is predicted, and a predicted flight trajectory is generated.

[0019] Based on the predicted flight trajectory, a three-dimensional spatial model of the UAV flight path is created using geometric modeling methods to generate a geometric representation of the UAV flight trajectory.

[0020] Based on the geometric representation of the UAV's flight trajectory and combined with environmental reflection path information, a path tracing method is used to analyze the signal propagation path and generate a comprehensive modeling result for predicting the flight trajectory.

[0021] The present invention is further configured such that, based on the predicted flight trajectory, the initial adjustment values ​​of the antenna azimuth and elevation angles are calculated, and a genetic algorithm is used to optimize the combination of the antenna azimuth, elevation angles, and polarization direction to determine the coordination parameter set. The specific steps are as follows:

[0022] Based on the predicted flight trajectory, the initial adjustment values ​​of the antenna azimuth and pitch angles are calculated using a geometric analytical method to generate the initial angle configuration;

[0023] Based on the initial angle configuration, a genetic algorithm is used to optimize the antenna azimuth angle, elevation angle and polarization direction. Through simulated selection, crossover and mutation operations, multiple sets of candidate parameter combinations are generated.

[0024] Based on the multiple sets of candidate parameter combinations, the interference effect of each parameter combination is evaluated using a signal propagation simulation method, the optimal parameter combination is selected, and a coordination parameter set is generated.

[0025] Based on the aforementioned set of coordination parameters, a parameter sensitivity analysis method is used to evaluate the degree of influence of each parameter on the interference effect, and a parameter optimization analysis report is generated.

[0026] The present invention is further configured such that, based on the polarization direction distribution in the coordination parameter set, signal strength is simulated for the multipath propagation path, and if the simulated intensity is lower than a preset threshold on the target path, the gain distribution is adjusted to obtain an enhanced interference beam. The specific steps are as follows:

[0027] Based on the polarization direction distribution in the coordination parameter set, the signal distribution on the multipath propagation path is analyzed using the signal strength simulation method, and a signal strength distribution map is generated.

[0028] Based on the signal strength distribution map, if the signal strength on the target path is lower than a preset threshold, the signal on the target path is enhanced by adjusting the antenna gain distribution to generate an enhanced interference beam.

[0029] Based on the enhanced interference beam, the coherent features of the signal are extracted using the coherent superposition analysis method to generate a coherent superposition feature map.

[0030] Based on the coherent superposition feature map, signal compensation technology is used to further optimize the target path signal and generate enhanced interference beam optimization results.

[0031] The present invention is further configured such that the steps of extracting coherent superposition features from the enhanced interference beam and using a particle swarm optimization algorithm to coordinate multipath path compensation to obtain a unified optimized pointing configuration are as follows:

[0032] Based on the coherent superposition characteristics of the enhanced interference beams, a particle swarm optimization algorithm is used to coordinate and optimize the multipath compensation, generating a multipath compensation scheme.

[0033] Based on the multipath compensation scheme, the antenna pointing adjustment method is used to fine-tune the antenna azimuth and elevation angles to generate an optimized antenna pointing configuration.

[0034] Based on the optimized antenna pointing configuration, the compensation effect is verified by a signal propagation scenario simulation method, and a unified optimized pointing configuration is generated.

[0035] Based on the unified and optimized pointing configuration, a performance evaluation method is used to comprehensively evaluate the interference effect and generate a pointing configuration optimization report.

[0036] The present invention is further configured such that, by simulating the signal propagation scenario with a uniformly optimized pointing configuration, if the radiation in the surrounding area exceeds a preset threshold, the polarization direction is iteratively adjusted to determine the minimum false alarm distribution. The specific steps are as follows:

[0037] Based on the unified and optimized pointing configuration, a signal coverage map is generated using a signal propagation scenario simulation method, and the radiation intensity distribution in the surrounding area is analyzed.

[0038] Based on the signal coverage map, if the radiation intensity in the surrounding area exceeds a preset threshold, the signal distribution is optimized by iteratively adjusting the polarization direction, and a polarization direction adjustment scheme is generated.

[0039] Based on the aforementioned polarization direction adjustment scheme, the accidental injury risk of surrounding equipment is analyzed using an accidental injury risk assessment method, and a minimum accidental injury distribution is generated.

[0040] Based on the minimum accidental injury distribution, a signal strength correction method is used to further optimize the signal in the accidental injury area, generating an optimized signal propagation scenario.

[0041] The present invention is further configured to verify the adaptability of the predicted flight trajectory based on the minimum false alarm distribution, obtain real-time environmental feedback data, and determine if the deviation of the feedback data is greater than a preset threshold, then update the input of the Kalman filter algorithm to obtain the refined trajectory prediction. The specific steps are as follows:

[0042] Based on the aforementioned minimum error distribution, the accuracy of the predicted flight trajectory is analyzed using a trajectory adaptability verification method, and a trajectory adaptability assessment report is generated.

[0043] Based on the trajectory adaptability assessment report, real-time environmental feedback data is obtained. If the deviation between the feedback data and the predicted data is greater than a preset threshold, the input of the Kalman filter algorithm is updated to generate an updated predicted flight trajectory.

[0044] Based on the updated predicted flight trajectory, the UAV flight path is further corrected using a dynamic path optimization method to generate a refined trajectory prediction.

[0045] Based on the refined trajectory prediction, the prediction results are verified using real-time data analysis methods, and a trajectory prediction optimization report is generated.

[0046] The present invention is further configured such that the steps of deriving the dynamic adjustment sequence of antenna gain distribution from refined trajectory prediction, integrating the sequence with multipath propagation compensation using a genetic algorithm, and determining the final coordinated optimization parameters are as follows:

[0047] Based on the refined trajectory prediction, a dynamic adjustment sequence for the antenna gain distribution is generated using a gain distribution derivation method.

[0048] Based on the dynamically adjusted sequence, a genetic algorithm is used to integrate and optimize the sequence and multipath propagation compensation to generate multiple sets of candidate coordination parameters.

[0049] Based on the multiple sets of candidate coordination parameters, the optimal parameter combination is selected using the interference effect evaluation method, and the final coordination optimization parameters are generated.

[0050] Based on the final coordinated optimization parameters, a system integration method is used to deploy the parameters to the countermeasures equipment, generating a parameter deployment scheme.

[0051] The present invention is further configured such that, by deploying the final coordinated optimization parameters to the countermeasure device, obtaining the signal monitoring data after deployment, and determining whether the monitoring data shows that the interference intensity attenuates at the target, the particle swarm optimization algorithm is backtracked to obtain the correction configuration set. The specific steps are as follows:

[0052] Based on the final coordination and optimization parameters, signal monitoring data after deployment is obtained using a signal monitoring method, and a signal monitoring report is generated.

[0053] Based on the signal monitoring report, if it is determined that the interference intensity decreases at the target location, the backtracking particle swarm optimization algorithm is used to re-optimize the parameters and generate a correction configuration set.

[0054] Based on the aforementioned correction configuration set, a signal compensation method is used to supplement and optimize the attenuated signal, thereby generating an optimized signal interference effect.

[0055] Based on the optimized signal interference effect, a system verification method is used to evaluate the overall countermeasure effect and generate a final countermeasure effect evaluation report.

[0056] The beneficial effects of this invention are as follows: This invention achieves accurate prediction of UAV flight trajectory by fusing multi-source data through Kalman filtering algorithm, solves the interference problem caused by multipath propagation effect by optimizing antenna parameter combination using genetic algorithm, and significantly improves the directivity and stability of interference beam by coordinating multipath path compensation through particle swarm optimization algorithm; combined with minimum false positive distribution and real-time environmental feedback mechanism, this method can dynamically adjust the countermeasure strategy to minimize the risk of false positive damage to surrounding equipment, while ensuring continuous optimization of interference effect. Attached Figure Description

[0057] The invention will be further illustrated with reference to the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the invention. For those skilled in the art, other drawings can be obtained based on the following drawings without any creative effort.

[0058] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0059] The present invention will be further described in conjunction with the following embodiments.

[0060] Depend on Figure 1 As can be seen, the UAV countermeasure method optimized by genetic algorithm described in this embodiment firstly involves collecting real-time UAV position data and environmental reflection path information through a sensor network in step S1, and then fusing multi-source inputs using a Kalman filter algorithm to generate a predicted flight trajectory. In practical applications, the sensor network consists of multiple distributed nodes, including devices such as radar, infrared sensors, and GPS receivers, used to capture the UAV's position information and the reflection signal characteristics of its surrounding environment. To ensure data time synchronization, time synchronization technology is used to align the time axis of the multi-source data, and spatial coordinate calibration is used to map all data into a unified three-dimensional spatial model, thereby generating an initial data fusion result. Subsequently, the Kalman filter algorithm is used to estimate the state of the UAV's position data.

[0061] The core formula of Kalman filtering is: , In this model, X(k|k-1) represents the predicted state value, F(k) is the state transition matrix, B(k) is the control matrix, U(k) is the control vector, P(k|k-1) is the prediction error covariance matrix, and Q(k) is the process noise covariance matrix. Then, by combining historical trajectory information with current observation data, Kalman filtering can effectively reduce data noise and improve prediction accuracy. Next, geometric modeling methods are used to create a three-dimensional spatial model of the UAV's flight path, generating a geometric representation for subsequent analysis. Finally, combined with environmental reflection path information, ray tracing is used to analyze the signal propagation path, forming a comprehensive modeling result for the predicted flight trajectory.

[0062] In step S2, initial adjustment values ​​for the antenna azimuth and elevation angles are calculated based on the predicted flight trajectory. A genetic algorithm is then used to optimize the combination of the antenna azimuth, elevation, and polarization direction, ultimately determining the coordination parameter set. First, the initial azimuth and elevation angles of the antenna are calculated using a geometric analytical method based on the UAV's predicted flight trajectory. The azimuth angle θ can be calculated using the formula θ = atan2(y,x), where x and y are the coordinates of the UAV on the horizontal plane, respectively. The elevation angle φ is calculated using the formula... The initial angle configuration, where z is the UAV's altitude, provides the foundation for subsequent optimization. Then, a genetic algorithm is used to optimize the antenna azimuth, elevation, and polarization directions. The basic process of the genetic algorithm includes selection, crossover, and mutation operations. Its fitness function is designed to maximize the interference effect while minimizing the risk of collateral damage. The fitness function can be expressed as F = αE - βR, where E is the interference effect score, R is the collateral damage risk score, and α and β are weighting coefficients. By simulating the selection operation to retain high-fitness individuals, the crossover operation to generate new individuals, and the mutation operation to introduce randomness, the genetic algorithm can quickly converge to the optimal solution. Next, signal propagation simulations are performed on multiple sets of candidate parameter combinations to evaluate the interference effect of each set of parameters and select the optimal combination, generating a coordinated parameter set. Finally, parameter sensitivity analysis is used to evaluate the influence of each parameter on the interference effect, generating a parameter optimization analysis report to guide subsequent steps.

[0063] In step S3, based on the polarization direction distribution in the coordination parameter set, signal strength simulation is performed for the multipath propagation path. When the signal strength at the target path is below a preset threshold, the gain distribution is adjusted to obtain an enhanced interference beam. First, the signal distribution along the multipath propagation path is analyzed using signal strength simulation methods to generate a signal strength distribution map; the signal strength I can be expressed by the formula... The calculation is performed, where P is the transmit power, G is the antenna gain, λ is the signal wavelength, and d is the propagation distance. Next, it is determined whether the signal strength along the target path is lower than a preset threshold T; if it is lower, the signal along the target path is enhanced by adjusting the antenna gain distribution to generate an enhanced interference beam; the gain adjustment formula is... Where G_new is the adjusted gain value and G_old is the original gain value. Then, coherent superposition analysis is used to extract the coherent features of the signal, generating a coherent superposition feature map; the coherent superposition features can be expressed by the formula... The signal strength of the i-th path is calculated, where I_i is the signal strength of the i-th path and φ_i is the phase offset. Finally, signal compensation techniques are used to further optimize the target path signal, generating an enhanced interference beam optimization result.

[0064] In step S4, coherent superposition features are extracted from the enhanced interference beam, and a particle swarm optimization algorithm is used to coordinate multipath path compensation, ultimately obtaining a unified and optimized pointing configuration. First, based on the coherent superposition features of the enhanced interference beam, a particle swarm optimization algorithm is used to coordinate and optimize multipath path compensation. The basic formula of the particle swarm optimization algorithm is:

[0065] Where v_i is the particle velocity, w is the inertial weight, c1 and c2 are learning factors, r1 and r2 are random numbers, pbest_i is the individual optimal position, and gbest is the global optimal position; by iteratively updating the particle position, the algorithm can quickly find the optimal compensation scheme. Next, based on the multipath compensation scheme, an antenna pointing adjustment method is used to fine-tune the antenna azimuth and elevation angles, generating an optimized antenna pointing configuration. Then, the compensation effect is verified through signal propagation scenario simulation, generating a unified optimized pointing configuration. Finally, a performance evaluation method is used to comprehensively evaluate the interference effect, generating a pointing configuration optimization report.

[0066] In step S5, a signal propagation scenario is simulated using a unified and optimized pointing configuration, and the polarization direction is iteratively adjusted to determine the minimum false alarm distribution when the radiation in the surrounding area exceeds a preset threshold. First, based on the unified and optimized pointing configuration, a signal coverage map is generated using a signal propagation scenario simulation method, and the radiation intensity distribution in the surrounding area is analyzed. Next, it is determined whether the radiation intensity in the surrounding area exceeds a preset threshold T_r; if it exceeds the threshold, the signal distribution is optimized by iteratively adjusting the polarization direction, generating a polarization direction adjustment scheme; the polarization direction adjustment formula is as follows: Where P_new is the adjusted polarization direction, P_old is the original polarization direction, and ΔP is the adjustment step size. Then, a risk assessment method for accidental damage to surrounding equipment is used to analyze the risk of accidental damage, generating a minimum accidental damage distribution. Finally, a signal strength correction method is used to further optimize the signal in the accidental damage area, generating an optimized signal propagation scenario.

[0067] In step S6, the adaptability of the predicted flight trajectory is verified based on the minimum false alarm distribution, and real-time environmental feedback data is acquired. If the deviation of the feedback data is greater than a preset threshold, the input of the Kalman filter algorithm is updated to obtain a refined trajectory prediction. First, based on the minimum false alarm distribution, the accuracy of the predicted flight trajectory is analyzed using a trajectory adaptability verification method, generating a trajectory adaptability evaluation report. Next, real-time environmental feedback data is acquired, and it is determined whether the deviation between the feedback data and the predicted data is greater than a preset threshold T_d. If it is greater than the threshold, the input of the Kalman filter algorithm is updated, generating an updated predicted flight trajectory. Then, a dynamic path optimization method is used to further correct the UAV flight path, generating a refined trajectory prediction. Finally, the prediction results are verified using real-time data analysis methods, generating a trajectory prediction optimization report.

[0068] In step S7, the dynamic adjustment sequence of antenna gain distribution is derived from the refined trajectory prediction, and a genetic algorithm is used to integrate the sequence with multipath propagation compensation to finally determine the final coordinated optimization parameters. First, based on the refined trajectory prediction, a dynamic adjustment sequence of antenna gain distribution is generated using a gain distribution derivation method. Then, a genetic algorithm is used to integrate and optimize the dynamic adjustment sequence with multipath propagation compensation, generating multiple sets of candidate coordinated parameters. Next, the optimal parameter combination is selected using an interference effect evaluation method to generate the final coordinated optimization parameters. Finally, a system integration method is used to deploy the final coordinated optimization parameters to the countermeasures equipment, generating a parameter deployment scheme.

[0069] In step S8, the final coordinated optimization parameters are deployed to the countermeasures device, and signal monitoring data after deployment is acquired. If the monitoring data shows that the interference intensity attenuates at the target, the particle swarm optimization algorithm is backtracked to obtain a correction configuration set. First, based on the final coordinated optimization parameters, signal monitoring data after deployment is acquired using a signal monitoring method, and a signal monitoring report is generated. Next, it is determined whether the interference intensity attenuates at the target; if attenuation occurs, the particle swarm optimization algorithm is backtracked to re-optimize the parameters, generating a correction configuration set. Then, the attenuated signal is supplemented and optimized using a signal compensation method to generate an optimized signal interference effect. Finally, a system verification method is used to evaluate the overall countermeasures effect, generating a final countermeasures effect evaluation report.

[0070] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit the scope of protection of the present invention. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the essence and scope of the technical solutions of the present invention.

Claims

1. A method for countering unmanned aerial vehicles (UAVs) optimized using a genetic algorithm, characterized in that: Includes the following steps: S1. Collect real-time position data and environmental reflection path information of the UAV through a sensor network, and use the Kalman filter algorithm to fuse multi-source inputs to obtain the predicted flight trajectory; S2. Based on the predicted flight trajectory, calculate the initial adjustment values ​​of the antenna azimuth angle and elevation angle, and use a genetic algorithm to optimize the combination of antenna azimuth angle, elevation angle and polarization direction to determine the coordination parameter set; S3. Based on the polarization direction distribution in the coordination parameter set, perform signal strength simulation for the multipath propagation path. If the simulated intensity is lower than a preset threshold on the target path, adjust the gain distribution to obtain an enhanced interference beam. S4. Extract coherent superposition features from the enhanced interference beam, and use the particle swarm optimization algorithm to coordinate multipath path compensation to obtain a unified optimized pointing configuration. S5. By simulating the signal propagation scenario through the unified and optimized pointing configuration, if the radiation in the surrounding area exceeds the preset threshold, the polarization direction is iteratively adjusted to determine the minimum accidental damage distribution. S6. Verify the adaptability of the predicted flight trajectory based on the minimum error distribution, obtain real-time environmental feedback data, and if the deviation of the feedback data is greater than the preset threshold, update the input of the Kalman filter algorithm to obtain a refined trajectory prediction. S7. Derive the dynamic adjustment sequence of antenna gain distribution from refined trajectory prediction, and use a genetic algorithm to integrate the sequence with multipath propagation compensation to determine the final coordinated optimization parameters. S8. Deploy the final coordinated optimization parameters to the countermeasures device, obtain the signal monitoring data after deployment, and determine if the monitoring data shows that the interference intensity is attenuated at the target. Then, backtrack the particle swarm optimization algorithm to obtain the correction configuration set.

2. The UAV countermeasure method optimized by genetic algorithm according to claim 1, characterized in that: The predicted flight trajectory includes UAV 3D spatial path modeling, flight speed change trend, and environmental reflection path analysis. The coordination parameter set includes antenna azimuth distribution, pitch distribution, and polarization direction optimization strategies. The enhanced interference beam specifically includes multipath path signal superposition intensity assessment and target path signal enhancement. The unified optimized pointing configuration includes antenna pointing angle error correction and multipath path compensation results. The minimum false alarm distribution specifically includes surrounding area radiation intensity control and false alarm risk assessment. The refined trajectory prediction includes real-time environmental feedback correction and dynamic path prediction optimization. The final coordinated optimization parameters include antenna gain distribution dynamic adjustment sequence and multipath propagation compensation strategy. The correction configuration set includes signal strength attenuation compensation and interference beam re-optimization.

3. The UAV countermeasure method optimized by genetic algorithm according to claim 1, characterized in that: The specific steps for obtaining a predicted flight trajectory by collecting real-time location data and environmental reflection path information of the UAV through a sensor network, and fusing multi-source inputs using a Kalman filter algorithm are as follows: Based on the real-time location data and environmental reflection path information of the UAV collected by the sensor network, time synchronization technology is used to align the time axis of the multi-source data, and spatial coordinate calibration is used to map the data into a unified three-dimensional spatial model to generate the initial data fusion result. Based on the initial data fusion results, the Kalman filter algorithm is used to estimate the state of the UAV's position data. Combining historical trajectory information and current observation data, the flight path of the UAV in the future period is predicted, and a predicted flight trajectory is generated. Based on the predicted flight trajectory, a three-dimensional spatial model of the UAV flight path is created using geometric modeling methods to generate a geometric representation of the UAV flight trajectory. Based on the geometric representation of the UAV's flight trajectory and combined with environmental reflection path information, a path tracing method is used to analyze the signal propagation path and generate a comprehensive modeling result for predicting the flight trajectory.

4. The UAV countermeasure method optimized by genetic algorithm according to claim 1, characterized in that: The steps for calculating the initial adjustment values ​​of the antenna azimuth and elevation angles based on the predicted flight trajectory, and then using a genetic algorithm to optimize the combination of antenna azimuth, elevation angles, and polarization direction to determine the coordination parameter set are as follows: Based on the predicted flight trajectory, the initial adjustment values ​​of the antenna azimuth and pitch angles are calculated using a geometric analytical method to generate the initial angle configuration; Based on the initial angle configuration, a genetic algorithm is used to optimize the antenna azimuth angle, elevation angle and polarization direction. Through simulated selection, crossover and mutation operations, multiple sets of candidate parameter combinations are generated. Based on the multiple sets of candidate parameter combinations, the interference effect of each parameter combination is evaluated using a signal propagation simulation method, the optimal parameter combination is selected, and a coordination parameter set is generated. Based on the aforementioned set of coordination parameters, a parameter sensitivity analysis method is used to evaluate the degree of influence of each parameter on the interference effect, and a parameter optimization analysis report is generated.

5. The UAV countermeasure method optimized by genetic algorithm according to claim 1, characterized in that: Based on the polarization direction distribution in the coordination parameter set, signal strength simulation is performed for the multipath propagation path. If the simulated intensity is lower than a preset threshold on the target path, the gain distribution is adjusted to obtain an enhanced interference beam. The specific steps are as follows: Based on the polarization direction distribution in the coordination parameter set, the signal distribution on the multipath propagation path is analyzed using the signal strength simulation method, and a signal strength distribution map is generated. Based on the signal strength distribution map, if the signal strength on the target path is lower than a preset threshold, the signal on the target path is enhanced by adjusting the antenna gain distribution to generate an enhanced interference beam. Based on the enhanced interference beam, the coherent features of the signal are extracted using the coherent superposition analysis method to generate a coherent superposition feature map. Based on the coherent superposition feature map, signal compensation technology is used to further optimize the target path signal and generate enhanced interference beam optimization results.

6. The UAV countermeasure method optimized by genetic algorithm according to claim 1, characterized in that: The specific steps for extracting coherent superposition features from the enhanced interference beam and using the particle swarm optimization algorithm to coordinate multipath path compensation to obtain a unified optimized pointing configuration are as follows: Based on the coherent superposition characteristics of the enhanced interference beams, a particle swarm optimization algorithm is used to coordinate and optimize the multipath compensation, generating a multipath compensation scheme. Based on the multipath compensation scheme, the antenna pointing adjustment method is used to fine-tune the antenna azimuth and elevation angles to generate an optimized antenna pointing configuration. Based on the optimized antenna pointing configuration, the compensation effect is verified by a signal propagation scenario simulation method, and a unified optimized pointing configuration is generated. Based on the unified and optimized pointing configuration, a performance evaluation method is used to comprehensively evaluate the interference effect and generate a pointing configuration optimization report.

7. The UAV countermeasure method optimized by genetic algorithm according to claim 1, characterized in that: By simulating the signal propagation scenario with a unified and optimized pointing configuration, and determining if the radiation in the surrounding area exceeds a preset threshold, the specific steps for iteratively adjusting the polarization direction and determining the minimum false alarm distribution are as follows: Based on the unified and optimized pointing configuration, a signal coverage map is generated using a signal propagation scenario simulation method, and the radiation intensity distribution in the surrounding area is analyzed. Based on the signal coverage map, if the radiation intensity in the surrounding area exceeds a preset threshold, the signal distribution is optimized by iteratively adjusting the polarization direction, and a polarization direction adjustment scheme is generated. Based on the aforementioned polarization direction adjustment scheme, the accidental injury risk of surrounding equipment is analyzed using an accidental injury risk assessment method, and a minimum accidental injury distribution is generated. Based on the minimum accidental injury distribution, a signal strength correction method is used to further optimize the signal in the accidental injury area, generating an optimized signal propagation scenario.

8. The UAV countermeasure method optimized by genetic algorithm according to claim 1, characterized in that: The adaptability of the predicted flight trajectory is verified based on the minimum false alarm distribution. Real-time environmental feedback data is obtained, and if the deviation of the feedback data is greater than a preset threshold, the input of the Kalman filter algorithm is updated to obtain the refined trajectory prediction. The specific steps are as follows: Based on the aforementioned minimum error distribution, the accuracy of the predicted flight trajectory is analyzed using a trajectory adaptability verification method, and a trajectory adaptability assessment report is generated. Based on the trajectory adaptability assessment report, real-time environmental feedback data is obtained. If the deviation between the feedback data and the predicted data is greater than a preset threshold, the input of the Kalman filter algorithm is updated to generate an updated predicted flight trajectory. Based on the updated predicted flight trajectory, the UAV flight path is further corrected using a dynamic path optimization method to generate a refined trajectory prediction. Based on the refined trajectory prediction, the prediction results are verified using real-time data analysis methods, and a trajectory prediction optimization report is generated.

9. A method for countering unmanned aerial vehicles (UAVs) optimized using a genetic algorithm according to claim 1, characterized in that: The specific steps for deriving the dynamic adjustment sequence of antenna gain distribution from refined trajectory prediction, integrating the sequence with multipath propagation compensation using a genetic algorithm, and determining the final coordinated optimization parameters are as follows: Based on the refined trajectory prediction, a dynamic adjustment sequence for the antenna gain distribution is generated using a gain distribution derivation method. Based on the dynamically adjusted sequence, a genetic algorithm is used to integrate and optimize the sequence and multipath propagation compensation to generate multiple sets of candidate coordination parameters. Based on the multiple sets of candidate coordination parameters, the optimal parameter combination is selected using the interference effect evaluation method, and the final coordination optimization parameters are generated. Based on the final coordinated optimization parameters, a system integration method is used to deploy the parameters to the countermeasures equipment, generating a parameter deployment scheme.

10. The UAV countermeasure method optimized by genetic algorithm according to claim 1, characterized in that: The steps for deploying the final coordinated and optimized parameters to the countermeasures device, obtaining the signal monitoring data after deployment, and determining whether the monitoring data shows that the interference intensity is attenuating at the target location, are as follows: The particle swarm optimization algorithm is then backtracked to obtain the corrected configuration set. Based on the final coordination and optimization parameters, signal monitoring data after deployment is obtained using a signal monitoring method, and a signal monitoring report is generated. Based on the signal monitoring report, if it is determined that the interference intensity decreases at the target location, the backtracking particle swarm optimization algorithm is used to re-optimize the parameters and generate a correction configuration set. Based on the aforementioned correction configuration set, a signal compensation method is used to supplement and optimize the attenuated signal, thereby generating an optimized signal interference effect. Based on the optimized signal interference effect, a system verification method is used to evaluate the overall countermeasure effect and generate a final countermeasure effect evaluation report.

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