A multi - means fusion unmanned aerial vehicle counter - measure method and system

Abstract

The present application provides a multi-means fusion unmanned aerial vehicle (UAV) countermeasure method and system. Specifically, in the present application, a distributed sensing array is deployed in the target area to obtain three-dimensional terrain data, atmospheric turbulence characteristics, and electromagnetic spectrum characteristics in real time, construct the boundary of a multi-layer virtual signal barrier, and generate a set of frequency band interference parameters. When it is detected that the UAV crosses the barrier boundary, the cooperative interference module is triggered to generate the initial interference signal weight, and dynamically correct the polarization direction distribution and power density distribution in combination with the electromagnetic scattering characteristics of the UAV in real time, and use them as the input for trajectory modeling to generate a three-dimensional dynamic trajectory model, so as to calculate the azimuth correction amount, elevation angle compensation coefficient, and energy gradient parameters of the ground interception device, and then generate a dynamic interception strategy through a dynamic strategy fusion device. Through multi-means fusion and dynamic closed-loop control, the present application improves the interference and interception efficiency of UAVs, and realizes a full-link closed loop from environmental perception to interception control.

Description

Technical Field

[0001] The embodiments of the present application relate to the technical field of unmanned aerial vehicles, and in particular, to an unmanned aerial vehicle countermeasure method and system integrating multiple means. Background Art

[0002] With the rapid development of unmanned aerial vehicle technology, its application fields have expanded from entertainment and photography to important fields such as logistics distribution, agricultural monitoring, disaster response, and military reconnaissance. However, this has also brought new security challenges, especially in protecting critical infrastructure (such as airports, nuclear power plants, government agencies, etc.) from unauthorized or maliciously controlled unmanned aerial vehicle intrusions;

[0003] Current countermeasures against unmanned aerial vehicles mainly include radar detection, radio interference, physical capture (such as using a net to capture unmanned aerial vehicles), laser destruction, etc. These methods usually operate independently and are responsible for different aspects of tasks. For example, radar is used for preliminary detection, while radio interference attempts to cut off the communication link between the unmanned aerial vehicle and its operator. In addition, there are also solutions based on visual recognition systems, which attempt to identify and track unmanned aerial vehicles through image analysis;

[0004] Although existing unmanned aerial vehicle countermeasures can, to a certain extent, cope with unmanned aerial vehicle threats, each method has its obvious limitations. Radar detection can provide basic position information, but has limited recognition ability for small unmanned aerial vehicles and is prone to false alarms; radio interference lacks pertinence and may cause unnecessary interference to surrounding legitimate radio signals; physical capture and laser destruction are not only costly and complex to deploy, but also have certain limitations in operation flexibility and response speed; visual recognition systems are limited by weather conditions and light conditions, resulting in low accuracy and reliability. Summary of the Invention

[0005] The embodiments of the present application provide an unmanned aerial vehicle countermeasure method and system integrating multiple means to solve the problems in the prior art, such as limited recognition ability for small unmanned aerial vehicles, easy generation of false alarms, lack of targeted interference measures, possible unnecessary interference to surrounding legitimate radio signals, high cost and complex deployment of physical capture and laser destruction methods with limited operation flexibility and response speed, and low accuracy and reliability of visual recognition systems under different weather conditions and light environments.

[0006] In a first aspect, the embodiments of the present application provide an unmanned aerial vehicle countermeasure method integrating multiple means, including:

[0007] Deploy a distributed sensing array in the target area and obtain three-dimensional terrain data, atmospheric turbulence characteristics, and electromagnetic spectrum characteristics in real time. Based on the three-dimensional terrain data and atmospheric turbulence characteristics, construct the boundary of a multi-layer virtual signal barrier, and generate a set of frequency band interference parameters by associating the abnormal frequency bands extracted from the electromagnetic spectrum characteristics;

[0008] When it is detected that the UAV crosses the boundary of the virtual signal barrier, trigger the collaborative interference module to generate an initial interference signal weight that matches the set of frequency band interference parameters;

[0009] Dynamically correct the polarization direction distribution and power density distribution of the initial interference signal weight in combination with the electromagnetic scattering characteristics feedback by the UAV in real time, obtain the corrected polarization direction distribution and power density distribution, and use the corrected polarization direction distribution and power density distribution as the input parameters for trajectory modeling;

[0010] Fuse the input parameters of trajectory modeling with the heading offset and speed vector changes of the UAV collected in real time to generate a three-dimensional dynamic trajectory model, and calculate the azimuth correction amount, elevation compensation coefficient, and energy gradient parameter of the ground interception device based on the three-dimensional dynamic trajectory model;

[0011] Input the azimuth correction amount, elevation compensation coefficient, and energy gradient parameter into a preset dynamic strategy fusion device to generate a dynamic interception strategy, where the dynamic interception strategy includes: an optimized emission timing sequence and a spatial density distribution matrix.

[0012] Optionally, input the azimuth correction amount, elevation compensation coefficient, and energy gradient parameter into a preset dynamic strategy fusion device to generate a dynamic interception strategy, where the dynamic interception strategy includes: an optimized emission timing sequence and a spatial density distribution matrix, including:

[0013] Input the azimuth correction amount and elevation compensation coefficient into a geometric parameter calculation unit, and generate an azimuth dynamic compensation amount and an elevation attenuation factor based on the obstacle shielding effect in the three-dimensional terrain data;

[0014] Input the energy gradient parameter, the azimuth dynamic compensation amount, and the elevation attenuation factor into a non-linear optimization model, and generate an optimized emission timing sequence through constraint condition calculation, where the constraint conditions include: the instantaneous acceleration of the UAV movement trajectory, the physical response delay of the ground interception device, and the dynamic allocation upper limit of the energy gradient parameter;

[0015] Construct a spatial density distribution matrix based on the azimuth dynamic compensation amount and the elevation attenuation factor;

[0016] Input the optimized emission timing sequence and the spatial density distribution matrix into a preset dynamic strategy fusion device, and generate a dynamic interception strategy through a multi-objective optimization algorithm.

[0017] Optionally, input the energy gradient parameter, the azimuth dynamic compensation amount, and the elevation attenuation factor into a non-linear optimization model, and generate an optimized emission timing sequence through constraint condition calculation, including:

[0018] Perform a dynamic coupling operation on the energy gradient parameter and the azimuth dynamic compensation amount to generate an energy-azimuth coupling coefficient, and perform a time-domain convolution on the elevation attenuation factor and the instantaneous acceleration of the UAV's motion trajectory to generate an elevation dynamic response parameter;

[0019] Input the energy-azimuth coupling coefficient and the elevation dynamic response parameter into a multi-constraint optimization framework. The multi-constraint optimization framework divides the time window through the three-dimensional curvature characteristics of the UAV's motion trajectory, and performs the following processing in each time window:

[0020] Generate a device response time baseline based on the physical response delay parameter of the ground interception device, and calculate an energy allocation window function in combination with the dynamic allocation upper limit of the energy gradient parameter;

[0021] Construct a dynamic weight allocation vector through the vector modulus value of the UAV's instantaneous acceleration and the device response time baseline. The dynamic weight allocation vector is used to adjust the time phase offset of the energy allocation window function;

[0022] Perform a non-linear superposition operation on the dynamic weight allocation vector and the elevation dynamic response parameter to generate an initial time sequence distribution diagram, and perform truncation compensation on the peak section exceeding the energy allocation upper limit in the initial time sequence distribution diagram through a constraint conflict resolution algorithm to generate an optimized transmission time sequence;

[0023] Optionally, dynamically correct the polarization direction distribution and power density distribution of the initial interference signal weight in combination with the electromagnetic scattering characteristics feedback by the UAV in real time to obtain the corrected polarization direction distribution and power density distribution, and use the corrected polarization direction distribution and power density distribution as input parameters for trajectory modeling, including:

[0024] Input the electromagnetic scattering characteristics feedback by the UAV in real time into a polarization characteristic calculation unit, extract the main polarization component and cross-polarization component of the scattered signal, and generate an initial solution of the polarization direction distribution;

[0025] Calculate the polarization matching degree based on the initial solution of the polarization direction distribution and the initial interference signal weight to generate a polarization mismatch factor, and input the polarization mismatch factor into a power density correction model;

[0026] In the power density correction model, generate a power density reference value through the intensity distribution of abnormal frequency bands in the electromagnetic spectrum characteristics, and generate a power density dynamic correction coefficient in combination with the polarization mismatch factor and the instantaneous acceleration of the UAV's motion trajectory;

[0027] Jointly optimize the power density dynamic correction coefficient and the initial solution of the polarization direction distribution to generate the corrected polarization direction distribution and power density distribution. Among them, the polarization direction distribution is dynamically compensated through the phase offset of the main polarization component, and the power density distribution is generated through the product operation of the power density reference value and the dynamic correction coefficient;

[0028] Use the corrected polarization direction distribution and power density distribution as the input parameters for trajectory modeling, and synchronize the polarization mismatch factor and the power density dynamic correction coefficient to the course offset calculation module of the three-dimensional dynamic trajectory model.

[0029] Optionally, deploy a distributed sensing array in the target area and obtain three-dimensional terrain data, atmospheric turbulence characteristics, and electromagnetic spectrum characteristics in real time. Based on the three-dimensional terrain data and atmospheric turbulence characteristics, construct the boundary of a multi-layer virtual signal barrier, and generate a set of frequency band interference parameters by associating the abnormal frequency bands extracted from the electromagnetic spectrum characteristics, including:

[0030] Deploy a multi-node distributed sensing array on the periphery of the target area, and use lidar scanning and multi-spectral imaging technology to collect three-dimensional terrain data in real time, and use an atmospheric turbulence sensor and a spectrum monitoring device to obtain atmospheric turbulence characteristics and electromagnetic spectrum characteristics respectively;

[0031] Input the three-dimensional terrain data into the terrain masking effect analysis module, extract the terrain elevation gradient and obstacle distribution characteristics, and generate a terrain masking weight matrix;

[0032] Generate atmospheric disturbance compensation parameters based on the turbulence intensity distribution and wind speed gradient in the atmospheric turbulence characteristics, and perform a spatial superposition operation on the atmospheric disturbance compensation parameters and the terrain masking weight matrix to generate an initial distribution map of the multi-layer virtual signal barrier boundary;

[0033] Input the electromagnetic spectrum characteristics into the abnormal frequency band detection unit, extract the abnormal energy peak and frequency band width characteristics in the electromagnetic spectrum characteristics, and generate a set of abnormal frequency band fingerprints;

[0034] Perform a frequency band spatial mapping on the set of abnormal frequency band fingerprints and the initial distribution map of the multi-layer virtual signal barrier boundary to generate a set of frequency band interference parameters. Among them, each frequency band interference parameter in the set of frequency band interference parameters includes: the center frequency, bandwidth, and power distribution ratio of the interference signal.

[0035] Optionally, fuse the input parameters of trajectory modeling with the course offset and speed vector changes of the unmanned aerial vehicle collected in real time to generate a three-dimensional dynamic trajectory model, and calculate the azimuth correction amount, elevation compensation coefficient, and energy gradient parameters of the ground interception device based on the three-dimensional dynamic trajectory model, including:

[0036] Input the corrected polarization direction distribution and power density distribution in the input parameters of trajectory modeling into the trajectory feature extraction unit, and extract the main polarization direction offset and power density change gradient of the unmanned aerial vehicle's motion state;

[0037] Perform a spatial vector superposition operation on the main polarization direction offset and the heading offset of the UAV collected in real time to generate a comprehensive heading offset vector, and perform a time-domain convolution operation on the power density change gradient and the velocity vector change to generate a velocity dynamic response parameter;

[0038] Input the comprehensive heading offset vector and the velocity dynamic response parameter into a three-dimensional flight path reconstruction module, and generate a three-dimensional dynamic flight path model based on the instantaneous curvature characteristics of the UAV motion trajectory. Among them, the three-dimensional dynamic flight path model includes: flight path curvature distribution, instantaneous acceleration distribution, and energy attenuation characteristics;

[0039] Input the flight path curvature distribution in the three-dimensional dynamic flight path model into an azimuth angle calculation unit, and generate an azimuth angle correction amount in combination with a terrain occlusion weight matrix. Among them, the azimuth angle correction amount includes: terrain occlusion compensation value and curvature dynamic offset;

[0040] Input the instantaneous acceleration distribution and the energy attenuation characteristics into an elevation angle compensation solver to generate an elevation angle compensation coefficient. Among them, the elevation angle compensation coefficient includes: acceleration dynamic response factor and energy attenuation compensation value;

[0041] Input the flight path curvature distribution, the instantaneous acceleration distribution, and the energy attenuation characteristics into an energy gradient calculation module to generate an energy gradient parameter. Among them, the energy gradient parameter includes: curvature energy distribution weight, acceleration energy compensation factor, and attenuation energy correction value.

[0042] Optionally, when it is detected that the UAV crosses the boundary of the virtual signal barrier, trigger the cooperative interference module to generate an initial interference signal weight that matches the set of frequency band interference parameters, including:

[0043] Obtain the layer index of the virtual signal barrier boundary crossed by the UAV and the associated set of frequency band interference parameters, and extract the center frequency and bandwidth parameters in the frequency band interference parameters corresponding to the virtual signal barrier boundary based on the layer index;

[0044] Input the center frequency and bandwidth parameters into a frequency band coverage matching calculation module to generate a frequency band coverage matching matrix. Among them, the frequency band coverage matching matrix contains the weight assignment priority and spectrum overlap coefficient of each frequency band interference signal;

[0045] Generate interference signal baseband parameters based on the spectrum overlap coefficient and the motion velocity vector of the UAV when crossing the barrier. Among them, the interference signal baseband parameters include: pulse repetition frequency, modulation depth, and phase jump interval;

[0046] Input the frequency band coverage matching matrix and the interference signal baseband parameters into a multi-target weight assignment algorithm to generate an initial interference signal weight. Among them, the initial interference signal weight contains the power ratio distribution and time-frequency resource allocation matrix of each frequency band interference signal.

[0047] In a second aspect, an embodiment of the present application provides an unmanned aerial vehicle (UAV) countermeasure system integrating multiple means, including:

[0048] A deployment module, configured to deploy a distributed sensing array in a target area and obtain three-dimensional terrain data, atmospheric turbulence characteristics, and electromagnetic spectrum characteristics in real time, construct a multi-layer virtual signal barrier boundary based on the three-dimensional terrain data and atmospheric turbulence characteristics, and generate a set of frequency band interference parameters by associating abnormal frequency bands extracted from the electromagnetic spectrum characteristics;

[0049] A trigger module, configured to trigger a cooperative interference module to generate an initial interference signal weight matching the set of frequency band interference parameters when detecting that the UAV crosses the virtual signal barrier boundary;

[0050] A correction module, configured to dynamically correct the polarization direction distribution and power density distribution of the initial interference signal weight in combination with the electromagnetic scattering characteristics feedback by the UAV in real time, obtain the corrected polarization direction distribution and power density distribution, and use the corrected polarization direction distribution and power density distribution as input parameters for trajectory modeling;

[0051] A calculation module, configured to fuse the input parameters for trajectory modeling with the heading offset and speed vector changes of the UAV collected in real time to generate a three-dimensional dynamic trajectory model, and calculate the azimuth correction amount, elevation compensation coefficient, and energy gradient parameter of the ground interception device based on the three-dimensional dynamic trajectory model;

[0052] A generation module, configured to input the azimuth correction amount, elevation compensation coefficient, and energy gradient parameter into a preset dynamic strategy fusion device to generate a dynamic interception strategy, where the dynamic interception strategy includes: an optimized emission timing sequence and a spatial density distribution matrix.

[0053] In a third aspect, an embodiment of the present application provides a computing device, including a processor and a memory, where a computer program is stored in the memory, and the processor is configured to run the computer program to execute the multi-means fusion UAV countermeasure method according to any one of the first aspects.

[0054] In a fourth aspect, an embodiment of the present application provides a computer storage medium, on which computer program instructions are stored, and when the computer program instructions are executed by a processor, the multi-means fusion UAV countermeasure method according to any one of the first aspects is implemented.

[0055] In the embodiments of the present application, a distributed sensing array is deployed in a target area to obtain three-dimensional terrain data, atmospheric turbulence characteristics, and electromagnetic spectrum characteristics in real time. A multi-layer virtual signal barrier boundary is constructed based on the three-dimensional terrain data and atmospheric turbulence characteristics, and an abnormal frequency band interference parameter set is generated by associating with the abnormal frequency band extracted from the electromagnetic spectrum characteristics. When it is detected that a drone crosses the virtual signal barrier boundary, a cooperative interference module is triggered to generate an initial interference signal weight matching the frequency band interference parameter set. The polarization direction distribution and power density distribution of the initial interference signal weight are dynamically corrected in combination with the electromagnetic scattering characteristics feedback by the drone in real time to obtain the corrected polarization direction distribution and power density distribution, and the corrected polarization direction distribution and power density distribution are used as input parameters for trajectory modeling. The input parameters for trajectory modeling are combined with the heading offset and speed vector changes of the drone collected in real time for fusion to generate a three-dimensional dynamic trajectory model, and the azimuth correction amount, elevation compensation coefficient, and energy gradient parameter of the ground interception device are calculated based on the three-dimensional dynamic trajectory model. The azimuth correction amount, elevation compensation coefficient, and energy gradient parameter are input into a preset dynamic strategy fusion device to generate a dynamic interception strategy, where the dynamic interception strategy includes: an optimized emission timing sequence and a spatial density distribution matrix.

[0056] The technical solution of the present application has the following beneficial effects:

[0057] The recognition accuracy and response speed of the drone target are improved, and the false alarm rate and environmental impact are effectively reduced, providing strong technical support for ensuring the safety of key areas. At the same time, the design of the system takes into account the comprehensive processing of various factors, including but not limited to terrain shielding effect, atmospheric disturbance compensation, abnormal frequency band detection, etc., thus realizing a more intelligent, accurate, and efficient drone countermeasure solution;

[0058] Furthermore, the azimuth correction amount and elevation compensation coefficient are input into the geometric parameter calculation unit, and the azimuth dynamic compensation amount and elevation attenuation factor are calculated in combination with the obstacle shielding effect in the three-dimensional terrain data. Then, using the non-linear optimization model, by considering constraints such as the instantaneous acceleration of the UAV's motion trajectory, the physical response delay of the ground interception device, and the dynamic allocation upper limit of the energy gradient parameter, an optimized launch timing sequence is generated. Then, a spatial density distribution matrix is constructed based on the azimuth dynamic compensation amount and elevation attenuation factor. Finally, the optimized launch timing sequence and the spatial density distribution matrix are input into the preset dynamic strategy fusion device, and an efficient dynamic interception strategy is generated through a multi-objective optimization algorithm, significantly improving the accuracy and response speed of the UAV countermeasure system. By comprehensively considering factors such as terrain features, atmospheric turbulence, and electromagnetic spectrum characteristics, precise positioning and efficient interference of UAV targets are achieved. Specifically, the introduction of the azimuth dynamic compensation amount and elevation attenuation factor effectively overcomes the influence of the terrain shielding effect and improves the interception accuracy; the application of the non-linear optimization model ensures that the optimized launch timing sequence can maximize the interception efficiency under complex constraints. In addition, the dynamic interception strategy generated based on the multi-objective optimization algorithm not only enhances the adaptability to different UAV threat types, but also optimizes resource allocation, reduces the false alarm rate and environmental impact, thus providing a more intelligent and effective solution for the security protection of key areas.

[0059] These aspects or other aspects of the present application will be more clearly understood in the following description of the embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the technical solutions in the embodiments of the present application or in the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0061] Figure 1 It is a flowchart of a multi-means fusion UAV countermeasure method provided by an embodiment of the present application;

[0062] Figure 2 It is a schematic structural diagram of a multi-means fusion UAV countermeasure system provided by an embodiment of the present application;

[0063] Figure 3 It is a schematic structural diagram of a computing device provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0064] To enable those skilled in the art to better understand the solution of this application, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of this application.

[0065] In some processes described in the specification, claims and the above-mentioned accompanying drawings of this application, there are multiple operations that appear in a specific order. However, it should be clearly understood that these operations may not be executed in the order in which they appear in this document or may be executed in parallel. The operation numbers such as 101 and 102 are only used to distinguish different operations, and the numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions such as "first" and "second" in this document are used to distinguish different messages, devices, modules, etc., do not represent the sequence, and do not limit that "first" and "second" are of different types.

[0066] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of this application.

[0067] Figure 1 The flowchart of a multi-means fusion UAV countermeasure method provided for the embodiments of this application is as Figure 1 shown, and the method includes:

[0068] Step 101, deploy a distributed sensing array in the target area and obtain three-dimensional terrain data, atmospheric turbulence characteristics and electromagnetic spectrum characteristics in real time, construct a multi-layer virtual signal barrier boundary based on the three-dimensional terrain data and atmospheric turbulence characteristics, and generate a set of frequency band interference parameters by associating the abnormal frequency bands extracted from the electromagnetic spectrum characteristics, including:

[0069] In this step, the distributed sensing array refers to a network composed of multiple sensor nodes arranged around the target area, which is used to monitor various parameters in the environment in real time, such as three-dimensional terrain data, atmospheric turbulence characteristics, and electromagnetic spectrum characteristics; three-dimensional terrain data refers to the digital information of the surface morphology of the target area obtained through technologies such as lidar scanning or satellite remote sensing, including elevation, slope, and obstacle distribution, etc.; atmospheric turbulence characteristics refer to the data set describing the irregular flow characteristics in the air, usually including indicators such as wind speed gradient and turbulence intensity, which affect the propagation of radio signals; electromagnetic spectrum characteristics refer to the data reflecting all electromagnetic wave activities in a specific area, including information such as frequency and intensity, which are used to identify abnormal signal sources; the multi-layer virtual signal barrier boundary refers to a multi-level electronic protection area constructed based on terrain and atmospheric conditions, aiming to detect and respond to unauthorized drone intrusions; the set of frequency band interference parameters: a parameter set extracted from the abnormal frequency bands in the electromagnetic spectrum, including center frequency, bandwidth, and power distribution ratio, etc., which is used to generate targeted interference signals;

[0070] First, deploy a distributed sensing array composed of lidar, atmospheric turbulence sensors, and spectrum monitoring devices around the target area to collect three-dimensional terrain data, atmospheric turbulence characteristics, and electromagnetic spectrum characteristics in real time. Then, input the three-dimensional terrain data into the terrain masking effect analysis module to generate a terrain masking weight matrix, and combine the turbulence intensity distribution and wind speed gradient in the atmospheric turbulence characteristics to generate atmospheric disturbance compensation parameters, thereby constructing an initial distribution map of the multi-layer virtual signal barrier boundary. At the same time, use the abnormal frequency band detection unit to extract the abnormal energy peak and frequency band width characteristics from the electromagnetic spectrum characteristics to form a set of abnormal frequency band fingerprints. Finally, associate the set of abnormal frequency band fingerprints with the initial distribution map of the virtual signal barrier boundary through frequency band space mapping technology to generate a set of frequency band interference parameters including the center frequency, bandwidth, and power distribution ratio of each frequency band interference signal;

[0071] For example, a distributed sensing array composed of multiple lidar scanners, atmospheric turbulence sensors, and spectrum monitoring stations is deployed around the airport. These devices collect three-dimensional terrain data, atmospheric turbulence characteristics, and electromagnetic spectrum characteristics around the airport in real time. Subsequently, the system establishes a multi-layer virtual signal barrier boundary based on the collected data, especially focusing on areas that may pose a threat to the aircraft takeoff and landing paths. When abnormal electromagnetic activities are detected in a certain frequency band, the system automatically extracts the characteristics of this frequency band, generates the corresponding set of frequency band interference parameters, and prepares targeted interference strategies. For example, once an unauthorized drone attempts to cross this barrier, the system immediately activates the collaborative interference module, generates a matching interference signal according to the preset set of frequency band interference parameters, effectively prevents the further movement of the drone, ensures the safe operation of the airport, not only improves the accuracy and efficiency of drone countermeasures, but also greatly reduces the false alarm rate and the impact on legal radio communications.

[0072] Step 102, when it is detected that the drone crosses the boundary of the virtual signal barrier, trigger the cooperative interference module to generate an initial interference signal weight matching the frequency band interference parameter set, including:

[0073] In this step, the virtual signal barrier boundary is a multi-level electronic protection area constructed based on three-dimensional terrain data and atmospheric turbulence characteristics, used to monitor and respond to unauthorized drone intrusions; the cooperative interference module refers to an integrated system component responsible for generating and executing interference strategies according to the detected drone characteristics and environmental parameters to prevent illegal activities of drones; the initial interference signal weight refers to the intensity distribution of the interference signal initially set according to the frequency band interference parameter set, used to guide the adjustment and optimization of subsequent interference signals;

[0074] When the distributed sensing array detects that a drone crosses the boundary of the pre-constructed virtual signal barrier, the system will automatically trigger the cooperative interference module to enter the working state. First, the system obtains the specific layer index through which the drone crosses and its associated frequency band interference parameter set, and extracts the corresponding center frequency and bandwidth parameters from it. Then, these parameters are input into the frequency band coverage matching calculation module to generate a frequency band coverage matching matrix including the priority of the weight distribution of each frequency band interference signal and the spectrum overlap coefficient. Based on this matrix, and combined with the velocity vector of the drone when crossing the barrier, the baseband parameters of the interference signal, such as pulse repetition frequency, modulation depth, and phase jump interval, are further calculated. Finally, through the multi-objective weight distribution algorithm, an initial interference signal weight matching the frequency band interference parameter set is generated, providing basic data support for subsequent dynamic correction;

[0075] For example, when the distributed sensing array deployed around the airport detects an unauthorized drone attempting to cross the boundary of the virtual signal barrier, the system immediately activates the cooperative interference module. The cooperative interference module first determines the specific position and direction through which the drone crosses, and then calculates the optimal interference frequency range for this drone based on the center frequency and bandwidth parameters in the preset frequency band interference parameter set. Next, using the frequency band coverage matching matrix and the velocity vector of the drone, the system formulates specific baseband parameters of the interference signal including pulse repetition frequency and modulation depth. Based on these parameters, the cooperative interference module generates a preliminary interference signal weight and starts sending customized interference signals to the drone, effectively interrupting its communication link and forcing it to change course or land, thus ensuring the safe operation of the airport.

[0076] Step 103, dynamically correct the polarization direction distribution and power density distribution of the initial interference signal weight in combination with the electromagnetic scattering characteristics real-time fed back by the drone, obtain the corrected polarization direction distribution and power density distribution, and use the corrected polarization direction distribution and power density distribution as the input parameters for trajectory modeling, including:

[0077] The electromagnetic scattering characteristics refer to the characteristics of electromagnetic waves reflected or emitted by the UAV, including signal strength, frequency, polarization direction, etc., which are used to analyze and identify targets; the initial interference signal weight refers to the preliminary interference signal strength distribution set based on the frequency band interference parameter set, providing a basis for subsequent adjustment; the polarization direction distribution refers to the description of the polarization state of the interference signal in different directions, affecting the directivity and pertinence of the interference effect; the power density distribution refers to the energy intensity of the interference signal received per unit area, determining the effective range and intensity of the interference; the input parameters of the trajectory modeling refer to the data set used to generate the three-dimensional dynamic trajectory model of the UAV, including information such as the corrected polarization direction distribution and power density distribution.

[0078] When the system detects that the UAV crosses the virtual signal barrier boundary and triggers the cooperative interference module, it starts to receive the electromagnetic scattering characteristics of the UAV in real time. First, these electromagnetic scattering characteristics are input into the polarization characteristic calculation unit, from which the main polarization component and cross-polarization component of the scattered signal are extracted to generate the initial solution of the polarization direction distribution. Then, based on this initial solution and the initial interference signal weight, the polarization matching degree is calculated to generate the polarization mismatch factor, which is input into the power density correction model. In this model, the power density reference value is generated according to the abnormal frequency band intensity distribution in the electromagnetic spectrum characteristics, and the power density dynamic correction coefficient is generated by combining the polarization mismatch factor and the instantaneous acceleration of the UAV movement trajectory. Finally, through the joint optimization process, the corrected polarization direction distribution and power density distribution are generated, and these corrected parameters are used as the input parameters for trajectory modeling to further optimize the UAV interception strategy.

[0079] For example, in the aforementioned application example of airport security protection, when the cooperative interference module is activated and sends customized interference signals to unauthorized UAVs, the system continuously receives the electromagnetic scattering characteristics of the UAV in real time. Through the polarization characteristic calculation unit, the system analyzes the main polarization and cross-polarization components of the electromagnetic waves returned by the UAV to determine whether the polarization direction distribution of the current interference signal needs to be adjusted. Based on the calculated polarization mismatch factor, the system finds that the current interference signal fails to effectively cover the communication frequency band of the UAV, so it makes a dynamic adjustment through the power density correction model. Specifically, the system re-evaluates the power density reference value according to the abnormal frequency band intensity distribution in the electromagnetic spectrum characteristics, and considering the changes in the UAV flight path (such as acceleration or turning), generates the power density dynamic correction coefficient. Finally, the system generates the corrected polarization direction distribution and power density distribution, and uses these data as the input parameters for trajectory modeling to further optimize the three-dimensional dynamic trajectory model of the UAV. This enables the ground interception equipment to track and respond to the movement of the UAV more accurately, ensuring the effective protection of the airport airspace.

[0080] Step 104: Integrate the input parameters of the trajectory modeling with the heading offset and the change in velocity vector of the UAV collected in real time to generate a three-dimensional dynamic trajectory model, and calculate the azimuth correction amount, elevation compensation coefficient, and energy gradient parameter of the ground interception device based on the three-dimensional dynamic trajectory model, including:

[0081] The heading offset refers to the actual deviation angle of the UAV relative to its predetermined path, which is used to evaluate the change in the flight trajectory; the change in velocity vector refers to the change in the magnitude and direction of the UAV's velocity at different time points, reflecting the dynamic characteristics of its motion state; the three-dimensional dynamic trajectory model is a model constructed based on the real-time data of the UAV, which is used to predict information such as the future position, velocity, and acceleration of the UAV; the azimuth correction amount refers to adjusting the horizontal rotation angle of the ground interception device according to the terrain shielding effect to ensure accurate aiming at the target; the elevation compensation coefficient refers to adjusting the vertical pointing angle of the ground interception device considering the influence of factors such as atmospheric turbulence on signal propagation; the energy gradient parameter refers to the gradient representing the change in the energy emitted by the ground interception device with distance, which affects the effect of interfering with or destroying the UAV.

[0082] First, take the corrected polarization direction distribution and power density distribution as the input parameters of the trajectory modeling, and integrate and process them with the heading offset and the change in velocity vector of the UAV collected in real time. Using these data, the system generates a three-dimensional dynamic trajectory model of the UAV through the trajectory feature extraction unit. This model not only includes basic information such as the current position, velocity, and acceleration of the UAV, but also covers its future motion trend. Then, based on the generated three-dimensional dynamic trajectory model, the system further calculates the azimuth correction amount, elevation compensation coefficient, and energy gradient parameter of the ground interception device. Specifically, the azimuth correction amount is adjusted by combining the terrain shielding weight matrix to adjust the horizontal rotation angle of the ground interception device, while the elevation compensation coefficient is adjusted based on the atmospheric disturbance compensation parameter to adjust the vertical pointing angle. Finally, the energy gradient parameter determines the optimal energy distribution scheme according to the instantaneous acceleration and energy attenuation characteristics of the UAV to achieve efficient interception.

[0083] For example, after successfully generating the corrected polarization direction distribution and power density distribution, the system begins to integrate these track modeling input parameters with the heading offset and velocity vector changes of the UAV collected in real time. As the UAV attempts to avoid interference signals and changes its flight path, the system quickly updates its three-dimensional dynamic track model and accurately predicts the UAV's next actions. Based on this model, the system calculates the azimuth correction amount and elevation compensation coefficient required for the ground interception device, ensuring that the interception device can accurately track and lock the fast-moving target. At the same time, considering the situation where the UAV accelerates and escapes, the system also adjusts the energy gradient parameter and optimizes the energy output strategy of the ground interception device, so that the interference signal can cover the target within the most effective range. In this way, even if the UAV tries to escape through complex flight maneuvers, the system can quickly respond and adjust the interception measures, greatly improving the success rate of countermeasures and effectively ensuring the safety of the airport airspace.

[0084] Step 105: Input the azimuth correction amount, elevation compensation coefficient, and energy gradient parameter into a preset dynamic strategy fusion device to generate a dynamic interception strategy, where the dynamic interception strategy includes: an optimized emission timing sequence and a spatial density distribution matrix, including:

[0085] The dynamic strategy fusion device is an integrated system component responsible for comprehensively analyzing the input various parameters to generate a dynamic interception strategy; the optimized emission timing sequence refers to the best emission order and timing arrangement for different time points to maximize the interception effect; the spatial density distribution matrix refers to the description of the energy density distribution required to be emitted at each position within a specific area, which is used to guide the effective allocation of resources;

[0086] First, input the calculated azimuth correction amount, elevation compensation coefficient, and energy gradient parameter into the preset dynamic strategy fusion device. The dynamic strategy fusion device first inputs the azimuth correction amount and elevation compensation coefficient into the geometric parameter calculation unit to generate an azimuth dynamic compensation amount and an elevation attenuation factor based on the obstacle shielding effect in the three-dimensional terrain data. Next, input the energy gradient parameter, azimuth dynamic compensation amount, and elevation attenuation factor into the non-linear optimization model, and generate an optimized emission timing sequence by considering constraints such as the instantaneous acceleration of the UAV's movement trajectory, the physical response delay of the ground interception device, and the dynamic allocation upper limit of the energy gradient parameter. In addition, construct a spatial density distribution matrix based on the azimuth dynamic compensation amount and elevation attenuation factor to determine the optimal energy allocation in each area. Finally, input the optimized emission timing sequence and the spatial density distribution matrix into the preset dynamic strategy fusion device, and generate a dynamic interception strategy including the optimized emission timing sequence and the spatial density distribution matrix through a multi-objective optimization algorithm to achieve efficient and accurate UAV countermeasures;

[0087] For example, continuing with the previous airport security protection case, after successfully calculating the azimuth correction amount, elevation angle compensation coefficient, and energy gradient parameters, these parameters are input into the dynamic policy fusion unit. The dynamic policy fusion unit first uses the geometric parameter calculation unit to adjust the azimuth dynamic compensation amount of the ground interception device according to the obstacle shielding effect in the three-dimensional terrain data, and adjusts the elevation angle attenuation factor based on the atmospheric disturbance compensation parameters. Subsequently, the non-linear optimization model combines the energy gradient parameters, azimuth dynamic compensation amount, and elevation angle attenuation factor, and generates an optimized launch timing sequence considering factors such as the instantaneous acceleration of the UAV's movement trajectory and the physical response delay of the ground interception device. At the same time, the system constructs a spatial density distribution matrix to clarify the optimal energy density distribution at each position in a specific area. Finally, the dynamic policy fusion unit generates a dynamic interception policy that includes the optimized launch timing sequence and the spatial density distribution matrix. This policy not only improves the response speed and accuracy of the ground interception device but also optimizes the energy distribution, enabling effective response to UAV threats even in complex environments and ensuring the safe operation of the airport.

[0088] To solve the problems of low positioning accuracy, slow response speed, and unreasonable energy distribution existing in traditional UAV countermeasure methods, in some embodiments, as described in step 105, the azimuth correction amount, elevation angle compensation coefficient, and energy gradient parameters are input into a preset dynamic policy fusion unit to generate a dynamic interception policy. This process includes:

[0089] First, the azimuth correction amount and elevation angle compensation coefficient are input into the geometric parameter calculation unit to generate an azimuth dynamic compensation amount and an elevation angle attenuation factor based on the obstacle shielding effect in the three-dimensional terrain data; then, the energy gradient parameters, azimuth dynamic compensation amount, and elevation angle attenuation factor are input into the non-linear optimization model, and an optimized launch timing sequence is generated by considering constraints such as the instantaneous acceleration of the UAV's movement trajectory, the physical response delay of the ground interception device, and the dynamic allocation upper limit of the energy gradient parameters; next, a spatial density distribution matrix is constructed based on the azimuth dynamic compensation amount and elevation angle attenuation factor; finally, the optimized launch timing sequence and the spatial density distribution matrix are input into the preset dynamic policy fusion unit, and a dynamic interception policy is generated through a multi-objective optimization algorithm;

[0090] In this embodiment, the azimuth dynamic compensation amount refers to the horizontal rotation angle of the ground interception device adjusted to overcome the terrain shielding effect to ensure accurate aiming at the target; the elevation angle attenuation factor is the vertical pointing angle adjusted based on the influence of factors such as atmospheric turbulence on signal propagation to improve the interception accuracy; the non-linear optimization model is used to handle complex constraints such as the instantaneous acceleration of the UAV and the response delay of the ground device to generate the optimal launch timing; the spatial density distribution matrix describes the optimal energy density distribution at each position in a specific area to guide the effective allocation of resources;

[0091] In the embodiment of the present application, first, the geometric parameter calculation unit analyzes the influence of terrain shielding effect and atmospheric disturbance on signal propagation, and generates the azimuth dynamic compensation amount and the elevation attenuation factor. Then, these parameters together with the energy gradient parameter are sent into the non-linear optimization model, and combined with the real-time motion data of the UAV, the optimal transmission timing optimization sequence is calculated. At the same time, the system constructs a spatial density distribution matrix based on the azimuth dynamic compensation amount and the elevation attenuation factor, and determines the optimal energy density at each position. Finally, all key parameters are integrated into the dynamic strategy fusion device, and the final dynamic interception strategy is generated through the multi-objective optimization algorithm, realizing efficient and accurate UAV countermeasure.

[0092] For example, in an actual application of airport security protection, when the distributed sensing array detects an unauthorized UAV crossing the virtual barrier, the system immediately activates the cooperative interference module and generates the initial interference signal weight. As the UAV continuously changes its flight path, the system continuously receives its electromagnetic scattering characteristics, and dynamically adjusts the polarization direction distribution and power density distribution of the interference signal. Based on the updated track modeling input parameters and the change of the UAV's velocity vector, the system generates an accurate three-dimensional dynamic track model, and calculates the azimuth correction amount, the elevation compensation coefficient and the energy gradient parameter. Subsequently, these parameters are input into the dynamic strategy fusion device, and through detailed geometric parameter calculation and non-linear optimization processing, a dynamic interception strategy including the transmission timing optimization sequence and the spatial density distribution matrix is generated. This strategy not only improves the response speed and accuracy of the ground interception equipment, but also optimizes the energy distribution, enabling effective response to UAV threats even in complex environments and ensuring the safe operation of the airport.

[0093] In order to further improve the accuracy and response speed of UAV countermeasure, as described in the previous embodiment, the energy gradient parameter, the azimuth dynamic compensation amount and the elevation attenuation factor are input into the non-linear optimization model, and the transmission timing optimization sequence is generated through constraint condition calculation. The process includes:

[0094] First, perform a dynamic coupling operation on the energy gradient parameter and the azimuth dynamic compensation amount to generate an energy-azimuth coupling coefficient, and perform a time-domain convolution on the elevation attenuation factor and the instantaneous acceleration of the UAV's motion trajectory to generate an elevation dynamic response parameter. Then, input the energy-azimuth coupling coefficient and the elevation dynamic response parameter into a multi-constraint optimization framework. Within each time window, generate a device response time baseline based on the physical response delay parameter of the ground interception device, and calculate an energy allocation window function in combination with the dynamic allocation upper limit of the energy gradient parameter. Construct a dynamic weight allocation vector through the vector modulus value of the UAV's instantaneous acceleration and the device response time baseline to adjust the time-phase offset of the energy allocation window function. Finally, perform a non-linear superposition operation on the dynamic weight allocation vector and the elevation dynamic response parameter to generate an initial timing distribution map, and perform truncation compensation on the peak section exceeding the energy allocation upper limit through a constraint conflict resolution algorithm to generate a final optimized transmission timing sequence;

[0095] In this embodiment, the energy-azimuth coupling coefficient is a comprehensive index obtained through the coupling operation of the energy gradient parameter and the azimuth dynamic compensation amount, and is used to describe the relationship between the energy distribution of the interference signal and the target direction. The elevation dynamic response parameter is generated by performing a time-domain convolution operation on the elevation attenuation factor and the time-varying instantaneous acceleration of the UAV, and reflects the dynamic changes in the vertical direction during the UAV's flight. The multi-constraint optimization framework is a mathematical model that can handle multiple complex constraint conditions (such as the three-dimensional curvature characteristics of the UAV's motion trajectory, the response delay of the ground device, etc.) and is used to generate the optimal transmission timing. The energy allocation window function represents the energy range that should be allocated to the ground interception device within a specific time to ensure the effective utilization of resources. The dynamic weight allocation vector is used to adjust the time-phase offset of the energy allocation window function to adapt to the real-time motion state of the UAV;

[0096] In the embodiment of the present application, first, perform a coupling operation on the energy gradient parameter and the azimuth dynamic compensation amount to generate an energy-azimuth coupling coefficient, and perform a time-domain convolution on the elevation attenuation factor and the instantaneous acceleration of the UAV to generate an elevation dynamic response parameter. Then, these parameters are fed into a multi-constraint optimization framework, which divides the time window according to the three-dimensional curvature characteristics of the UAV's motion trajectory and performs detailed optimization calculations within each window. Specifically, the system generates a device response time baseline based on the physical response delay parameter of the ground interception device, and calculates an energy allocation window function in combination with the dynamic allocation upper limit of the energy gradient parameter. Construct a dynamic weight allocation vector through the vector modulus value of the UAV's instantaneous acceleration and the device response time baseline to adjust the time-phase offset of the energy allocation window function. Subsequently, the system performs a non-linear superposition operation on the dynamic weight allocation vector and the elevation dynamic response parameter to generate an initial timing distribution map, and performs truncation compensation on the peak section exceeding the energy allocation upper limit through a constraint conflict resolution algorithm to finally generate an optimized transmission timing sequence;

[0097] For example, in the actual application of airport security protection, when an unauthorized drone is detected crossing the virtual barrier, the system activates the collaborative interference module and generates a preliminary interference signal weight. As the drone continuously changes its flight path, the system continuously receives its electromagnetic scattering characteristics and dynamically corrects the polarization direction distribution and power density distribution of the interference signal. Based on the updated input parameters of the trajectory modeling and the change in the velocity vector of the drone, the system generates an accurate three-dimensional dynamic trajectory model and calculates the azimuth correction amount, elevation compensation coefficient, and energy gradient parameter. Next, the system couples the energy gradient parameter with the azimuth dynamic compensation amount to generate an energy-azimuth coupling coefficient, and performs a time-domain convolution of the elevation attenuation factor with the instantaneous acceleration of the drone to generate an elevation dynamic response parameter. These parameters are fed into a multi-constraint optimization framework. The system divides the time window according to the three-dimensional curvature characteristics of the drone and calculates the device response time baseline and energy allocation window function in detail within each window. The time-phase offset of the energy allocation window function is adjusted through a dynamic weight allocation vector, and an initial timing distribution map is generated. Finally, the system truncates and compensates the peak section exceeding the energy allocation upper limit through a constraint conflict resolution algorithm to generate the final optimized transmission timing sequence. This strategy not only improves the response speed and accuracy of ground interception devices but also optimizes the energy allocation, enabling effective response to drone threats even in complex environments and ensuring the safe operation of the airport.

[0098] To further improve the interference effect and accuracy against drones, as another embodiment, according to what is described in step 103, the polarization direction distribution and power density distribution of the initial interference signal weight are dynamically corrected in combination with the electromagnetic scattering characteristics feedback by the drone in real time to obtain the corrected polarization direction distribution and power density distribution, and the corrected polarization direction distribution and power density distribution are used as the input parameters for trajectory modeling. The specific steps include:

[0099] First, the electromagnetic scattering characteristics feedback by the drone in real time are input into the polarization characteristic calculation unit, from which the main polarization component and cross-polarization component of the scattering signal are extracted, and an initial solution of the polarization direction distribution is generated; then, based on this initial solution and the initial interference signal weight, a polarization matching degree calculation is performed to generate a polarization mismatch factor, which is input into the power density correction model; in the power density correction model, the power density reference value is determined by analyzing the abnormal frequency band intensity distribution in the electromagnetic spectrum characteristics, and a power density dynamic correction coefficient is generated in combination with the polarization mismatch factor and the instantaneous acceleration of the drone's movement trajectory; next, the power density dynamic correction coefficient and the initial solution of the polarization direction distribution are jointly optimized to generate the final corrected polarization direction distribution and power density distribution; finally, these correction results are used as the input parameters for trajectory modeling and synchronized to the heading offset calculation module of the three-dimensional dynamic trajectory model;

[0100] In this embodiment, the polarization characteristic calculation unit is used to separate the main polarization component and the cross-polarization component from the received electromagnetic scattering signal, providing accurate polarization information; the polarization mismatch factor is a quantitative index for measuring the difference between the expected polarization direction and the actually received electromagnetic scattering signal, helping to evaluate the effectiveness of the current interference strategy; the power density correction model uses information such as the intensity distribution of abnormal frequency bands in the electromagnetic spectrum characteristics, the polarization mismatch factor, and the motion state of the UAV to adjust the power density of the interference signal to ensure the maximization of interference efficiency; the power density dynamic correction coefficient is the key parameter output by the model, indicating how to adjust the power level of the interference signal according to the current environmental conditions;

[0101] In the embodiment of the present application, first, the polarization characteristic calculation unit analyzes the electromagnetic scattering characteristics of the UAV's real-time feedback, extracts the main polarization component and the cross-polarization component of the scattering signal, and forms a preliminary estimate of the polarization direction distribution. Then, the polarization matching degree is calculated using this preliminary estimate and the weight of the initially set interference signal, so as to determine the polarization mismatch factor and send it to the power density correction model. Inside this model, the basic reference value of the power density is determined by checking the intensity distribution of abnormal frequency bands in the electromagnetic spectrum characteristics, and at the same time considering the influence of the polarization mismatch factor and the instantaneous acceleration of the UAV, the power density dynamic correction coefficient is calculated. Subsequently, a joint optimization method is adopted to combine the power density dynamic correction coefficient with the initial solution of the polarization direction distribution to generate the corrected polarization direction distribution and power density distribution. Specifically, the polarization direction distribution is adjusted according to the phase offset of the main polarization component, and the power density distribution is obtained by multiplying the power density reference value by the dynamic correction coefficient. Finally, these corrected parameters are applied to the track modeling process, and the relevant parameters in the three-dimensional dynamic track model are updated synchronously, such as the data of the heading offset calculation module.

[0102] In order to further improve the monitoring accuracy and response speed of the UAV threat, as another embodiment, as described in step 101, a distributed sensing array is deployed in the target area to obtain three-dimensional terrain data, atmospheric turbulence characteristics, and electromagnetic spectrum characteristics in real time. A multi-layer virtual signal barrier boundary is constructed based on the three-dimensional terrain data and atmospheric turbulence characteristics, and an abnormal frequency band interference parameter set is generated by associating the abnormal frequency bands extracted from the electromagnetic spectrum characteristics, specifically including:

[0103] Deploy a multi-node distributed sensing array around the target area. Real-time three-dimensional terrain data is collected through lidar scanning and multispectral imaging technology, and atmospheric turbulence characteristics and electromagnetic spectrum characteristics are obtained through an atmospheric turbulence sensor and a spectrum monitoring device respectively. The three-dimensional terrain data is input into the terrain masking effect analysis module to extract the terrain elevation gradient and obstacle distribution characteristics, and generate a terrain masking weight matrix. Based on the turbulence intensity distribution and wind speed gradient in the atmospheric turbulence characteristics, atmospheric disturbance compensation parameters are generated, and the atmospheric disturbance compensation parameters and the terrain masking weight matrix are subjected to a spatial superposition operation to generate an initial distribution map of the boundaries of multi-layer virtual signal barriers. The electromagnetic spectrum characteristics are input into the abnormal frequency band detection unit to extract the abnormal energy peak and frequency band width characteristics in the electromagnetic spectrum characteristics, and generate an abnormal frequency band fingerprint set. Finally, the abnormal frequency band fingerprint set and the initial distribution map of the boundaries of multi-layer virtual signal barriers are subjected to a frequency band space mapping to generate a frequency band interference parameter set, where each frequency band interference parameter includes the center frequency, bandwidth, and power distribution ratio of the interference signal;

[0104] In this embodiment, the multi-node distributed sensing array is a network composed of multiple sensor nodes for real-time monitoring of various parameters in the environment, such as three-dimensional terrain data, atmospheric turbulence characteristics, and electromagnetic spectrum characteristics. The terrain masking weight matrix is a data set describing the terrain elevation gradient and obstacle distribution characteristics, used to evaluate the impact of terrain on signal propagation. The atmospheric disturbance compensation parameters are a set of parameters generated based on the atmospheric turbulence characteristics, used to compensate for the impact of atmospheric disturbances on signal propagation. The abnormal frequency band fingerprint set is a set of abnormal energy peaks and frequency band width characteristics extracted from the electromagnetic spectrum characteristics, used to identify potential UAV communication frequency bands. The frequency band interference parameter set contains detailed information for each abnormal frequency band, such as the center frequency, bandwidth, and power distribution ratio, used to formulate targeted interference strategies;

[0105] In the embodiments of the present application, first, a distributed sensing array composed of lidar, atmospheric turbulence sensors, and spectrum monitoring stations is deployed around the target area. These devices collect the three-dimensional terrain data, atmospheric turbulence characteristics, and electromagnetic spectrum characteristics of the target area in real time. The system inputs the three-dimensional terrain data into the terrain masking effect analysis module, extracts the terrain elevation gradient and obstacle distribution characteristics from it, and generates a terrain masking weight matrix. At the same time, based on the turbulence intensity distribution and wind speed gradient in the atmospheric turbulence characteristics, an atmospheric disturbance compensation parameter is generated, and it is subjected to a spatial superposition operation with the terrain masking weight matrix to generate an initial distribution map of the boundaries of multi-layer virtual signal barriers. Next, the system inputs the electromagnetic spectrum characteristics into the abnormal frequency band detection unit, extracts the abnormal energy peak and frequency band width characteristics from it, and generates a set of abnormal frequency band fingerprints. Finally, through the frequency band space mapping technology, the system correlates the set of abnormal frequency band fingerprints with the initial distribution map of the boundaries of multi-layer virtual signal barriers to generate a set of frequency band interference parameters including the central frequency, bandwidth, and power distribution ratio of the interference signals in each frequency band.

[0106] To further improve the accurate prediction of the UAV's movement trajectory and the response efficiency of ground interception devices, as another embodiment, according to what is described in step 104, the input parameters of the trajectory modeling are combined with the heading offset and speed vector changes of the UAV collected in real time for fusion to generate a three-dimensional dynamic trajectory model, and the azimuth correction amount, elevation compensation coefficient, and energy gradient parameters of the ground interception device are calculated based on the three-dimensional dynamic trajectory model, specifically including:

[0107] The corrected polarization direction distribution and power density distribution in the input parameters of the trajectory modeling are input into the trajectory feature extraction unit to extract the main polarization direction offset and power density change gradient of the UAV's movement state; the main polarization direction offset and the heading offset of the UAV collected in real time are subjected to a spatial vector superposition operation to generate a comprehensive heading offset vector, and the power density change gradient and the speed vector change are subjected to a time-domain convolution operation to generate a speed dynamic response parameter; the comprehensive heading offset vector and the speed dynamic response parameter are input into the three-dimensional trajectory reconstruction module to generate a three-dimensional dynamic trajectory model based on the instantaneous curvature characteristics of the UAV's movement trajectory, and this model includes the trajectory curvature distribution, instantaneous acceleration distribution, and energy attenuation characteristics; the trajectory curvature distribution in the three-dimensional dynamic trajectory model is input into the azimuth calculation unit to generate an azimuth correction amount in combination with the terrain masking weight matrix, where the azimuth correction amount includes a terrain masking compensation value and a curvature dynamic offset; the instantaneous acceleration distribution and energy attenuation characteristics are input into the elevation compensation solver to generate an elevation compensation coefficient, where the elevation compensation coefficient includes an acceleration dynamic response factor and an energy attenuation compensation value; finally, the trajectory curvature distribution, instantaneous acceleration distribution, and energy attenuation characteristics are input into the energy gradient calculation module to generate an energy gradient parameter, and this parameter includes a curvature energy distribution weight, an acceleration energy compensation factor, and an attenuation energy correction value;

[0108] In this embodiment, the trajectory feature extraction unit is an algorithm or device for extracting the UAV motion state information from the corrected polarization direction distribution and power density distribution, capable of identifying the main polarization direction offset and the power density change gradient; the heading comprehensive offset vector is the result obtained by performing a spatial vector superposition on the actual heading offset of the UAV and the main polarization direction offset, used to describe the actual flight path of the UAV; the speed dynamic response parameter is the data generated by performing a time-domain convolution operation on the power density change gradient and the time-varying speed vector change, reflecting the speed change trend of the UAV; the three-dimensional trajectory reconstruction module is a mathematical model that generates a three-dimensional dynamic trajectory model using the heading comprehensive offset vector and the speed dynamic response parameter of the UAV; the azimuth angle calculation unit is used to calculate the optimal horizontal rotation angle of the ground interception device in combination with the terrain occlusion weight matrix; the elevation angle compensation solver is used to adjust the vertical pointing angle according to the instantaneous acceleration distribution and the energy attenuation characteristics; the energy gradient calculation module is responsible for determining the optimal energy distribution scheme according to the trajectory curvature distribution, the instantaneous acceleration distribution, and the energy attenuation characteristics.

[0109] In the embodiment of the present application, first, the corrected polarization direction distribution and power density distribution are input into the trajectory feature extraction unit to extract the main polarization direction offset and the power density change gradient of the UAV motion state from them. Then, a spatial vector superposition operation is performed on the main polarization direction offset and the UAV heading offset collected in real time to generate the heading comprehensive offset vector, and a time-domain convolution operation is performed on the power density change gradient and the speed vector change to generate the speed dynamic response parameter. Then, these data are input into the three-dimensional trajectory reconstruction module to generate a three-dimensional dynamic trajectory model based on the instantaneous curvature characteristics of the UAV motion trajectory, which details the trajectory curvature distribution, the instantaneous acceleration distribution, and the energy attenuation characteristics of the UAV. Next, the system inputs the trajectory curvature distribution in the three-dimensional dynamic trajectory model into the azimuth angle calculation unit to generate an azimuth angle correction amount in combination with the terrain occlusion weight matrix to ensure that the ground interception device can accurately aim at the target. At the same time, the instantaneous acceleration distribution and the energy attenuation characteristics are input into the elevation angle compensation solver to generate an elevation angle compensation coefficient to optimize the vertical pointing angle. Finally, the trajectory curvature distribution, the instantaneous acceleration distribution, and the energy attenuation characteristics are input into the energy gradient calculation module to generate an energy gradient parameter to provide the optimal energy distribution scheme for the ground interception device.

[0110] In order to further improve the response speed and interference accuracy for the UAV crossing the virtual signal barrier boundary, as another embodiment, according to what is described in step 102, when it is detected that the UAV crosses the virtual signal barrier boundary, the cooperative interference module is triggered to generate an initial interference signal weight matching the frequency band interference parameter set. Specifically, it includes:

[0111] First, obtain the hierarchical index of the virtual signal barrier boundary through which the UAV passes and the associated set of frequency band interference parameters, and extract the center frequency and bandwidth parameters in the frequency band interference parameters corresponding to the virtual signal barrier boundary based on the hierarchical index; then input the center frequency and bandwidth parameters into the frequency band matching degree calculation module to generate a frequency band coverage matching matrix including the weight assignment priority and spectrum overlap coefficient of each frequency band interference signal; then generate the baseband parameters of the interference signal based on the spectrum overlap coefficient and the motion speed vector of the UAV when passing through the barrier, where the baseband parameters of the interference signal include the pulse repetition frequency, modulation depth, and phase jump interval; finally, input the frequency band coverage matching matrix and the baseband parameters of the interference signal into the multi-objective weight assignment algorithm to generate the initial interference signal weights including the power ratio distribution and time-frequency resource allocation matrix of each frequency band interference signal.

[0112] In this embodiment, the hierarchical index refers to the identifier of the specific virtual signal barrier boundary through which the UAV passes, and is used to determine the corresponding set of frequency band interference parameters; the set of frequency band interference parameters contains detailed information for each abnormal frequency band, such as the center frequency, bandwidth, and power allocation ratio, and is used to formulate targeted interference strategies; the frequency band coverage matching matrix is a data structure that describes the weight assignment priority and spectrum overlap situation between different frequency band interference signals, and helps to optimize the selection of interference signals; the baseband parameters of the interference signal are a set of key parameters, including the pulse repetition frequency, modulation depth, and phase jump interval, which determine the basic characteristics of the interference signal; the multi-objective weight assignment algorithm is a mathematical model used to comprehensively consider multiple factors (such as the spectrum overlap coefficient, UAV speed, etc.) to generate the optimal interference signal weights.

[0113] In the embodiment of the present application, first, after the system detects that the UAV passes through the virtual signal barrier boundary, it obtains the hierarchical index of its passage and the corresponding set of frequency band interference parameters. Based on the hierarchical index, the system extracts the center frequency and bandwidth parameters in the relevant frequency band interference parameters. These parameters are then sent to the frequency band matching degree calculation module to generate a frequency band coverage matching matrix, which includes the weight assignment priority and spectrum overlap coefficient of each frequency band interference signal. Next, the system uses the spectrum overlap coefficient and the motion speed vector of the UAV when passing through the barrier to generate the baseband parameters of the interference signal, including the pulse repetition frequency, modulation depth, and phase jump interval. These baseband parameters reflect the basic characteristics and adaptability of the interference signal. Finally, the frequency band coverage matching matrix and the baseband parameters of the interference signal are input into the multi-objective weight assignment algorithm to generate the initial interference signal weights including the power ratio distribution and time-frequency resource allocation matrix of each frequency band interference signal. Ensure that the interference signal can accurately match the communication frequency band of the UAV and maximize the interference effect.

[0114] Figure 2 The structure diagram of a UAV countermeasure system integrating multiple means is provided for the embodiment of the present application, asFigure 2 As shown in the figure, the system includes:

[0115] A deployment module 21, configured to deploy a distributed sensing array in a target area and obtain three-dimensional terrain data, atmospheric turbulence characteristics, and electromagnetic spectrum characteristics in real time, construct a multi-layer virtual signal barrier boundary based on the three-dimensional terrain data and atmospheric turbulence characteristics, and generate a set of frequency band interference parameters by associating abnormal frequency bands extracted from the electromagnetic spectrum characteristics;

[0116] A trigger module 22, configured to trigger the cooperative interference module to generate an initial interference signal weight matching the set of frequency band interference parameters when it detects that a drone crosses the virtual signal barrier boundary;

[0117] A correction module 23, configured to dynamically correct the polarization direction distribution and power density distribution of the initial interference signal weight in combination with the electromagnetic scattering characteristics fed back by the drone in real time, obtain the corrected polarization direction distribution and power density distribution, and use the corrected polarization direction distribution and power density distribution as input parameters for trajectory modeling;

[0118] A calculation module 24, configured to fuse the input parameters for trajectory modeling with the heading offset and velocity vector changes of the drone collected in real time, generate a three-dimensional dynamic trajectory model, and calculate the azimuth correction amount, elevation compensation coefficient, and energy gradient parameter of the ground interception device based on the three-dimensional dynamic trajectory model;

[0119] A generation module 25, configured to input the azimuth correction amount, elevation compensation coefficient, and energy gradient parameter into a preset dynamic strategy fusion device to generate a dynamic interception strategy, where the dynamic interception strategy includes: an optimized emission timing sequence and a spatial density distribution matrix.

[0120] Figure 2 The described multi-means fusion drone countermeasure system can execute Figure 1 The multi-means fusion drone countermeasure method described in the embodiments shown, and its implementation principle and technical effects will not be elaborated. For the multi-means fusion drone countermeasure system in the above embodiments, the specific ways in which each module and unit perform operations have been described in detail in the embodiments related to the method, and will not be elaborated here.

[0121] In a possible design, Figure 2 The multi-means fusion drone countermeasure system in the embodiments shown can be implemented as a computing device, such as Figure 3 As shown in the figure, the computing device may include a storage component 31 and a processing component 32;

[0122] The storage component 31 stores one or more computer instructions, where the one or more computer instructions are called and executed by the processing component 32.

[0123] The processing component 32 is used to deploy a distributed sensing array in the target area and obtain three-dimensional terrain data, atmospheric turbulence characteristics, and electromagnetic spectrum characteristics in real time. Based on the three-dimensional terrain data and atmospheric turbulence characteristics, a multi-layer virtual signal barrier boundary is constructed, and an abnormal frequency band interference parameter set is generated by associating the abnormal frequency bands extracted from the electromagnetic spectrum characteristics. When it is detected that a drone crosses the virtual signal barrier boundary, the cooperative interference module is triggered to generate an initial interference signal weight that matches the frequency band interference parameter set. The polarization direction distribution and power density distribution of the initial interference signal weight are dynamically corrected in combination with the electromagnetic scattering characteristics feedback by the drone in real time to obtain the corrected polarization direction distribution and power density distribution, and the corrected polarization direction distribution and power density distribution are used as input parameters for trajectory modeling. The input parameters for trajectory modeling are combined with the heading offset and speed vector changes of the drone collected in real time for fusion to generate a three-dimensional dynamic trajectory model, and the azimuth correction amount, elevation compensation coefficient, and energy gradient parameter of the ground interception device are calculated based on the three-dimensional dynamic trajectory model. The azimuth correction amount, elevation compensation coefficient, and energy gradient parameter are input into a preset dynamic strategy fusion device to generate a dynamic interception strategy.

[0124] Among them, the processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components for executing the above method.

[0125] The storage component 31 is configured to store various types of data to support the operation of the terminal. The storage component may be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, a magnetic disk, or an optical disc.

[0126] Of course, the computing device may also necessarily include other components, such as input / output interfaces, display components, communication components, etc.

[0127] The input / output interface provides an interface between the processing component and the peripheral interface module, and the above peripheral interface module may be an output device, an input device, etc.

[0128] The communication component is configured to facilitate communication between the computing device and other devices in a wired or wireless manner, etc.

[0129] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform. In this case, the computing device can refer to a cloud server. The above-mentioned processing components, storage components, etc. can be basic server resources leased or purchased from a cloud computing platform.

[0130] The embodiment of the present application also provides a computer storage medium storing a computer program, which when executed by a computer can implement the above-mentioned Figure 1 multi-means fusion unmanned aerial vehicle countermeasure method shown in the embodiment.

[0131] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described systems, devices, and units can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0132] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative efforts.

[0133] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the essence of the above technical solution, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., including several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0134] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and are not intended to limit them. Although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments or perform equivalent replacements for some of the technical features. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of each embodiment of the present application.

Claims

1. A multi-means integrated drone countermeasure method, characterized in that: include: Deploy a distributed sensing array in the target area and obtain three-dimensional terrain data, atmospheric turbulence characteristics, and electromagnetic spectrum characteristics in real time. Construct a multi-layer virtual signal barrier boundary based on the three-dimensional terrain data and atmospheric turbulence characteristics, and associate the abnormal frequency bands extracted from the electromagnetic spectrum characteristics to generate a set of frequency band interference parameters. When a drone is detected crossing the boundary of the virtual signal barrier, the coordinated interference module is triggered to generate an initial interference signal weight matching the frequency band interference parameter set; Dynamically correct the polarization direction distribution and power density distribution of the initial interference signal weight in combination with the electromagnetic scattering characteristics fed back by the UAV in real time, obtain the corrected polarization direction distribution and power density distribution, and use the corrected polarization direction distribution and power density distribution as input parameters for track modeling; The input parameters of the track modeling are combined with the real-time collected UAV heading offset and velocity vector changes to generate a three-dimensional dynamic track model, and the azimuth correction, elevation compensation coefficient and energy gradient parameter of the ground interception equipment are calculated based on the three-dimensional dynamic track model; The azimuth correction value, elevation compensation coefficient and energy gradient parameter are input into a preset dynamic strategy fuser to generate a dynamic interception strategy, wherein the dynamic interception strategy includes: a transmission timing optimization sequence and a spatial density distribution matrix.

2. The method according to claim 1, characterized in that The azimuth correction, elevation compensation coefficient and energy gradient parameters are input into the preset dynamic strategy fusion to generate a dynamic interception strategy, including: The azimuth correction value and the elevation compensation coefficient are input into the geometric parameter solving unit, and the azimuth dynamic compensation value and the elevation attenuation factor are generated based on the obstacle shielding effect in the three-dimensional terrain data; The energy gradient parameter, the azimuth dynamic compensation amount and the elevation attenuation factor are input into the nonlinear optimization model, and the launch timing optimization sequence is generated by solving the constraint conditions, wherein the constraint conditions include: the instantaneous acceleration of the UAV motion trajectory, the physical response delay of the ground interception equipment and the upper limit of the dynamic allocation of the energy gradient parameter; Construct a spatial density distribution matrix based on the azimuth dynamic compensation and elevation attenuation factor; The launch timing optimization sequence and the spatial density distribution matrix are input into the preset dynamic strategy fuser, and a dynamic interception strategy is generated through a multi-objective optimization algorithm.

3. The method according to claim 2, characterized in that The energy gradient parameters, azimuth dynamic compensation and elevation attenuation factor are input into the nonlinear optimization model, and the emission timing optimization sequence is generated by solving the constraint conditions, including: The energy gradient parameter is dynamically coupled with the azimuth dynamic compensation to generate the energy azimuth coupling coefficient, and the elevation attenuation factor is convolved with the instantaneous acceleration of the UAV motion trajectory in the time domain to generate the elevation dynamic response parameter; The energy azimuth coupling coefficient and the elevation angle dynamic response parameters are input into a multi-constraint optimization framework, which divides the time window by the three-dimensional curvature characteristics of the UAV motion trajectory and performs the following processing in each time window: Generate a device response time baseline based on the physical response delay parameters of the ground interception equipment, and calculate the energy allocation window function in combination with the dynamic allocation upper limit of the energy gradient parameter; A dynamic weight allocation vector is constructed by using the vector modulus of the instantaneous acceleration of the drone and the device response time baseline, wherein the dynamic weight allocation vector is used to adjust the time phase offset of the energy allocation window function; The dynamic weight allocation vector and the elevation angle dynamic response parameter are nonlinearly superimposed to generate the initial timing distribution diagram, and the peak segment exceeding the upper limit of energy allocation in the initial timing distribution diagram is truncated and compensated by the constraint conflict resolution algorithm to generate the emission timing optimization sequence.

4. The method according to claim 1, characterized in that: The polarization direction distribution and power density distribution of the initial interference signal weight are dynamically corrected in combination with the electromagnetic scattering characteristics fed back by the UAV in real time, and the corrected polarization direction distribution and power density distribution are obtained. The corrected polarization direction distribution and power density distribution are used as input parameters for track modeling, including: The electromagnetic scattering characteristics fed back by the UAV in real time are input into the polarization characteristic solving unit to extract the main polarization component and cross-polarization component of the scattered signal and generate the initial solution of polarization direction distribution; Calculating polarization matching based on the initial solution of polarization direction distribution and the initial interference signal weight to generate a polarization mismatch factor, and inputting the polarization mismatch factor into a power density correction model; In the power density correction model, the power density reference value is generated by the abnormal frequency band intensity distribution in the electromagnetic spectrum characteristics, and the power density dynamic correction coefficient is generated by combining the polarization mismatch factor and the instantaneous acceleration of the UAV motion trajectory; The dynamic correction coefficient of power density and the initial solution of polarization direction distribution are jointly optimized to generate the corrected polarization direction distribution and power density distribution, wherein the polarization direction distribution is dynamically compensated by the phase offset of the main polarization component, and the power density distribution is generated by multiplying the power density reference value by the dynamic correction coefficient; The corrected polarization direction distribution and power density distribution are used as input parameters for track modeling, and the polarization mismatch factor and power density dynamic correction coefficient are synchronized to the heading offset solution module of the three-dimensional dynamic track model.

5. The method according to claim 1, characterized in that Deploy a distributed sensing array in the target area and obtain three-dimensional terrain data, atmospheric turbulence characteristics and electromagnetic spectrum characteristics in real time. Construct a multi-layer virtual signal barrier boundary based on the three-dimensional terrain data and atmospheric turbulence characteristics, and associate the abnormal frequency bands extracted from the electromagnetic spectrum characteristics to generate a set of frequency band interference parameters, including: Deploy a multi-node distributed sensing array outside the target area to collect three-dimensional terrain data in real time through lidar scanning and multi-spectral imaging technology, and obtain atmospheric turbulence characteristics and electromagnetic spectrum characteristics through atmospheric turbulence sensors and spectrum monitoring devices respectively; Input the three-dimensional terrain data into the terrain shielding effect analysis module, extract the terrain elevation gradient and obstacle distribution characteristics, and generate the terrain shielding weight matrix; Based on the turbulence intensity distribution and wind speed gradient in the atmospheric turbulence characteristics, the atmospheric disturbance compensation parameters are generated, and the atmospheric disturbance compensation parameters are spatially superimposed with the terrain shielding weight matrix to generate the initial distribution map of the multi-layer virtual signal barrier boundary; Input the electromagnetic spectrum features into the abnormal frequency band detection unit, extract the abnormal energy peak and frequency band width features in the electromagnetic spectrum features, and generate an abnormal frequency band fingerprint set; The abnormal frequency band fingerprint set and the initial distribution map of the multi-layer virtual signal barrier boundary are mapped in the frequency band space to generate a frequency band interference parameter set, wherein each frequency band interference parameter in the frequency band interference parameter set includes: the center frequency, bandwidth and power allocation ratio of the interference signal.

6. The method according to claim 1, characterized in that The input parameters of the track modeling are combined with the real-time collected heading offset and velocity vector changes of the UAV to generate a three-dimensional dynamic track model. Based on the three-dimensional dynamic track model, the azimuth correction, elevation compensation coefficient and energy gradient parameters of the ground interception equipment are calculated, including: Input the corrected polarization direction distribution and power density distribution in the track modeling input parameters into the track feature extraction unit to extract the main polarization direction offset and power density change gradient of the UAV motion state; The main polarization direction offset is combined with the real-time collected UAV heading offset to generate a heading integrated offset vector, and the power density change gradient is combined with the velocity vector change to generate a velocity dynamic response parameter. Inputting the heading integrated offset vector and the velocity dynamic response parameters into the three-dimensional track reconstruction module, generating a three-dimensional dynamic track model based on the instantaneous curvature characteristics of the UAV motion track, wherein the three-dimensional dynamic track model includes: track curvature distribution, instantaneous acceleration distribution and energy attenuation characteristics; Inputting the track curvature distribution in the three-dimensional dynamic track model into the azimuth angle solving unit, and generating the azimuth angle correction value in combination with the terrain shielding weight matrix, wherein the azimuth angle correction value includes: terrain shielding compensation value and curvature dynamic offset; Inputting the instantaneous acceleration distribution and the energy attenuation characteristics into the elevation angle compensation solver to generate an elevation angle compensation coefficient, wherein the elevation angle compensation coefficient includes: an acceleration dynamic response factor and an energy attenuation compensation value; The track curvature distribution, instantaneous acceleration distribution and energy attenuation characteristics are input into the energy gradient solution module to generate energy gradient parameters, wherein the energy gradient parameters include: curvature energy distribution weight, acceleration energy compensation factor and attenuation energy correction value.

7. The method according to claim 1, characterized in that When a drone is detected crossing the boundary of the virtual signal barrier, the coordinated interference module is triggered to generate an initial interference signal weight matching the frequency band interference parameter set, including: Obtain the hierarchical index of the virtual signal barrier boundary traversed by the UAV and the associated frequency band interference parameter set, and extract the center frequency and bandwidth parameters from the frequency band interference parameters corresponding to the virtual signal barrier boundary based on the hierarchical index; Input the center frequency and bandwidth parameters into the frequency band matching calculation module to generate a frequency band coverage matching matrix, wherein the frequency band coverage matching matrix includes the weight allocation priority and spectrum overlap coefficient of the interference signal of each frequency band; Generate interference signal baseband parameters based on the spectrum overlap coefficient and the motion speed vector of the UAV when it passes through the barrier, wherein the interference signal baseband parameters include: pulse repetition frequency, modulation depth and phase jump interval; The frequency band coverage matching matrix and the interference signal baseband parameters are input into a multi-objective weight allocation algorithm to generate an initial interference signal weight, wherein the initial interference signal weight includes the power ratio distribution of the interference signal in each frequency band and the time-frequency resource allocation matrix.

8. A multi-means integrated drone countermeasure system, characterized in that: include: A deployment module is used to deploy a distributed sensing array in the target area and obtain three-dimensional terrain data, atmospheric turbulence characteristics, and electromagnetic spectrum characteristics in real time. A multi-layer virtual signal barrier boundary is constructed based on the three-dimensional terrain data and atmospheric turbulence characteristics, and a frequency band interference parameter set is generated by associating the abnormal frequency bands extracted from the electromagnetic spectrum characteristics. A trigger module, configured to trigger the coordinated interference module to generate an initial interference signal weight matching the frequency band interference parameter set when a drone is detected crossing the boundary of the virtual signal barrier; A correction module is used to dynamically correct the polarization direction distribution and power density distribution of the initial interference signal weight in combination with the electromagnetic scattering characteristics fed back by the UAV in real time, obtain the corrected polarization direction distribution and power density distribution, and use the corrected polarization direction distribution and power density distribution as track modeling input parameters; A calculation module is used to fuse the input parameters of the track modeling with the heading offset and velocity vector change of the UAV collected in real time to generate a three-dimensional dynamic track model, and calculate the azimuth correction, elevation compensation coefficient and energy gradient parameter of the ground interception equipment based on the three-dimensional dynamic track model; A generation module is used to input the azimuth correction value, elevation compensation coefficient and energy gradient parameter into a preset dynamic strategy fuser to generate a dynamic interception strategy, wherein the dynamic interception strategy includes: a launch timing optimization sequence and a spatial density distribution matrix.

9. A computing device, characterized in that It comprises a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a multi-means integrated drone countermeasure method as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that: A computer program is stored, and when the computer program is executed by a computer, a multi-means integrated drone countermeasure method as described in any one of claims 1 to 7 is implemented.

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