Signal interference method, system and equipment for approaching and expelling of unmanned aerial vehicle, and medium

Abstract

The invention relates to the technical field of unmanned aerial vehicles, in particular to a signal interference method, system, equipment and medium for approaching and expelling of an unmanned aerial vehicle, and the method comprises the steps: carrying out the differential operation of the relative space distance between an own unmanned aerial vehicle and a suspicious unmanned aerial vehicle and the geological space distance between a ground control center and the suspicious unmanned aerial vehicle; differential measurement data is obtained; performing dynamic tracking on the differential measurement data by using a Kalman filtering algorithm to obtain tracking and positioning information of the suspicious unmanned aerial vehicle; when it is detected that the suspicious unmanned aerial vehicle enters a preset interference signal emission range, determining signal emission power and a signal emission angle vector according to the motion state information of the suspicious unmanned aerial vehicle; and generating an electromagnetic interference signal matched with the working frequency band of the suspicious unmanned aerial vehicle according to the signal transmitting power and the signal transmitting angle vector so as to interfere the suspicious unmanned aerial vehicle. By accurately measuring and dynamically tracking the position of the suspicious unmanned aerial vehicle, a matched electromagnetic interference signal is generated, and effective interference on the suspicious unmanned aerial vehicle is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicles, and in particular, to a signal interference method, system, device and medium for close-range driving away of unmanned aerial vehicles. Background Art

[0002] With the rapid development of unmanned aerial vehicle technology, its applications in civilian and industrial fields are becoming increasingly widespread. However, the popularization of unmanned aerial vehicles has also brought severe challenges to the safe operation of key power facilities such as substations, power plants, and transmission lines. Whether it is a small civilian aerial photography unmanned aerial vehicle or an industrial-grade unmanned aerial vehicle with potentially malicious control, it may cause equipment damage due to collision with key power facilities, thereby triggering power transmission interruption, affecting power supply in a large area, and even causing catastrophic consequences such as fire or explosion, posing a severe challenge to public safety.

[0003] In the face of this problem, the existing means of preventing unmanned aerial vehicles have many significant deficiencies. Traditional detection methods are difficult to accurately and timely detect the traces of unmanned aerial vehicles in the complex electromagnetic environment and vast operation area where power facilities are located, resulting in frequent missed detections or misjudgments by existing detection equipment. This leads to a lack of reliable early warning information for subsequent defense actions, making power facilities in a passive defense state when facing unmanned aerial vehicle intrusion. At the same time, there are also many deficiencies in the existing signal interference methods. The adjustment of interference power lacks flexibility, and interference devices often can only transmit at a fixed power. This leads to excessive interference with unmanned aerial vehicles at close range, causing an expansion of the electromagnetic pollution range and affecting the normal operation of surrounding legal communication devices; while at long range, the power is insufficient to effectively interfere with unmanned aerial vehicles, enabling them to continue approaching power facilities, seriously affecting the defense effect and unable to reliably protect power station equipment from the threat of unmanned aerial vehicle intrusion;

[0004] In addition, most of the existing unmanned aerial vehicle interference devices lack the vector adjustment function, and the adjustment method of the emission angle lacks flexibility. In the actual power station defense scenario, due to the dynamic changes in the flight attitude and position of unmanned aerial vehicles, the interference device cannot quickly and accurately adjust the emission angle to aim at suspicious unmanned aerial vehicles, greatly affecting the transmission efficiency and coverage range of interference signals and resulting in a significant reduction in the interference success rate.

[0005] In summary, there are many problems with the existing unmanned aerial vehicle interference technology in the face of the defense requirements of power facilities, and it is difficult to effectively ensure the safe and stable operation of power facilities. Therefore, there is an urgent need to provide a more efficient and flexible signal interference method for close-range driving away of unmanned aerial vehicles to improve the safety protection ability of power facilities. Summary of the Invention

[0006] To solve the above technical problems, the present invention provides a signal interference method, system, device and medium for close-range driving away of unmanned aerial vehicles.

[0007] In a first aspect, the present invention provides a signal interference method for approaching and driving away drones, and the method includes the following steps:

[0008] Measure the relative spatial distance between the own drone and the suspicious drone through a detection device; the relative spatial distance includes the horizontal relative distance, the vertical relative distance, and the spatial relative position relationship between the own drone and the suspicious drone;

[0009] Obtain the geological spatial distance between the ground control center and the suspicious drone, and perform a difference operation on the relative spatial distance and the geological spatial distance to obtain difference measurement data;

[0010] Use the Kalman filter algorithm to continuously and dynamically track the difference measurement data to obtain the tracking and positioning information of the suspicious drone;

[0011] When it is detected through the tracking and positioning information that the suspicious drone enters the preset interference signal emission range, determine the signal emission power and the signal emission angle vector according to the motion state information of the suspicious drone;

[0012] Generate an electromagnetic interference signal matching the working frequency band of the suspicious drone according to the signal emission power and the signal emission angle vector, and control the own drone to interfere with the suspicious drone through the electromagnetic interference signal.

[0013] In a further embodiment, the step of using the Kalman filter algorithm to continuously and dynamically track the difference measurement data to obtain the tracking and positioning information of the suspicious drone includes:

[0014] Initialize the state vector of the Kalman filter algorithm according to the state information of the suspicious drone at the initial moment;

[0015] Predict the current state of the suspicious drone using the Kalman filter algorithm according to the state information of the suspicious drone at the previous moment to obtain the predicted state and the predicted error covariance of the suspicious drone at the current moment;

[0016] Perform noise elimination and filtering processing on the difference measurement data to obtain preprocessed difference data, and compare the preprocessed difference data with the predicted state of the suspicious drone to obtain a measurement residual;

[0017] Calculate the Kalman gain according to the measurement residual, the predicted state of the suspicious drone, and the predicted error covariance, and use the Kalman gain to correct the predicted state of the suspicious drone to obtain the optimal state estimate value at the current moment;

[0018] Take the optimal state estimate value at the current moment as the initial value of the predicted state of the suspicious UAV at the next moment. After multiple iterations, output the optimal estimate value sequence of the position and speed of the suspicious UAV;

[0019] Obtain the tracking and positioning information of the suspicious UAV according to the optimal estimate value sequence of the position and speed of the suspicious UAV.

[0020] In a further implementation, the step of determining the signal transmission power and the signal transmission angle vector according to the motion state information of the suspicious UAV when it is detected by the tracking and positioning information that the suspicious UAV enters the preset interference signal transmission range includes:

[0021] Continuously track the suspicious UAV according to the tracking and positioning information of the suspicious UAV, and when the detection device detects that the suspicious UAV enters the preset warning range, start the own UAV to perform a close approach action;

[0022] During the process of the own UAV approaching the suspicious UAV, if it is monitored according to the tracking and positioning information that the relative spatial distance between the suspicious UAV and the own UAV enters the preset interference signal transmission range, then according to the operation state information of the suspicious UAV, use the dynamic path planning algorithm to generate the optimal close approach path;

[0023] Dynamically determine the signal transmission power according to the real-time relative position information of the suspicious UAV and the own UAV on the optimal close approach path;

[0024] Determine the signal transmission angle vector including horizontal and vertical direction components according to the real-time relative position information of the suspicious UAV and the own UAV on the optimal close approach path and the motion state information of the suspicious UAV by using the angle control algorithm.

[0025] In a further implementation, the step of generating the optimal close approach path by using the dynamic path planning algorithm according to the operation state information of the suspicious UAV includes:

[0026] Real-time collect the operation state information of the suspicious UAV; the operation state information includes the real-time change information of the speed of the suspicious UAV, the real-time change information of the heading angle, the real-time change information of the acceleration, and the real-time change information of the operation trajectory;

[0027] According to the relative spatial distance between the suspicious UAV and the own UAV and the operation state information of the suspicious UAV, adaptively update and obtain the dynamic composite weight by using the reinforcement learning algorithm;

[0028] Discretize the three-dimensional flight space where the suspicious UAV is located into grids to form a discrete state space; where each grid is a state node;

[0029] Add the current position of the own UAV as the starting node to the list of nodes to be evaluated, and use the current position of the suspicious UAV as the target node. According to the Euclidean distance between the suspicious UAV and the own UAV, obtain the initial estimated cost of each node to be evaluated;

[0030] Select the node to be evaluated with the minimum initial estimated cost from the list of nodes to be evaluated as the current node. If the current node is the target node, backtrack through the parent node pointer to obtain the optimal approaching path from the own UAV to the suspicious UAV;

[0031] If the current node is not the target node, move the current node from the list of nodes to be evaluated to the list of evaluated nodes, and calculate the movement cost between nodes according to the distance and dynamic composite weight between the current node and its adjacent nodes;

[0032] According to the movement cost between nodes, use the A-Star algorithm to evaluate the adjacent nodes of the current node, select the adjacent node with the minimum estimated cost as the next node to be evaluated, and repeat this step until the optimal approaching path from the own UAV to the suspicious UAV is found in the discrete state space.

[0033] In a further implementation, the step of dynamically determining the signal transmission power according to the real-time relative position information of the suspicious UAV and the own UAV on the optimal approaching path includes:

[0034] Judge whether there is an obstacle occlusion between the suspicious UAV and the own UAV according to the real-time relative position information of the suspicious UAV and the own UAV on the optimal approaching path;

[0035] When there is no obstacle occlusion between the suspicious UAV and the own UAV, determine the power-distance interval to which the straight-line distance between the suspicious UAV and the own UAV belongs;

[0036] If the power-distance interval is in the short-distance interval, use the lowest base power as the signal transmission power;

[0037] If the power-distance interval is between the short-distance interval and the long-distance interval or the power-distance interval is in the long-distance interval, use the real-time relative position information of the suspicious UAV and the own UAV on the optimal approaching path as the independent variable, and calculate the signal transmission power using a linear increasing function.

[0038] In a further implementation, the step of using the angle control algorithm to determine the signal transmission angle vector including horizontal and vertical direction components according to the real-time relative position information of the suspicious UAV and the own UAV on the optimal approaching path and the motion state information of the suspicious UAV includes:

[0039] Based on the real-time relative position information between the suspicious drone and the own drone on the optimal approaching path, calculate the position differences between the own drone and the suspicious drone in the horizontal and vertical directions;

[0040] Obtain the real-time speed change information of the suspicious drone according to the motion state information of the suspicious drone, and perform time integration on the real-time speed change information of the suspicious drone in the horizontal and vertical directions to obtain the horizontal displacement change amount and the vertical displacement change amount of the suspicious drone during the electromagnetic interference signal emission delay time;

[0041] According to the position differences between the own drone and the suspicious drone in the horizontal and vertical directions, the horizontal displacement change amount and the vertical displacement change amount of the suspicious drone, use the trigonometric relationship to calculate the preliminary horizontal launch angle component and the preliminary vertical launch angle component;

[0042] Introduce an environmental correction coefficient to correct the preliminary horizontal launch angle component and the preliminary vertical launch angle component to obtain a signal launch angle vector including horizontal and vertical direction components.

[0043] In a further implementation scheme, the step of generating an electromagnetic interference signal matching the working frequency band of the suspicious drone according to the signal emission power and the signal emission angle vector includes:

[0044] Obtain the original radio signal of the suspicious drone, and perform noise filtering processing on the original radio signal by using an adaptive filter based on the least mean square error criterion to obtain a radio filtered signal;

[0045] Perform a fast Fourier transform on the radio filtered signal, and perform butterfly operations in combination with the radix-2 time-domain Fourier transform algorithm to generate a frequency-domain feature matrix; wherein, the frequency-domain feature matrix includes the amplitude spectral density and phase information of the radio filtered signal at different frequency points;

[0046] Construct a two-dimensional spectrum distribution map of the suspicious drone according to the frequency-domain feature matrix, traverse the two-dimensional spectrum distribution map by using the sliding window energy detection method, and screen out the frequency corresponding to the maximum spectral peak as the candidate center frequency of the radio filtered signal by comparing the power spectral density in each local area;

[0047] Perform gradient searches on both sides of the candidate center frequency in the two-dimensional spectrum distribution map, locate the boundary frequency points where the power spectral density drops to half, and calculate the effective signal bandwidth according to the boundary frequency points;

[0048] Input the candidate center frequency and the effective signal bandwidth into a preset drone frequency band feature database for pattern matching to determine the working frequency band parameters of the suspicious drone;

[0049] Generate an electromagnetic interference signal that matches the operating frequency band of the suspicious drone based on the operating frequency band parameters, the signal transmission power, and the signal transmission angle vector of the suspicious drone.

[0050] In a second aspect, the present invention provides a signal interference system for repelling drones approaching closely. The system includes:

[0051] A drone detection module for measuring the relative spatial distance between the own drone and the suspicious drone through a detection device; the relative spatial distance includes the horizontal relative distance, the vertical relative distance, and the spatial relative position relationship between the own drone and the suspicious drone.

[0052] A differential measurement module for obtaining the geological spatial distance between the ground control center and the suspicious drone, and performing a differential operation on the relative spatial distance and the geological spatial distance to obtain differential measurement data.

[0053] A tracking and positioning module for continuously and dynamically tracking the differential measurement data using the Kalman filtering algorithm to obtain the tracking and positioning information of the suspicious drone.

[0054] A transmission analysis module for determining the signal transmission power and the signal transmission angle vector according to the motion state information of the suspicious drone when it is detected that the suspicious drone enters the preset interference signal transmission range through the tracking and positioning information.

[0055] A signal generation module for generating an electromagnetic interference signal that matches the operating frequency band of the suspicious drone according to the signal transmission power and the signal transmission angle vector, and controlling the own drone to interfere with the suspicious drone through the electromagnetic interference signal.

[0056] In a third aspect, the present invention further provides a computer device, including a processor and a memory. The processor is connected to the memory. The memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory so that the computer device executes the steps of implementing the above method.

[0057] In a fourth aspect, the present invention further provides a computer-readable storage medium. A computer program is stored in the computer-readable storage medium. When the computer program is executed by a processor, the steps of implementing the above method are realized.

[0058] The present invention provides a signal interference method, system, device and medium for close-range drone repulsion. The method measures the relative spatial distance between the own drone and the suspicious drone through a detection device; obtains the geological spatial distance between the ground control center and the suspicious drone, and performs a differential operation on the relative spatial distance and the geological spatial distance to obtain differential measurement data; uses the Kalman filter algorithm to continuously dynamically track the differential measurement data to obtain the tracking and positioning information of the suspicious drone; when it is detected through the tracking and positioning information that the suspicious drone enters the preset interference signal emission range, determines the signal emission power and the signal emission angle vector according to the motion state information of the suspicious drone; generates an electromagnetic interference signal matching the working frequency band of the suspicious drone according to the signal emission power and the signal emission angle vector, and controls the own drone to interfere with the suspicious drone through the electromagnetic interference signal. Compared with the prior art, this method accurately measures and dynamically tracks the position of the suspicious drone, generates a matching electromagnetic interference signal, and realizes effective interference with the suspicious drone. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 is a schematic flow chart of the signal interference method for close-range drone repulsion provided by an embodiment of the present invention;

[0060] Figure 2 is a schematic diagram of the suspicious drone recognition process provided by an embodiment of the present invention;

[0061] Figure 3 is a schematic diagram of the electromagnetic interference signal generation process provided by an embodiment of the present invention;

[0062] Figure 4 is a schematic diagram of the buffer warning range division provided by an embodiment of the present invention;

[0063] Figure 5 is a schematic flow chart of the signal interference range judgment provided by an embodiment of the present invention;

[0064] Figure 6 is a schematic flow chart of the suspicious drone working frequency band judgment provided by an embodiment of the present invention;

[0065] Figure 7 is a block diagram of the signal interference system for close-range drone repulsion provided by an embodiment of the present invention;

[0066] Figure 8 is a schematic structural diagram of the computer device provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0067] The embodiments of the present invention will be specifically described below in conjunction with the accompanying drawings. The given examples are only for illustrative purposes and should not be construed as limiting the present invention. The included drawings are for reference and illustration only and do not constitute a limitation on the scope of patent protection of the present invention, because many changes can be made to the present invention without departing from the spirit and scope of the present invention.

[0068] Reference Figure 1 , an embodiment of the present invention provides a signal interference method for close-range driving away of unmanned aerial vehicles (UAVs). As Figure 1 shown, the method includes the following steps:

[0069] S1. Determine the relative spatial distance between the own UAV and the suspicious UAV through a detection device; the relative spatial distance includes the horizontal relative distance, vertical relative distance, and spatial relative position relationship between the own UAV and the suspicious UAV.

[0070] Specifically, as Figure 2 shown, in this embodiment, high-performance detection devices are respectively configured on the own UAV and the ground control center, and the detection devices are used to scan all directions within the buffer range for the received air radio signals, monitor the intensity of the signals in real time, obtain real-time signal intensity data, and based on the real-time signal intensity data of multiple consecutive sampling points, when the real-time signal intensity continuously exceeds the preset environmental background noise threshold, it is determined that a suspicious UAV is found. At the same time, the direction of the signal source is locked according to the signal intensity data to obtain the position information of the suspicious UAV. Then, in this embodiment, according to the locked suspicious UAV, the detection device is used to determine the horizontal relative distance, vertical relative distance, and spatial relative position relationship between the own UAV and the suspicious UAV. Among them, the horizontal relative distance can obtain the longitude and latitude information of the own UAV and the suspicious UAV through the GPS module and calculate the straight-line distance between the two; the vertical relative distance can use devices such as a barometer, radar, or laser rangefinder, combined with the GPS altitude information, to measure the vertical distance between the two; the spatial relative position relationship can combine the horizontal relative distance and the vertical relative distance, as well as the attitude information such as the heading, pitch, and roll of the two, to determine the three-dimensional spatial position relationship between the two UAVs.

[0071] Meanwhile, the ground control center uses its detection equipment to obtain the geological space distance between the ground control center and the suspicious drone. Based on the GPS position information of the suspicious drone and in combination with GIS data (such as topographic maps, elevation models, etc.), the ground control center calculates the straight-line distance from the ground control center to the suspicious drone and the actual path distance considering terrain factors, thus serving as the geological space distance between the ground control center and the suspicious drone. In addition, the radio signal receiver in the detection equipment continuously scans the air frequency band to capture the communication signals of the suspicious drone, identify the spectral characteristics of the suspicious drone, identify the identity and communication protocol of the drone, and transmit the detected radio signal information to the control center, providing data support for drone safety management and emergency response.

[0072] S2. Obtain the geological space distance between the ground control center and the suspicious drone, and perform a difference operation on the relative space distance and the geological space distance to obtain difference measurement data.

[0073] Specifically, in this embodiment, it is necessary to ensure that the relative space distance data and geological space distance data collected from the friendly drone and the ground control center are synchronized in time to avoid errors caused by time differences. For each set of synchronized distance data, perform a difference operation on the relative space distance data between the friendly drone and the suspicious drone and the geological space distance data between the ground control center and the suspicious drone to obtain the relative position change information between the two. For example, calculate the difference between the horizontal distance from the friendly drone to the suspicious drone and the horizontal distance from the ground control center to the suspicious drone, as well as the difference in vertical distance. Store the results of the difference operation in the database of the data processing center for subsequent analysis and use.

[0074] S3. Use the Kalman filter algorithm to continuously and dynamically track the difference measurement data to obtain the tracking and positioning information of the suspicious drone.

[0075] In some embodiments, the step of using the Kalman filter algorithm to continuously and dynamically track the difference measurement data to obtain the tracking and positioning information of the suspicious drone includes:

[0076] Initialize the state vector of the Kalman filter algorithm according to the state information of the suspicious drone at the initial moment;

[0077] According to the state information of the suspicious drone at the previous moment, use the Kalman filter algorithm to predict the current state of the suspicious drone to obtain the predicted state and predicted error covariance of the suspicious drone at the current moment;

[0078] Perform noise elimination and filtering on the difference measurement data to obtain preprocessed difference data, and compare the preprocessed difference data with the predicted state of the suspicious drone to obtain the measurement residual;

[0079] Calculate the Kalman gain based on the measurement residual, the predicted state of the suspicious UAV, and the predicted error covariance, and use the Kalman gain to correct the predicted state of the suspicious UAV to obtain the optimal state estimate value at the current moment;

[0080] Use the optimal state estimate value at the current moment as the initial value for predicting the state of the suspicious UAV at the next moment. After multiple iterations, output the optimal estimate value sequence of the position and speed of the suspicious UAV;

[0081] Obtain the tracking and positioning information of the suspicious UAV according to the optimal estimate value sequence of the position and speed of the suspicious UAV.

[0082] Specifically, in this embodiment, according to the state information (such as position, speed, etc.) of the suspicious UAV at the initial moment, the state vector of the Kalman filter algorithm is initialized, and then the current state is predicted using a linear system model. The state transition equation is:

[0083] x k|k-1 = Fx k-1|k-1 + Bu k

[0084] where, assuming the UAV moves at a constant speed, the state transition matrix F is:

[0085]

[0086] In the formula, F is the state transition matrix; x k|k-1 is the state vector at the current moment; x k-1|k-1 is the state vector at the previous moment; B is the control input matrix; u k is the control input vector. Among them, if the UAV has no external control input (such as GPS automatic adjustment), then B = 0, and the control input vector u k = 0.

[0087] If the error covariance matrix at the previous moment is P k-1|k-1 , the predicted error covariance at the current moment can be obtained through the state transition matrix F and the process noise covariance matrix Q. The updated predicted error covariance matrix P k|k-1 can reflect the uncertainty of the current predicted state. The calculation formula for the predicted error covariance is:

[0088] P k|k-1 = FPF k-1|k-1 F T + Q

[0089] Since differential measurement data (such as relative position change data obtained based on satellite signal differential technology) usually contains high-frequency noise, noise elimination and filtering need to be performed first. To improve the accuracy and reliability of the data, this embodiment also needs to perform background noise elimination operations on the differential data. By analyzing the statistical characteristics of the differential data (such as mean, variance, peak value, etc.), the background noise caused by measurement errors, environmental factors, or equipment noise is identified, and a low-pass filtering algorithm is used to process the differential data to eliminate or weaken the influence of the background noise. The preprocessed differential measurement data z k is compared with the predicted state x k|k-1 to calculate the measurement residual v k . The calculation formula for the measurement residual is:

[0090] v k = z k - Hx k|k-1

[0091] where, when the sensor measures the position of the UAV, the observation matrix H is:

[0092]

[0093] In this embodiment, the Kalman gain is calculated using the prediction error covariance and the predicted state of the suspicious UAV, and the predicted state is corrected using the Kalman gain and the measurement residual to obtain the optimal state estimate at the current moment. The mathematical expression for the optimal state estimate at the current moment is:

[0094] x k|k = x k|k-1 + Kv k

[0095] At the same time, the error covariance matrix is updated:

[0096] P k|k = (I - KH)P k|k-1

[0097] This embodiment uses the current optimal state estimate and covariance matrix as the initial values for the next moment. Through continuous iteration, the Kalman gain can balance the weights of the predicted value and the measured value, achieve the optimal estimation of the state, and output the optimal estimation value sequence of the position and speed of the suspicious UAV. This sequence effectively filters out noise and uncertainty by integrating the measurement information at multiple time points and using the optimal estimation characteristics of the Kalman filter, providing a stable positioning result.

[0098] S4. When it is detected through the tracking and positioning information that the suspicious UAV enters the preset interference signal emission range, determine the signal emission power and signal emission angle vector according to the motion state information of the suspicious UAV.

[0099] In some embodiments, the step of determining the signal transmission power and the signal transmission angle vector according to the motion state information of the suspicious drone when it is detected that the suspicious drone enters the preset interference signal transmission range by the tracking and positioning information includes:

[0100] Continuously track the suspicious drone according to the tracking and positioning information of the suspicious drone, and when the detection device detects that the suspicious drone enters the preset warning range, start the own drone to perform a close approach action;

[0101] During the process of the own drone approaching the suspicious drone, if it is monitored according to the tracking and positioning information that the relative spatial distance between the suspicious drone and the own drone enters the preset interference signal transmission range, then according to the operating state information of the suspicious drone, use the dynamic path planning algorithm to generate the optimal close approach path;

[0102] Dynamically determine the signal transmission power according to the real-time relative position information of the suspicious drone and the own drone on the optimal close approach path;

[0103] According to the real-time relative position information of the suspicious drone and the own drone on the optimal close approach path and the motion state information of the suspicious drone, use the angle control algorithm to determine the signal transmission angle vector including horizontal and vertical direction components.

[0104] Specifically, as Figure 3 shown, in this embodiment, the tracking and positioning information obtained by the Kalman filter algorithm is transmitted to the central control system through wireless communication. The central control system processes the tracking and positioning information in real time to update the real-time state of the suspicious drone, and sets a preset buffer warning range around the key area according to the actual task requirements and the performance of its own equipment. As Figure 4 shown, the buffer warning range is divided into an outer buffer range for early warning observation and an inner warning range surrounding key facilities. The range boundary is set comprehensively according to factors such as the electromagnetic characteristics, terrain conditions, and surrounding equipment distribution in the actual environment. This range is accurately calibrated by the Geographic Information System (GIS). When the detection device detects that the suspicious drone enters the preset outer buffer range, it alarms the control center. The central control system automatically triggers an instruction to start the own drone to change from the standby state to the active state and continuously monitor the position of the suspicious drone; when the detection device detects that the suspicious drone enters the preset inner warning range, the central control system automatically triggers an instruction to start the own drone to start performing the close approach action and the electromagnetic interference mode.

[0105] As Figure 5As shown, in this embodiment, during the approach of the own UAV, the relative spatial distance between the own UAV and the suspicious UAV is continuously monitored through tracking and positioning information, the operating state information of the suspicious UAV such as flight altitude, speed, and direction is analyzed, and a preset interference signal emission range is set according to the performance parameters of the interference device. When it is monitored that the relative spatial distance enters the interference signal emission range and it is confirmed that there is no obstacle blocking between the own UAV and the suspicious UAV, the own UAV maintains a stable hovering state, and using a dynamic path planning algorithm, combined with the operating state and relative position information of the suspicious UAV, an optimal approach path is generated. This path aims to minimize the collision risk and maximize the effectiveness of interference signal emission. In some embodiments, the step of generating an optimal approach path using a dynamic path planning algorithm according to the operating state information of the suspicious UAV includes:

[0106] Real-time collect the operating state information of the suspicious UAV; the operating state information includes the real-time change information of the speed of the suspicious UAV, the real-time change information of the heading angle, the real-time change information of the acceleration, and the real-time change information of the flight trajectory;

[0107] According to the relative spatial distance between the suspicious UAV and the own UAV and the operating state information of the suspicious UAV, adaptively update and obtain a dynamic composite weight using a reinforcement learning algorithm;

[0108] Discretize the three-dimensional flight space where the suspicious UAV is located into grids to form a discrete state space; where each grid is a state node;

[0109] Add the current position of the own UAV as the starting node to the list of nodes to be evaluated, take the current position of the suspicious UAV as the target node, and obtain the initial estimated cost of each node to be evaluated according to the Euclidean distance between the suspicious UAV and the own UAV;

[0110] Select the node to be evaluated with the minimum initial estimated cost from the list of nodes to be evaluated as the current node. If the current node is the target node, then backtrack through the parent node pointer to obtain the optimal approach path from the own UAV to the suspicious UAV;

[0111] If the current node is not the target node, then move the current node from the list of nodes to be evaluated to the list of evaluated nodes, and calculate the movement cost between nodes according to the distance between the current node and its adjacent nodes and the dynamic composite weight;

[0112] According to the movement cost between nodes, use the A-Star algorithm to evaluate the adjacent nodes of the current node, select the adjacent node with the minimum estimated cost as the next node to be evaluated, and repeat this step until the optimal approach path from the own UAV to the suspicious UAV is found in the discrete state space.

[0113] Specifically, in this embodiment, a lidar or an inertial navigation system (INS) is deployed on the own UAV to collect the operation state information of the suspicious UAV in real time, and the real-time change information of the speed, the real-time change information of the heading angle, the real-time change information of the acceleration, and the real-time change information of the operation trajectory of the suspicious UAV collected in real time are preprocessed, such as filtering, denoising, etc., to improve the accuracy and reliability of the data. At the same time, a reinforcement learning model is constructed. The model takes the operation state information of the suspicious UAV and the relative spatial distance as inputs and outputs dynamic composite weights. In the training stage, historical data or simulated data is used to train the reinforcement learning model so that it can learn how to adjust the dynamic composite weights to optimize the path planning in different situations. The environment is composed of the three-dimensional space where the suspicious UAV and the own UAV are located and the relevant operation states. The state space includes multiple factors such as the relative position, relative speed, relative acceleration, and relative heading angle between the suspicious UAV and the own UAV. For example, the relative position can be converted into the difference of three-dimensional space coordinates, the relative speed is the speed of the suspicious UAV minus the speed of the own UAV, and the relative heading angle is the difference between the heading angle of the suspicious UAV and the heading angle of the own UAV. The action of the intelligent agent is to adjust the dynamic composite weights. For example, the weights can be divided into multiple dimensions, such as position weight, speed weight, heading angle weight, etc. The intelligent agent changes the priority of the path planning by making small incremental adjustments to these weights. The reward function is used to guide the intelligent agent to learn the optimal weight update strategy. In this embodiment, a positive reward can be given when the own UAV approaches the suspicious UAV, and a negative reward can be given when the distance increases or the collision risk rises. For example, when the relative distance decreases by 1 meter, the reward increases by 1 point; when the distance increases by 1 meter or the heading angle difference is too large, which may cause the trajectory to deviate beyond the preset threshold, the reward decreases by 2 points. In practical applications, after the operation state information of the suspicious UAV is collected in real time, the current state is input into the trained reinforcement learning model, and the model outputs the corresponding dynamic composite weights according to the state information and the learned strategy.

[0114] Then, in this embodiment, the size of each grid is determined according to the range and accuracy requirements of the flight space, and the three-dimensional flight space is divided into a series of uniform grids. Each grid represents a state node, and the state node includes position information and other relevant attributes at that position, such as whether it is an obstacle, whether it is covered and monitored by its own UAV or other devices, etc. All grid nodes are combined to form a discrete state space. The size of the grid can be adjusted according to the actual situation to ensure that the grid division can cover the entire flight area. Then, the grid node where the current position of its own UAV is located is used as the starting node and added to the list of nodes to be evaluated. The grid node where the current position of the suspicious UAV is located is used as the target node. According to the Euclidean distance between the suspicious UAV and its own UAV, the initial estimated cost of each node to be evaluated is calculated. Specifically, each node contains multiple attributes such as position coordinates, a pointer to the parent node, the movement cost (g-score, the actual movement cost from the starting node to the current node), and the heuristic cost (h-score, the estimated cost from the current node to the target node). Initially, the movement cost of the starting node is 0, and at the same time, according to the Euclidean distance between the suspicious UAV and its own UAV, the initial estimated cost of each node to be evaluated is calculated. The node with the smallest initial estimated cost is selected from the list of nodes to be evaluated as the current node. If the current node is the target node, it means that the shortest path has been found, and the algorithm ends. By backtracking through the pointer to the parent node, the optimal approaching path from its own UAV to the suspicious UAV is obtained. If the current node is not the target node, it is moved from the list of nodes to be evaluated to the list of evaluated nodes to avoid repeated evaluation. According to the distance and dynamic composite weight between the current node and its adjacent nodes, the movement cost between nodes is calculated, and the adjacent nodes of the current node are evaluated using the A-Star algorithm. The adjacent node with the smallest estimated cost is selected as the next node to be evaluated. The above steps are repeated until the optimal approaching path from its own UAV to the suspicious UAV is searched in the discrete state space, realizing the dynamic path planning for the suspicious UAV.

[0115] At the same time, in this embodiment, on the optimal approaching path, the real-time relative position information between the suspicious UAV and its own UAV is continuously updated. According to factors such as the relative distance, the situation of obstacles (if any), and environmental noise, a calculation model for the signal transmission power is established. According to the calculation model, the signal transmission power of its own UAV is dynamically adjusted to ensure that while maintaining effective interference, unnecessary electromagnetic pollution to the surrounding environment is avoided. In some embodiments, the step of dynamically determining the signal transmission power according to the real-time relative position information between the suspicious UAV and its own UAV on the optimal approaching path includes:

[0116] Judging whether there is an obstacle occlusion between the suspicious UAV and its own UAV according to the real-time relative position information between the suspicious UAV and its own UAV on the optimal approaching path;

[0117] When there is no obstacle blocking between the suspicious UAV and the own UAV, determine the power-distance interval to which the straight-line distance between the suspicious UAV and the own UAV belongs;

[0118] If the power-distance interval is in the short-distance interval, use the lowest basic power as the signal transmission power;

[0119] If the power-distance interval is between the short-distance interval and the long-distance interval or the power-distance interval is in the long-distance interval, use the real-time relative position information of the suspicious UAV and the own UAV on the optimal approaching path as the independent variable, and calculate the signal transmission power using a linear increasing function.

[0120] Specifically, in this embodiment, the own UAV is equipped with sensors to obtain three-dimensional data of the surrounding environment. Combining the real-time position information of the suspicious UAV, judge whether the line of sight between the two is blocked by obstacles. If the line of sight is not completely blocked by any obstacles, it is judged that there is no obstacle blocking; otherwise, it is judged that there is obstacle blocking. After confirming that there is no obstacle blocking, according to the straight-line distance between the suspicious UAV and the own UAV, classify it into the corresponding preset power-distance interval. In this embodiment, different strategies are adopted to calculate the signal transmission power according to different power-distance intervals. For the short-distance interval, in this embodiment, the signal transmission power is directly set to the preset lowest basic power (such as 1 mW) to save energy; for the medium and long-distance intervals, in this embodiment, the real-time relative position information of the suspicious UAV and the own UAV is used as the independent variable, and a linear increasing function is used to dynamically calculate the corresponding transmission power according to the real-time relative position information. For example, in this embodiment, the starting power (such as 10 mW) and the power increment coefficient (such as for every 100-meter increase in distance, the power increases by 1 mW) can be set, and then the transmission power is calculated according to the straight-line distance, so as to realize the dynamic adjustment of the communication signal transmission power between the suspicious UAV and the own UAV.

[0121] In addition, in this embodiment, it is also necessary to collect the real-time relative position information of the suspicious UAV and the own UAV on the optimal approaching path and the motion state information of the suspicious UAV (such as velocity vector, acceleration, etc.). Using an angle control algorithm (such as a PID control algorithm, etc.), combining the real-time relative position information and the motion state information, calculate the signal emission angle vector including horizontal and vertical direction components, so that subsequently, according to the calculated emission angle vector, through high-speed motor drive and precise angle sensor feedback control in both the horizontal and vertical dimensions, quickly and accurately rotate the interference signal emission device to ensure that the interference signal can accurately point to the suspicious UAV while avoiding unnecessary interference to other surrounding targets. In some embodiments, the step of determining the signal emission angle vector including horizontal and vertical direction components according to the real-time relative position information of the suspicious UAV and the own UAV on the optimal approaching path and the motion state information of the suspicious UAV using an angle control algorithm includes:

[0122] According to the real-time relative position information of the suspicious UAV and the own UAV on the optimal approaching path, calculate the position differences between the own UAV and the suspicious UAV in the horizontal and vertical directions;

[0123] According to the motion state information of the suspicious UAV, obtain the real-time velocity change information of the suspicious UAV, and perform time integration on the real-time velocity change information of the suspicious UAV in the horizontal and vertical directions to obtain the horizontal direction displacement change amount and the vertical direction displacement change amount of the suspicious UAV during the electromagnetic interference signal emission delay time;

[0124] According to the position differences between the own UAV and the suspicious UAV in the horizontal and vertical directions, the horizontal direction displacement change amount and the vertical direction displacement change amount of the suspicious UAV, use trigonometric relationships to calculate the preliminary horizontal emission angle component and the preliminary vertical emission angle component;

[0125] Introduce an environmental correction coefficient to correct the preliminary horizontal emission angle component and the preliminary vertical emission angle component to obtain the signal emission angle vector including horizontal and vertical direction components.

[0126] Specifically, in this embodiment, based on the real-time relative position information between the suspicious UAV and the own UAV on the optimal approaching path, the position differences between the two in the horizontal direction (east-west direction, north-south direction) and the vertical direction (height difference) are calculated respectively by using the distance and azimuth angle calculation formulas between two points in three-dimensional space. Then, according to the motion state information of the suspicious UAV, the real-time change information of the speed of the suspicious UAV (including horizontal and vertical components) is obtained. Numerical integration methods such as the trapezoidal method or the Simpson method are used to perform time integration on the real-time change information of the speed to predict the horizontal displacement change amount and the vertical displacement change amount of the suspicious UAV during the electromagnetic interference signal emission delay time. Next, regarding the own UAV as the origin and the suspicious UAV as the target point, a right triangle is constructed based on the position difference and the displacement change amount, and the horizontal emission angle component (the angle with the horizontal direction) and the vertical emission angle component (the angle with the vertical direction) are calculated by using the arctangent function (arctan). The position difference and the displacement change amount in three-dimensional space are projected onto a two-dimensional plane for calculation. At the same time, considering the influence of environmental factors (such as wind speed, wind direction, air density, etc.) on the signal propagation path, parameters such as wind speed, wind direction, and air density in the current environment are obtained through a weather station, a sensor network, or a prediction model. According to the environmental parameters and the electromagnetic wave propagation theory, an environmental correction coefficient is calculated, and the environmental correction coefficient is introduced to correct the preliminary emission angle component, that is, multiplying the preliminary emission angle component by the environmental correction coefficient to obtain the final signal emission angle vector. This embodiment not only considers the real-time relative position information between the suspicious UAV and the own UAV and the motion state information of the suspicious UAV, but also introduces an environmental correction coefficient to achieve accurate calculation of the signal emission angle vector.

[0127] S5. Generate an electromagnetic interference signal matching the working frequency band of the suspicious UAV according to the signal emission power and the signal emission angle vector, and control the own UAV to interfere with the suspicious UAV through the electromagnetic interference signal.

[0128] In some embodiments, the step of generating an electromagnetic interference signal matching the working frequency band of the suspicious UAV according to the signal emission power and the signal emission angle vector includes:

[0129] Obtain the original radio signal of the suspicious UAV, and perform noise filtering processing on the original radio signal by using an adaptive filter based on the least mean square error criterion to obtain a radio filtered signal;

[0130] Perform a fast Fourier transform on the radio filtered signal, and perform butterfly operations in combination with the radix-2 time division Fourier transform algorithm to generate a frequency domain feature matrix; wherein, the frequency domain feature matrix includes the amplitude spectral density and phase information of the radio filtered signal at different frequency points;

[0131] Construct a two-dimensional spectrum distribution map of the suspicious UAV based on the frequency-domain feature matrix, traverse the two-dimensional spectrum distribution map using the sliding window energy detection method, and screen out the candidate center frequency of the radio filtering signal with the frequency corresponding to the maximum spectral peak by comparing the power spectral densities in each local area;

[0132] Perform gradient searches on both sides of the candidate center frequency in the two-dimensional spectrum distribution map, locate the boundary frequency points where the power spectral density drops to half, and calculate the effective signal bandwidth based on the boundary frequency points;

[0133] Input the candidate center frequency and the effective signal bandwidth into a preset UAV frequency band feature database for pattern matching to determine the working frequency band parameters of the suspicious UAV;

[0134] Generate an electromagnetic interference signal matching the working frequency band of the suspicious UAV according to the working frequency band parameters of the suspicious UAV, the signal transmission power, and the signal transmission angle vector.

[0135] Specifically, as Figure 6 shown, in this embodiment, a detection device is used to capture the original radio signal emitted by the suspicious UAV in real time, ensuring that the frequency band coverage range of the detection device is wide enough to cover common frequency bands (such as 2.4G, 5.8G) and potential extended frequency bands. Then, an adaptive filter based on the least mean square error criterion is used to filter the original signal. The adaptive filter can dynamically adjust the filter parameters according to the noise characteristics in the received signal to accurately remove various types of noise. The filtered signal is the radio filtering signal. Next, this embodiment performs a fast Fourier transform (FFT) on the radio filtering signal to convert the time-domain signal into a frequency-domain signal. After the FFT operation, the signal amplitude and phase information at a series of discrete frequency points are obtained, and the butterfly operation is performed in combination with the radix-2 time-sharing Fourier transform algorithm. According to the FFT operation result, a two-dimensional spectrum distribution map of the suspicious UAV is constructed. The frequency-domain feature matrix contains the amplitude spectral density and phase information at different frequency points for subsequent analysis. At the same time, this embodiment uses the sliding window energy detection method to traverse the two-dimensional spectrum distribution map, and screens out the frequency corresponding to the maximum spectral peak as the candidate center frequency by comparing the power spectral densities in each local area. In the two-dimensional spectrum distribution map, gradient searches are performed from the candidate center frequency to both sides to locate the boundary frequency points where the power spectral density drops to half. The frequency range between these two frequency points is the effective signal bandwidth. The candidate center frequency and the effective signal bandwidth are input into a preset UAV frequency band feature database for pattern matching. The database contains the feature information of common frequency bands (such as 2.4G, 5.8G) and known non-standard frequency bands, and the working frequency band parameters of the suspicious UAV are determined according to the matching result.

[0136] In this embodiment, an electromagnetic interference signal matching the working frequency band of the suspicious UAV is generated according to the determined working frequency band parameters, signal transmission power, and signal transmission angle vector. When it is detected that the main working frequency band of the suspicious UAV is 2.4G or 5.8G, the signal interference module immediately switches to the interference mode of the corresponding frequency band and transmits the electromagnetic interference signal. If it does not match any of the known frequency band characteristic templates, it is determined to be a new non-standard frequency band UAV based on the adaptive spectrum analysis algorithm. When a non-standard frequency band is detected, the signal interference module analyzes and identifies the detected non-standard frequency band, determines key parameters such as its bandwidth and center frequency, and specifically transmits an electromagnetic interference signal covering the non-standard frequency band channel. At the same time, the characteristics of this frequency band are recorded for the update and optimization of the subsequent identification system.

[0137] An embodiment of the present invention provides a signal interference method for approaching and driving away UAVs. The method measures the relative spatial distance between the own UAV and the suspicious UAV through a detection device; obtains the geological spatial distance between the ground control center and the suspicious UAV, and performs a difference operation on the relative spatial distance and the geological spatial distance to obtain difference measurement data; uses the Kalman filter algorithm to continuously and dynamically track the difference measurement data to obtain the tracking and positioning information of the suspicious UAV; when it is detected through the tracking and positioning information that the suspicious UAV enters the preset interference signal transmission range, determines the signal transmission power and signal transmission angle vector according to the motion state information of the suspicious UAV; generates an electromagnetic interference signal matching the working frequency band of the suspicious UAV according to the signal transmission power and signal transmission angle vector, and controls the own UAV to interfere with the suspicious UAV through the electromagnetic interference signal. Compared with the prior art, this method realizes the precise positioning and efficient interference of the suspicious UAV by accurately measuring and dynamically tracking the position of the suspicious UAV, effectively improves the interference success rate and reliability of the own UAV in the approaching and driving away task, and at the same time ensures the accuracy and adaptability of the interference operation, and can stably interfere with the suspicious UAV in a complex environment.

[0138] It should be noted that the magnitudes of the sequence numbers of the above processes do not mean the order of execution. The order of execution of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0139] In one embodiment, as Figure 7 shown, an embodiment of the present invention provides a signal interference system for approaching and driving away UAVs. The system includes:

[0140] A UAV detection module 101, configured to measure the relative spatial distance between the own UAV and the suspicious UAV through a detection device; the relative spatial distance includes the horizontal relative distance, vertical relative distance, and spatial relative position relationship between the own UAV and the suspicious UAV;

[0141] The differential measurement module 102 is configured to obtain the geospatial distance between the ground control center and the suspicious unmanned aerial vehicle (UAV), and perform a differential operation on the relative spatial distance and the geospatial distance to obtain differential measurement data;

[0142] The tracking and positioning module 103 is configured to continuously and dynamically track the differential measurement data by using the Kalman filtering algorithm to obtain the tracking and positioning information of the suspicious UAV;

[0143] The transmission analysis module 104 is configured to determine the signal transmission power and the signal transmission angle vector according to the motion state information of the suspicious UAV when it is detected by the tracking and positioning information that the suspicious UAV enters the preset interference signal transmission range;

[0144] The signal generation module 105 is configured to generate an electromagnetic interference signal matching the working frequency band of the suspicious UAV according to the signal transmission power and the signal transmission angle vector, and control the own UAV to interfere with the suspicious UAV through the electromagnetic interference signal.

[0145] For the specific limitations of a signal interference system for UAV close-range expulsion, reference may be made to the above limitations on a signal interference method for UAV close-range expulsion, which will not be elaborated here. Those of ordinary skill in the art can realize that, in combination with the various modules and steps described in the embodiments disclosed in the present application, they can be implemented by hardware, software, or a combination of both. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0146] An embodiment of the present invention provides a signal interference system for close-range drone expulsion. The drone detection module of the system measures the relative spatial distance between its own drone and a suspicious drone through a detection device; the differential measurement module obtains the geological spatial distance between the ground control center and the suspicious drone, and performs a differential operation on the relative spatial distance and the geological spatial distance to obtain differential measurement data; the tracking and positioning module uses the Kalman filter algorithm to continuously and dynamically track the differential measurement data to obtain the tracking and positioning information of the suspicious drone; when the emission analysis module detects that the suspicious drone enters the preset interference signal emission range through the tracking and positioning information, it determines the signal emission power and the signal emission angle vector according to the motion state information of the suspicious drone; the signal generation module generates an electromagnetic interference signal matching the working frequency band of the suspicious drone according to the signal emission power and the signal emission angle vector, and controls its own drone to interfere with the suspicious drone through the electromagnetic interference signal. Compared with the prior art, the system realizes the precise positioning and efficient interference of the suspicious drone by accurately measuring and dynamically tracking the position of the suspicious drone, effectively improving the interference success rate and reliability of its own drone in the close-range expulsion task, while ensuring the accuracy and adaptability of the interference operation, and being able to stably interfere with the suspicious drone in a complex environment.

[0147] Figure 8 A computer device provided by an embodiment of the present invention includes a memory, a processor, and a transceiver, which are connected through a bus; the memory is used to store a set of computer program instructions and data, and can transmit the stored data to the processor, and the processor can execute the program instructions stored in the memory to perform the steps of the above method.

[0148] Among them, the memory may include a volatile memory or a non-volatile memory, or may include both a volatile and a non-volatile memory; the processor may be a central processing unit, a microprocessor, an application-specific integrated circuit, a programmable logic device, or a combination thereof. By way of example but not limitation, the above programmable logic device may be a complex programmable logic device, a field programmable gate array, a generic array logic, or any combination thereof.

[0149] In addition, the memory may be a physically independent unit or integrated with the processor.

[0150] Those of ordinary skill in the art can understand that Figure 8 the structure shown in

[0151] In one embodiment, the embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above method are implemented.

[0152] A signal interference method, system, device and medium for close-range drone expulsion provided by the embodiment of the present invention. A signal interference method for close-range drone expulsion realizes precise positioning and efficient interference of a suspicious drone by accurately measuring and dynamically tracking the position of the suspicious drone, effectively improving the interference success rate and reliability of its own drone in the close-range expulsion task, while ensuring the accuracy and adaptability of the interference operation, and being able to stably interfere with the suspicious drone in a complex environment.

[0153] In the above embodiment, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present invention are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from a website, a computer, a server or a data center to another website, a computer, a server or a data center by wire (such as coaxial cable, optical fiber, digital subscriber line) or wirelessly (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that integrates one or more available media. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a magnetic tape), an optical medium (for example, a DVD), or a semiconductor medium (for example, an SSD), etc.

[0154] Those skilled in the art can understand that all or part of the processes of implementing the methods in the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods.

[0155] The above-described embodiments merely represent several preferred embodiments of the present application. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the technical principle of the present invention, several improvements and substitutions can be made, and these improvements and substitutions should also be regarded as the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the protection scope of the claims described above.

Claims

1. A signal jamming method for approaching and driving away a drone, characterized in that: The following steps are involved: Determine the relative spatial distance between the own UAV and the suspicious UAV by using detection equipment; the relative spatial distance includes the horizontal relative distance, vertical relative distance and spatial relative position relationship between the own UAV and the suspicious UAV; Obtaining the geological spatial distance between the ground control center and the suspicious UAV, and performing a differential operation on the relative spatial distance and the geological spatial distance to obtain differential measurement data; The differential measurement data is continuously and dynamically tracked using a Kalman filter algorithm to obtain tracking and positioning information of the suspicious UAV; When a suspicious UAV is detected to enter a preset interference signal transmission range through the tracking and positioning information, a signal transmission power and a signal transmission angle vector are determined according to the motion state information of the suspicious UAV; According to the signal transmission power and the signal transmission angle vector, an electromagnetic interference signal matching the operating frequency band of the suspicious drone is generated, and the own drone is controlled to interfere with the suspicious drone through the electromagnetic interference signal.

2. A signal jamming method for approaching and driving away a drone as claimed in claim 1, characterized in that: The step of using the Kalman filter algorithm to continuously and dynamically track the differential measurement data to obtain the tracking and positioning information of the suspicious drone includes: Initialize the state vector of the Kalman filter algorithm according to the state information of the suspicious drone at the initial moment; According to the state information of the suspicious drone at the previous moment, the Kalman filter algorithm is used to predict the current state of the suspicious drone, and the predicted state and prediction error covariance of the suspicious drone at the current moment are obtained; Performing noise elimination and filtering processing on the differential measurement data to obtain pre-processed differential data, and comparing the pre-processed differential data with the predicted state of the suspicious UAV to obtain a measurement residual; A Kalman gain is calculated according to the measurement residual, the predicted state of the suspicious UAV and the prediction error covariance, and the predicted state of the suspicious UAV is corrected by using the Kalman gain to obtain an optimal state estimate at the current moment; The optimal state estimation value at the current moment is used as the initial value for predicting the state of the suspicious drone at the next moment. After multiple iterations, the optimal estimation value sequence of the position and speed of the suspicious drone is output; According to the optimal estimation value sequence of the position and speed of the suspicious drone, the tracking and positioning information of the suspicious drone is obtained.

3. A signal jamming method for approaching and driving away a drone as claimed in claim 1, characterized in that: The step of determining the signal transmission power and the signal transmission angle vector according to the motion state information of the suspicious drone when the suspicious drone is detected to enter the preset interference signal transmission range through the tracking and positioning information includes: Continuously track the suspicious drone based on its tracking and positioning information, and when the detection equipment detects that the suspicious drone has entered the preset warning range, activate your own drone to perform approach action; When the friendly UAV approaches the suspicious UAV, if the relative spatial distance between the suspicious UAV and the friendly UAV is detected to enter the preset interference signal emission range according to the tracking and positioning information, the optimal approach path is generated by using the dynamic path planning algorithm according to the operation status information of the suspicious UAV; Dynamically determine the signal transmission power based on the real-time relative position information between the suspicious drone and the own drone on the optimal approach path; According to the real-time relative position information between the suspicious UAV and its own UAV on the optimal approach path and the motion state information of the suspicious UAV, the angle control algorithm is used to determine the signal transmission angle vector containing horizontal and vertical components.

4. A signal jamming method for approaching and driving away a drone as claimed in claim 3, characterized in that: The step of generating an optimal approach path using a dynamic path planning algorithm according to the operating status information of the suspicious drone includes: Collecting the operating status information of the suspicious drone in real time; the operating status information includes the real-time change information of the speed, heading angle, acceleration and running trajectory of the suspicious drone; According to the relative spatial distance between the suspicious UAV and its own UAV and the operating status information of the suspicious UAV, the dynamic composite weight is adaptively updated using the reinforcement learning algorithm; Discretize the three-dimensional flight space where the suspicious drone is located into grids to form a discrete state space, where each grid is a state node; The current position of the own drone is added as the starting node to the list of nodes to be evaluated, and the current position of the suspicious drone is used as the target node. According to the Euclidean distance between the suspicious drone and the own drone, the initial estimated cost of each node to be evaluated is obtained; Select the node with the smallest initial estimated cost from the list of nodes to be evaluated as the current node. If the current node is the target node, then backtrack through the parent node pointer to obtain the optimal approach path from the own UAV to the suspicious UAV. If the current node is not the target node, move the current node from the list of nodes to be evaluated to the list of evaluated nodes, and calculate the inter-node movement cost based on the distance between the current node and its adjacent nodes and the dynamic composite weight; According to the inter-node movement cost, the A-Star algorithm is used to evaluate the adjacent nodes of the current node, and the adjacent node with the smallest estimated cost is selected as the next node to be evaluated. This step is repeated until the optimal approach path from the own UAV to the suspicious UAV is found in the discrete state space.

5. A signal jamming method for approaching and driving away a drone as claimed in claim 3, characterized in that: The step of dynamically determining the signal transmission power according to the real-time relative position information of the suspicious UAV and the own UAV on the optimal approach path includes: Determine whether there is an obstacle blocking the suspicious drone and the own drone based on the real-time relative position information of the suspicious drone and the own drone on the optimal approach path; When there is no obstacle blocking the suspicious UAV and the own UAV, determine the power distance interval to which the straight-line distance between the suspicious UAV and the own UAV belongs; If the power distance interval is in the short distance interval, the lowest basic power is used as the signal transmission power; If the power distance interval is between the short distance interval and the long distance interval or the power distance interval is in the long distance interval, the real-time relative position information of the suspicious UAV and its own UAV on the optimal approach path is used as the independent variable, and the signal transmission power is calculated using a linear increasing function.

6. A signal jamming method for approaching and driving away a drone as claimed in claim 3, characterized in that: The step of determining the signal transmission angle vector including horizontal and vertical direction components by using an angle control algorithm according to the real-time relative position information of the suspicious UAV and the own UAV on the optimal approach path and the motion state information of the suspicious UAV comprises: According to the real-time relative position information of the suspicious UAV and the own UAV on the optimal approach path, the position difference between the own UAV and the suspicious UAV in the horizontal direction and the vertical direction is calculated; The real-time speed change information of the suspicious UAV is obtained according to the motion state information of the suspicious UAV, and the real-time speed change information of the suspicious UAV is time-integrated in the horizontal and vertical directions to obtain the horizontal displacement change amount and the vertical displacement change amount of the suspicious UAV within the delay time of the electromagnetic interference signal transmission; According to the position difference between the own UAV and the suspicious UAV in the horizontal and vertical directions, the horizontal displacement change and vertical displacement change of the suspicious UAV, the preliminary horizontal launch angle component and the preliminary vertical launch angle component are calculated using trigonometric relationship; The environmental correction coefficient is introduced to correct the preliminary horizontal transmission angle component and the preliminary vertical transmission angle component to obtain a signal transmission angle vector containing horizontal and vertical direction components.

7. A signal jamming method for approaching and driving away a drone as claimed in claim 1, characterized in that: The step of generating an electromagnetic interference signal matching the operating frequency band of the suspicious drone according to the signal transmission power and the signal transmission angle vector includes: Acquire the original radio signal of the suspicious UAV, and use an adaptive filter based on the minimum mean square error criterion to perform noise filtering on the original radio signal to obtain a radio filtered signal; Performing a fast Fourier transform on the radio filter signal, and performing a butterfly operation in combination with a base-2 time-division Fourier transform algorithm to generate a frequency domain feature matrix; wherein the frequency domain feature matrix contains the amplitude spectrum density and phase information of the radio filter signal at different frequency points; A two-dimensional spectrum distribution diagram of the suspicious UAV is constructed according to the frequency domain feature matrix, and a sliding window energy detection method is used to traverse the two-dimensional spectrum distribution diagram, and by comparing the power spectrum density in each local area, a candidate center frequency of the radio filter signal is selected whose corresponding frequency of the maximum spectrum peak is selected; Performing gradient searches on both sides of the candidate center frequency in the two-dimensional spectrum distribution diagram to locate boundary frequency points where the power spectrum density drops to half, and calculating the effective signal bandwidth based on the boundary frequency points; Input the candidate center frequency and the effective signal bandwidth into a preset drone frequency band feature database for pattern matching to determine the operating frequency band parameters of the suspicious drone; According to the operating frequency band parameters of the suspicious drone, the signal transmission power and the signal transmission angle vector, an electromagnetic interference signal matching the operating frequency band of the suspicious drone is generated.

8. A signal jamming system for approaching and driving away drones, characterized in that: The system comprises: The drone detection module is used to determine the relative spatial distance between the own drone and the suspicious drone through the detection equipment; the relative spatial distance includes the horizontal relative distance, vertical relative distance and spatial relative position relationship between the own drone and the suspicious drone; A differential measurement module is used to obtain the geological spatial distance between the ground control center and the suspicious UAV, and perform a differential operation on the relative spatial distance and the geological spatial distance to obtain differential measurement data; A tracking and positioning module, used to continuously and dynamically track the differential measurement data using a Kalman filter algorithm to obtain tracking and positioning information of the suspicious drone; A transmission analysis module, used to determine the signal transmission power and the signal transmission angle vector according to the motion state information of the suspicious drone when the suspicious drone is detected to enter the preset interference signal transmission range through the tracking and positioning information; The signal generation module is used to generate an electromagnetic interference signal that matches the working frequency band of the suspicious drone according to the signal transmission power and the signal transmission angle vector, and control the own drone to interfere with the suspicious drone through the electromagnetic interference signal.

9. A computer device, characterized in that: The computer device comprises a processor and a memory, wherein the processor is connected to the memory, the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, so that the computer device executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed, the method according to any one of claims 1 to 7 is implemented.

Images