Wrist-worn drone countermeasure control method and system

Abstract

The application relates to the technical field of unmanned aerial vehicle control, and particularly discloses a wrist-mounted unmanned aerial vehicle countermeasure control method and system, which can acquire flight state data of an unmanned aerial vehicle through the cooperative work of multiple sensors such as radars, infrared sensors, optical sensors and GPSs, and predict the state and track the target of the unmanned aerial vehicle by using a particle filtering algorithm; the system can judge whether the unmanned aerial vehicle deviates from a track and timely adjust an interference strategy by calculating deviation coefficients of an actual flight track and a preset track through a dynamic time warping algorithm; the frequency, intensity and emission direction of interference signals can be dynamically optimized according to the flight state of the unmanned aerial vehicle to form an efficient countermeasure strategy; and the countermeasure precision coefficient is calculated by comprehensively evaluating the countermeasure response time and success rate, so as to provide data support for the optimization of subsequent countermeasure strategies.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of unmanned aerial vehicle control, in particular to a wrist-mounted unmanned aerial vehicle countermeasure control method and system. BACKGROUND

[0002] With the continuous development of unmanned aerial vehicle technology, unmanned aerial vehicles are increasingly widely used in military, civilian and commercial fields. However, the popularity of unmanned aerial vehicles has brought challenges in airspace safety and privacy protection, especially the interference and intrusion of malicious unmanned aerial vehicles, which pose potential threats to public safety and important facilities. Therefore, how to effectively and accurately control and counter unmanned aerial vehicles in flight has become an important direction of current technical research. In order to solve this problem, a high-efficiency, flexible and accurate wrist-mounted unmanned aerial vehicle countermeasure system is developed, which can monitor, identify and respond to various unmanned aerial vehicle threats in real time, and has become a technical problem to be solved.

[0003] The prior art has the following disadvantages:

[0004] Currently, existing unmanned aerial vehicle countermeasure technologies mainly focus on countermeasures through radio frequency interference or physical interception. However, these technologies often have slow response speed, low interference accuracy, poor adaptability and other problems. For example, traditional radio frequency interference methods may be affected by environmental factors, resulting in poor coverage effect of interference signals, and even may injure other legal equipment. Moreover, many existing countermeasure technologies lack real-time environmental perception and dynamic adjustment capabilities, and cannot flexibly respond to rapidly changing flight trajectories and variable unmanned aerial vehicle behaviors in complex environments. Therefore, existing technologies are difficult to provide efficient and accurate countermeasures when facing complex and dynamic unmanned aerial vehicle threats, and urgently need more intelligent and adaptable solutions. SUMMARY

[0005] The purpose of the present application is to provide a wrist-mounted unmanned aerial vehicle countermeasure control method and system to solve the problems in the above background.

[0006] The purpose of the present application can be achieved by the following technical solutions:

[0007] The wrist-mounted unmanned aerial vehicle countermeasure control method comprises the following steps:

[0008] S1: Real-time monitoring and identification of flight state data of flying unmanned aerial vehicles through multi-sensor data fusion, including radar, infrared, optical sensors and GPS, identifying and locking the state of the unmanned aerial vehicle, ensuring accurate identification of flying unmanned aerial vehicles and locking the target;

[0009] The flight state data includes flight speed, flight direction and flight height.

[0010] S2: According to the flight state of the unmanned aerial vehicle and the target set flight trajectory, dynamic tracking and flight state judgment are carried out to judge whether the unmanned aerial vehicle deviates from the predetermined flight trajectory;

[0011] S3: After determining that the unmanned aerial vehicle does not follow the predetermined flight trajectory, the frequency, intensity and emission direction of the radio frequency interference signal are dynamically adjusted according to the real-time flight state and flight trajectory of the unmanned aerial vehicle, an interference strategy for the unmanned aerial vehicle is formed to interfere with the control link of the unmanned aerial vehicle;

[0012] S4: By collecting the countermeasure response time and countermeasure success rate of multiple interfered unmanned aerial vehicles, the countermeasure precision coefficient is calculated by combining the response time and the success rate, which is used to evaluate the precision of the unmanned aerial vehicle countermeasure control strategy;

[0013] S5: According to the evaluation result, the unmanned aerial vehicle countermeasure control is divided into accurate countermeasure and non-accurate countermeasure, based on the accurate countermeasure, the target information of the unmanned aerial vehicle, the interference strategy execution state and the countermeasure effect are displayed in real time through the operation interface of the wrist-mounted device, and the operator is allowed to manually intervene and adjust the strategy, and a countermeasure effect evaluation report is generated.

[0014] As a further scheme of the application: the state of the unmanned aerial vehicle is identified and locked to ensure that the unmanned aerial vehicle in flight can be accurately identified, specifically comprising:

[0015] Obtain flight state data, process the flight state data by particle filtering, and predict the state of the unmanned aerial vehicle, the specific steps are: initialize a group of particles, each particle represents a flight state data of an unmanned aerial vehicle; predict the next state of each particle according to the motion model, and the calculation expression is: ; In the formula, i represents the number of particles, represents the control input, represents the state transition function of the unmanned aerial vehicle, represents the predicted state of the i-th particle at time step t, represents the process noise, and t represents the time step; At each time step, the particles are weighted according to the current observation data, and the weight of each particle is calculated based on the matching degree between the observation value and the predicted state of the particle. The particle weight is updated by calculating the difference between the predicted position and the observation value, and the calculation expression is: ; In the formula, represents the observation data at time step t, represents the probability of the observation data occurring when the state is given; wherein, The calculation expression of is: ; In the formula, represents the observation equation, represents the variance of the observation noise; resample the particle set after the weight update; calculate the state of the UAV at the current time by weighted average, and the calculation expression is: ; in the formula, N represents the total number of particles, represents the state of the UAV at the current time.

[0016] As a further scheme of the present application: the step of judging whether the UAV deviates from the predetermined flight trajectory specifically comprises:

[0017] obtaining the actual flight state of the UAV, determining the actual flight trajectory, obtaining the target set flight trajectory, comparing and analyzing the actual flight trajectory of the UAV with the target set flight trajectory, calculating the trajectory deviation coefficient according to the deviation value of the actual flight trajectory and the target set flight trajectory, comparing the trajectory deviation coefficient with the preset threshold value, and judging whether the UAV deviates from the predetermined flight trajectory.

[0018] As a further scheme of the present application: the trajectory deviation coefficient acquisition logic is:

[0019] set target trajectory and actual flight trajectory wherein each trajectory contains three-dimensional coordinate points of time steps;

[0020] calculate the Euclidean distance between each point, which represents the difference between the actual trajectory point and the target trajectory point in three-dimensional space;

[0021] construct a distance matrix for representing the distance between the actual trajectory point and the target trajectory point;

[0022] calculate the minimum cumulative distance between the two trajectories according to the distance matrix of the two trajectories;

[0023] calculate the trajectory deviation coefficient by ratio calculation of the minimum cumulative distance and the trajectory length.

[0024] As a further scheme of the present application: the dynamic adjustment of the frequency, intensity and emission direction of the radio frequency interference signal forms an interference strategy for the UAV to interfere with the control link of the UAV, specifically comprising:

[0025] obtain the intensity, frequency and emission direction of the radio frequency interference signal, calculate the distance factor, velocity factor and trajectory deviation factor according to the obtained intensity, frequency and emission direction of the radio frequency interference signal, normalize the distance factor, velocity factor and trajectory deviation factor, calculate the UAV interference coefficient, and adjust the intensity, frequency and emission direction of the radio frequency interference signal according to the UAV interference coefficient; wherein the calculation expression of the UAV interference coefficient is: in the formula, represents the UAV interference coefficient, , and is a preset proportion coefficient, and , and are all greater than 0, d represents a distance between the unmanned aerial vehicle and the interference source, and v represents a flight speed of the unmanned aerial vehicle. represents a speed of a corresponding position on a target trajectory of the unmanned aerial vehicle, represents a deviation of the trajectory of the unmanned aerial vehicle.

[0026] As a further scheme of the present application, the obtaining process of the countermeasure precision coefficient comprises:

[0027] obtaining a plurality of countermeasure response data sets of the disturbed unmanned aerial vehicles, the response data set comprising a countermeasure response time and a countermeasure success rate of the unmanned aerial vehicle; and calculating the countermeasure precision coefficient through fuzzy logic based on processing fuzzy input variables and corresponding membership functions, generating a fuzzy output variable, denoted as the countermeasure precision coefficient.

[0028] The fuzzy input variables comprise the countermeasure response time and the countermeasure success rate.

[0029] As a further scheme of the present application, the calculating of the countermeasure precision coefficient through fuzzy logic comprises:

[0030] the input variable countermeasure response time and the countermeasure success rate ; the output variable is the countermeasure precision coefficient ; the countermeasure response time and the countermeasure success rate are converted into fuzzy sets; fuzzy rules are set according to the countermeasure response time and the success rate, used for calculating the countermeasure precision coefficient; the input fuzzy sets are mapped to output fuzzy sets; the fuzzy output is converted into the clear countermeasure precision coefficient; wherein the calculation process of the countermeasure precision coefficient is: the countermeasure response time and the countermeasure success rate are converted into fuzzy sets, the countermeasure response time is fuzzified through a membership function, and the calculation expression is: ; the countermeasure success rate is fuzzified, and the calculation expression is: ; in the expressions, μ represents a membership function of the countermeasure response time, represents a membership function of the countermeasure success rate, represents a mean value of the countermeasure response time, represents a standard deviation of the countermeasure response time; the countermeasure precision coefficient , the calculation expression is: .

[0031] As a further scheme of the present application: the precision of the anti-precision control strategy of the unmanned aerial vehicle is evaluated, and specifically includes:

[0032] The anti-precision coefficient is compared with a preset threshold value, if the anti-precision coefficient is greater than or equal to the preset threshold value, it indicates that the corresponding unmanned aerial vehicle anti-precision control is accurate, and is recorded as accurate anti-precision, if the anti-precision coefficient is less than the preset threshold value, it indicates that the corresponding unmanned aerial vehicle anti-precision control is not accurate, and is recorded as non-accurate anti-precision.

[0033] For the wrist-mounted unmanned aerial vehicle anti-precision control system, comprising:

[0034] The unmanned aerial vehicle target identification and locking module, through the fusion of multiple sensor data, real-time monitoring of the unmanned aerial vehicle in flight and accurate identification of its flight state data, and locking the unmanned aerial vehicle target;

[0035] The dynamic tracking and flight state judgment module, according to the flight state of the unmanned aerial vehicle and the target set flight trajectory, dynamic tracking and judgment are carried out, whether the unmanned aerial vehicle deviates from the predetermined flight trajectory is analyzed, and the accuracy of the flight state is ensured;

[0036] The radio frequency interference strategy dynamic adjustment module, after confirming that the unmanned aerial vehicle deviates from the predetermined flight trajectory, based on the real-time flight state and trajectory, dynamically adjusts the frequency, intensity and emission direction of the radio frequency interference signal, forms the interference strategy for the unmanned aerial vehicle, and interferes with the control link;

[0037] The anti-precision coefficient calculation and evaluation module, the anti-precision response time and the anti-precision success rate of a plurality of disturbed unmanned aerial vehicles are collected, the anti-precision coefficient is calculated, and the precision of the anti-precision control strategy is evaluated to ensure the effect of the anti-precision strategy;

[0038] The anti-precision control state monitoring and manual intervention module, based on the accurate anti-precision, the operation interface of the wrist-mounted device is used to display the unmanned aerial vehicle target information, the interference strategy execution state and the anti-precision effect in real time, allowing the operator to manually intervene and adjust the strategy, and generating an anti-precision effect evaluation report to optimize the subsequent anti-precision strategy.

[0039] The beneficial effects of the present application are:

[0040] (1) The wrist-mounted unmanned aerial vehicle countermeasure control method provided by the application can realize real-time and accurate monitoring and identification of unmanned aerial vehicles in flight by virtue of multi-sensor data fusion technology, thereby ensuring accurate locking of the target unmanned aerial vehicle. In this process, radar, infrared, optical sensors and GPS multiple sensors work together to provide comprehensive and high-precision data support for the state perception of the unmanned aerial vehicle. By adopting advanced state estimation and target tracking algorithms such as particle filtering, the system can effectively process sensor data in complex environments, greatly reducing the risk of misidentification and false targets. In addition, the application also combines the dynamic time warping algorithm to perform fine calculation and analysis on the deviation of the flight trajectory. This process can detect the deviation state of the unmanned aerial vehicle in time by measuring the difference between the actual flight trajectory and the target set trajectory, thereby providing accurate decision basis for countermeasure operation. Through this multi-level real-time data fusion and accurate deviation analysis, the application ensures that interference can be quickly and efficiently implemented when the unmanned aerial vehicle deviates from the predetermined trajectory, effectively reducing the flight flexibility and operation controllability of the target unmanned aerial vehicle, thereby realizing accurate flight trajectory control.

[0041] (2) The application can adjust the frequency, intensity and emission direction of the radio frequency interference signal in real time and dynamically to accurately respond to unmanned aerial vehicles in different flight states and complex target trajectories based on real-time flight data of the unmanned aerial vehicle, thereby ensuring that the interference strategy can be efficiently and accurately executed in a variable environment. This intelligent optimization process greatly improves the effectiveness of the countermeasure, ensuring that the unmanned aerial vehicle can be quickly and accurately interfered in complex scenarios. By monitoring the target information of the unmanned aerial vehicle, the execution state of the interference strategy and the countermeasure effect in real time, the operator can obtain feedback and flexibly adjust the strategy in the first time to respond to different threat scenarios. At the same time, the system automatically generates a countermeasure effect evaluation report to provide a reliable decision basis for subsequent strategy optimization, improving the adaptability, intelligence and overall accuracy of the countermeasure system, and ensuring that the countermeasure operation can be continuously and efficiently executed in a dynamic and complex combat environment. This method not only enhances the accuracy of countermeasure control, but also makes the unmanned aerial vehicle countermeasure system more flexible and scalable, providing a solid technical guarantee for future unmanned aerial vehicle defense. BRIEF DESCRIPTION OF DRAWINGS

[0042] The application will be further described below with reference to the accompanying drawings.

[0043] Figure 1 is a specific step flow chart of the wrist-mounted unmanned aerial vehicle countermeasure control method of the application;

[0044] Figure 2 is a flow chart of the wrist-mounted unmanned aerial vehicle countermeasure control system in the application. DETAILED DESCRIPTION

[0045] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, but not all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative effort belong to the scope of the present application.

[0046] Please refer to Figure 1 The present application is a wrist-mounted drone countermeasure control method, comprising the following steps:

[0047] S1: Real-time monitoring and identifying the flight state data of the flying drone through multi-sensor data fusion, including radar, infrared, optical sensor and GPS, identifying and locking the state of the drone, ensuring that the flying drone can be accurately identified and the target can be locked to avoid misidentification; wherein the flight state data includes flight speed, flight direction and flight height;

[0048] S2: According to the flight state of the drone and the target set flight trajectory, dynamic tracking and flight state judgment are performed to determine whether the drone deviates from the predetermined flight trajectory;

[0049] S3: After determining that the drone does not follow the predetermined flight trajectory, the frequency, intensity and emission direction of the radio frequency interference signal are dynamically adjusted according to the real-time flight state and flight trajectory of the drone to form an interference strategy for the drone to interfere with the control link of the drone;

[0050] S4: By collecting the countermeasure response time and countermeasure success rate of multiple interfered drones, the countermeasure precision coefficient is calculated by combining the response time and the success rate, which is used to evaluate the precision of the drone countermeasure control strategy;

[0051] S5: According to the evaluation result, the drone countermeasure control is divided into accurate countermeasure and non-accurate countermeasure, based on the accurate countermeasure, the target information of the drone, the execution state of the interference strategy and the countermeasure effect are displayed in real time through the operation interface of the wrist-mounted device, and the operator is allowed to manually intervene and adjust the strategy to generate a countermeasure effect evaluation report for optimizing the subsequent countermeasure strategy.

[0052] In S1, the acquisition process of the flight state data is:

[0053] Wherein, the acquisition process of the flight speed is: combining the radial velocity information of the radar and the GPS position data, the flight speed of the drone is calculated through the calculation of the velocity vector;

[0054] The flight direction acquisition process is: the flight direction of the unmanned aerial vehicle is acquired in real time through the direction data provided by the optical sensor and the radar and the position change of the unmanned aerial vehicle, and the flight direction is obtained through angle calculation and track calculation;

[0055] The flight height acquisition process is: the flight height is determined through the vertical position data provided by the GPS;

[0056] The specific process of identifying and locking the unmanned aerial vehicle target is:

[0057] Flight state data is acquired, the flight state data is processed through particle filtering, and the state of the unmanned aerial vehicle is predicted, and the specific steps are:

[0058] A set of particles is initialized, and each particle represents flight state data of an unmanned aerial vehicle;

[0059] The next state of each particle is predicted according to a motion model, and the calculation expression is:

[0060] ;

[0061] In the formula, i represents the number of particles, represents a control input, represents a state transition function of the unmanned aerial vehicle, represents a predicted state of the i-th particle at time step t, represents process noise, and t represents a time step;

[0062] At each time step, the particles are weighted according to the current observation data, and the weight of each particle is calculated based on the matching degree between the observation value and the predicted state of the particle. The particle weight is updated by calculating the difference between the predicted position and the observation value, and the calculation expression is: ;

[0063] In the formula, represents observation data at time step t, represents the probability of observation data occurring when the given state is given;

[0064] The calculation expression of is: ;

[0065] In the formula, represents an observation equation, represents the variance of observation noise;

[0066] According to the particle set updated by the weight, resampling is performed;

[0067] The state of the UAV at the current moment is calculated by weighted average, and the calculation expression is: ;

[0068] In the formula, N represents the total number of particles, represents the state of the UAV at the current moment.

[0069] In S2, dynamic tracking and flight state judgment are performed according to the flight state of the UAV and the target set flight trajectory, and whether the UAV deviates from the predetermined flight trajectory is judged, which specifically includes:

[0070] The actual flight state of the UAV is obtained, the actual flight trajectory is determined, the target set flight trajectory is obtained, the actual flight trajectory of the UAV is compared with the target set flight trajectory, the trajectory deviation coefficient is calculated according to the deviation value of the actual flight trajectory and the target set flight trajectory, and the trajectory deviation coefficient is compared with the preset threshold value to judge whether the UAV deviates from the predetermined flight trajectory.

[0071] The logic for obtaining the trajectory deviation coefficient is as follows:

[0072] Set the target trajectory and the actual flight trajectory Each trajectory contains a three-dimensional coordinate point of a time step.

[0073] Calculate the Euclidean distance between each point, which represents the difference between the actual trajectory point and the target trajectory point in three-dimensional space.

[0074] Construct a distance matrix to represent the distance between the actual trajectory point and the target trajectory point.

[0075] Calculate the minimum cumulative distance between the two trajectories according to the distance matrix of the two trajectories.

[0076] Calculate the ratio of the minimum cumulative distance to the length of the trajectory to obtain the trajectory deviation coefficient.

[0077] Compare the trajectory deviation coefficient with the preset threshold value.

[0078] If the trajectory deviation coefficient is greater than or equal to the preset threshold value, it means that the UAV flight trajectory deviates and does not fly according to the predetermined flight trajectory.

[0079] If the trajectory deviation coefficient is less than the preset threshold value, it means that the UAV flight trajectory does not deviate and flies according to the predetermined flight trajectory.

[0080] In S3, after determining that the UAV does not follow the predetermined flight trajectory, the frequency, intensity and emission direction of the radio frequency interference signal are dynamically adjusted according to the real-time flight state and flight trajectory of the UAV to form an interference strategy for the UAV to interfere with the control link of the UAV, specifically including:

[0081] obtaining a UAV that does not follow a predetermined flight trajectory;

[0082] obtaining the intensity, frequency and emission direction of the radio frequency interference signal, calculating the distance factor, speed factor and trajectory deviation factor according to the obtained intensity, frequency and emission direction of the radio frequency interference signal, normalizing the distance factor, speed factor and trajectory deviation factor, and calculating the UAV interference coefficient, and adjusting the intensity, frequency and emission direction of the radio frequency interference signal according to the UAV interference coefficient;

[0083] wherein the distance factor indicates that the intensity of the interference signal is inversely proportional to the distance of the UAV;

[0084] The speed factor indicates that the difference between the flight speed of the UAV and the speed of the target trajectory will also affect the interference effect;

[0085] The trajectory deviation factor indicates that the greater the deviation of the trajectory of the UAV from the target trajectory, the higher the degree of deviation of the UAV from the trajectory, and the stronger the interference effect;

[0086] wherein the calculation expression of the UAV interference coefficient is:

[0087]

[0088] wherein, indicates the UAV interference coefficient, , and is a preset proportion coefficient, and , and are all greater than 0, d represents the distance between the UAV and the interference source, v represents the flight speed of the UAV, v represents the speed of the corresponding position on the target trajectory of the UAV, represents the deviation of the trajectory of the UAV;

[0089] adjusting the frequency, intensity and emission direction of the interference signal according to the UAV interference coefficient to interfere with the control link of the UAV, so that the UAV is forced to land;

[0090] comparing the interference coefficient with a preset threshold value, if the interference coefficient exceeds the preset threshold value and the UAV cannot recover the control, the interference strategy is successfully implemented.

[0091] In S4, the countermeasure precision coefficient is calculated by collecting the countermeasure response time and the countermeasure success rate of multiple disturbed unmanned aerial vehicles, combining the countermeasure response time and the countermeasure success rate, and comprehensively calculating the countermeasure precision coefficient, which is used to evaluate the precision of the unmanned aerial vehicle countermeasure control strategy, specifically including:

[0092] A plurality of countermeasure response data sets of disturbed unmanned aerial vehicles are obtained, and the response data set includes the countermeasure response time and the countermeasure success rate of the unmanned aerial vehicle.

[0093] The countermeasure precision coefficient is calculated by fuzzy logic, which is based on fuzzy set theory and reasoning system. By processing fuzzy input variables including countermeasure response time and countermeasure success rate, a fuzzy output variable is generated, which is denoted as countermeasure precision coefficient. Specifically, it includes:

[0094] The input variable is the countermeasure response time and the countermeasure success rate .

[0095] Wherein, r represents the number of unmanned aerial vehicles.

[0096] The output variable is the countermeasure precision coefficient .

[0097] The countermeasure response time and the countermeasure success rate are converted into fuzzy sets.

[0098] Fuzzy rules are set according to the countermeasure response time and the success rate, which are used to calculate the countermeasure precision coefficient.

[0099] The input fuzzy set is mapped to the output fuzzy set.

[0100] The fuzzy output is converted into a clear countermeasure precision coefficient.

[0101] Wherein, the calculation process of the countermeasure precision coefficient is:

[0102] The countermeasure response time and the countermeasure success rate are converted into fuzzy sets. The countermeasure response time is fuzzified by membership function, and the calculation expression is: ; The countermeasure success rate is fuzzified, and the calculation expression is: ; wherein, represents the membership function of the countermeasure response time, represents the membership function of the countermeasure success rate, represents the mean value of the countermeasure response time, represents the standard deviation of the countermeasure response time.

[0103] According to the membership function of the countermeasure response time and the membership function of the countermeasure success rate, the countermeasure precision coefficient , the expression is calculated as: .

[0104] In S5, according to the evaluation result, the UAV countermeasure control is divided into accurate countermeasure and non-accurate countermeasure, based on the accurate countermeasure, the target information of the UAV, the execution state of the interference strategy and the countermeasure effect are displayed in real time through the operation interface of the wrist-mounted device, and the operator is allowed to manually intervene and adjust the strategy, and a countermeasure effect evaluation report is generated for optimizing the subsequent countermeasure strategy, which specifically includes:

[0105] The countermeasure accuracy coefficient is compared with the preset threshold value, if the countermeasure accuracy coefficient is greater than or equal to the preset threshold value, it means that the corresponding UAV countermeasure control is accurate, recorded as accurate countermeasure, if the countermeasure accuracy coefficient is less than the preset threshold value, it means that the corresponding UAV countermeasure control is not accurate, recorded as non-accurate countermeasure;

[0106] Through the operation interface of the wrist-mounted device, the system monitors and displays the flight state data of the UAV in real time, such as flight speed, direction and height, and compares and analyzes according to the target set flight trajectory;

[0107] The interface displays the current interference strategy, including the frequency, intensity and direction of the interference signal, and displays the execution state of the interference strategy in real time, ensuring that the operator can quickly understand the progress of the system;

[0108] The operation interface also displays the real-time feedback of the countermeasure effect, including whether the UAV control link is interrupted, whether the flight state is deviated, etc.

[0109] On the basis of real-time data display, the operator can manually adjust the interference strategy according to the actual situation, including changing the frequency, intensity and emission direction of the interference signal;

[0110] After the countermeasure process is completed, the system automatically generates a detailed countermeasure effect evaluation report, analyzing the countermeasure response data at each stage, including the countermeasure success rate, response time and interference accuracy.

[0111] Please refer to Figure 2 shown, for a wrist-mounted UAV countermeasure control system, comprising:

[0112] A UAV target identification and locking module, the UAV target identification and locking module fuses multiple sensor data, monitors the UAV in flight in real time and accurately identifies its flight state data, and locks the UAV target;

[0113] A dynamic tracking and flight state judgment module, the dynamic tracking and flight state judgment module dynamically tracks and judges according to the flight state of the UAV and the target set flight trajectory, analyzes whether the UAV deviates from the predetermined flight trajectory, and ensures the accuracy of the flight state;

[0114] A radio frequency interference strategy dynamic adjustment module, which dynamically adjusts the frequency, intensity and emission direction of the radio frequency interference signal based on the real-time flight state and trajectory after confirming that the UAV deviates from the predetermined flight trajectory, forms an interference strategy for the UAV, and interferes with the control link;

[0115] A countermeasure precision coefficient calculation and evaluation module, which collects the countermeasure response time and countermeasure success rate of multiple interfered UAVs, comprehensively calculates the countermeasure precision coefficient, and is used for evaluating the precision of the countermeasure control strategy to ensure the effect of the countermeasure strategy.

[0116] A countermeasure control state monitoring and manual intervention module, which displays the UAV target information, interference strategy execution state and countermeasure effect in real time based on the precise countermeasure using the operation interface of the wrist-mounted device, allows the operator to manually intervene and adjust the strategy, and generates a countermeasure effect evaluation report to optimize the subsequent countermeasure strategy.

[0117] The working principle of the present application is as follows: through multiple sensor data fusion and intelligent algorithms, the UAV in flight is monitored, identified and locked in real time to achieve precise countermeasure control; the flight state data of the UAV, including flight speed, direction and height, are obtained by radar, infrared, optical sensors and GPS, and the particle filtering algorithm is used to process and predict the flight state to ensure accurate identification of the target; based on the actual flight state of the UAV and the target set trajectory, the method determines whether the UAV deviates from the predetermined flight path through dynamic tracking and trajectory deviation analysis, and adjusts the frequency, intensity and emission direction of the radio frequency interference signal according to the deviation to interfere with the control link of the UAV; the countermeasure response time and success rate of multiple interfered UAVs are analyzed comprehensively by fuzzy logic to calculate the countermeasure precision coefficient, thereby evaluating the precision of the control strategy; according to the countermeasure precision coefficient, the system divides the countermeasure control into precise countermeasure and non-precise countermeasure, and displays the UAV target information, interference strategy state and countermeasure effect in real time through the wrist-mounted device, allowing the operator to manually adjust to optimize the countermeasure strategy and ensure effective response to various UAV threats.

[0118] The above formulas are dimensionless values calculated, and the formulas are obtained by software simulation of a large amount of data to obtain a formula closest to the actual situation, and the preset parameters in the formula are set by a person skilled in the art according to the actual situation.

[0119] The above-described embodiments can be implemented in part or in whole through software, hardware, firmware or any combination thereof. When implemented in software, the above-described embodiments can be implemented in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded into a computer, all or part of the processes described in the embodiments of the present application are generated. 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 transferred from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions can be transferred from one website, computer, server or data center to another website, computer, server or data center through wired or wireless (such as infrared, wireless, microwave, etc.) manner. 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, data center, etc. containing one or more available medium collections. The available medium can be a magnetic medium (such as a floppy disk, a hard disk, a magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid state disk.

[0120] It should be understood that the term "and / or" herein merely describes the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B, which can represent three cases of A alone, A and B together, and B alone, where A and B can be singular or plural. In addition, the character " / " herein generally represents that the associated objects before and after it are in an "or" relationship, but it can also represent an "and / or" relationship, which can be understood according to the context before and after it.

[0121] It should be understood that in various embodiments of the present application, the size of the sequence number of the above-mentioned processes does not mean the order of execution, and the execution order of the processes should be determined according to its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0122] The above describes one embodiment of the present application in detail, but the content described is only the preferred embodiment of the present application, and cannot be considered as limiting the scope of the implementation of the present application. Any equivalent changes and improvements made within the scope of the present application are still within the scope of the patent coverage of the present application.

Claims

1. A method for countering and controlling a wrist-worn unmanned aerial vehicle (UAV), characterized in that, Includes the following steps: S1: By fusing data from multiple sensors, including radar, infrared, optical sensors, and GPS, the system monitors and identifies the flight status data of the drone in flight in real time, identifies and locks onto the drone's status, and ensures accurate identification and target locking of the drone in flight; wherein, the flight status data includes: flight speed, flight direction, and flight altitude; The process of identifying and locking the drone's status to ensure accurate identification of drones in flight specifically includes: The flight status data is acquired, processed using particle filtering, and the drone's status is predicted. The specific steps are as follows: Initialize a set of particles, each representing the flight status data of a drone; The next state of each particle is predicted based on the motion model, and the calculation expression is as follows: In the formula, i represents the number of particles, u t Indicates control input, This represents the state transition function of the UAV. w represents the predicted state of the i-th particle at time step t. t The noise level represents the process noise, and t represents the time step. At each time step, particles are weighted according to the current observation data. The weight of each particle is calculated based on the degree of matching between the observed value and the particle's predicted state. The particle weight is updated by calculating the difference between the predicted position and the observed value. The calculation expression is as follows: In the formula, z t This represents the observation data at time step t. Represents a given state At that time, the observed data z t The probability of occurrence; where, The calculation expression is: In the formula, Represent the observation equation, The variance of the observation noise is represented; resampling is performed based on the particle set updated with weights; the state of the UAV at the current moment is calculated by weighted averaging, and the calculation expression is: In the formula, N represents the total number of particles. This indicates the current status of the drone; S2: Based on the drone's status and the target's set flight trajectory, perform dynamic tracking and flight status judgment to determine whether the drone has deviated from the predetermined flight trajectory; S3: After determining that the drone is not following the predetermined flight path, dynamically adjust the frequency, intensity and transmission direction of the radio frequency interference signal according to the drone's real-time flight status and current flight path to form an interference strategy against the drone in order to interfere with the drone's control link. The dynamic adjustment of the frequency, intensity, and transmission direction of the radio frequency interference signal to form an interference strategy targeting the UAV, in order to disrupt the UAV's control link, specifically includes: The strength, frequency, and transmission direction of the radio frequency interference signal are obtained. Based on the obtained strength, frequency, and transmission direction of the radio frequency interference signal, the UAV interference coefficient is calculated. The strength, frequency, and transmission direction of the radio frequency interference signal are then adjusted according to the UAV interference coefficient. The expression for calculating the UAV interference coefficient is as follows: In the formula, γ interf The value represents the interference coefficient of the drone, where α1, α2, and α3 are preset proportional coefficients, and all α1, α2, and α3 are greater than 0. d represents the distance between the drone and the interference source, and v represents the flight speed of the drone. target This represents the velocity at the corresponding position on the target trajectory of the drone, and S represents the deviation of the drone's trajectory. S4: By collecting the countermeasure response time and countermeasure success rate of multiple interfered UAVs, and combining the countermeasure response time and countermeasure success rate, a comprehensive countermeasure accuracy coefficient is calculated to evaluate the accuracy of the UAV countermeasure control strategy. The process of obtaining the countermeasure accuracy coefficient is as follows: Multiple countermeasure response datasets of interfered drones are obtained. The response datasets include: the countermeasure response time and the countermeasure success rate of the drones. The countermeasure accuracy coefficient is calculated by fuzzy logic based on processing fuzzy input variables and their corresponding membership functions. By processing the fuzzy input variables and their corresponding membership functions, a fuzzy output variable is generated, which is denoted as the countermeasure accuracy coefficient. The fuzzy input variables are: countermeasure response time and countermeasure success rate. The calculation of the countermeasure precision coefficient using fuzzy logic specifically includes: the input variable countermeasure response time t. r and reverse conversion power s r Where r represents the number of drones; the output variable is the countermeasure accuracy coefficient θ; and the countermeasure response time t is... r and reverse conversion power s r Convert to a fuzzy set; set fuzzy rules based on countermeasure response time and success rate to calculate countermeasure accuracy coefficients; map the input fuzzy set to the output fuzzy set; convert the fuzzy output into clear countermeasure accuracy coefficients; wherein, the calculation process of the countermeasure accuracy coefficients is as follows: convert the countermeasure response time t... r and reverse conversion power s r Convert to a fuzzy set, calculate the countermeasure response time fuzzification using membership functions, and the calculation expression is: The calculation of the inverse power fuzzification is expressed as follows: In the formula, The membership function representing the countermeasure response time. μ represents the membership function of the inverse production power. s σ represents the mean of the countermeasure response time. s The standard deviation of the countermeasure response time is represented by the standard deviation of ... S5: Based on the evaluation results, the UAV countermeasure control is divided into precision countermeasure and non-precision countermeasure. Based on precision countermeasure, the UAV target information, the execution status of the interference strategy and the countermeasure effect are displayed in real time through the operation interface of the wrist-worn device. The operator is also allowed to manually intervene and adjust the strategy and generate a countermeasure effect evaluation report.

2. The method for countering and controlling a wrist-worn unmanned aerial vehicle according to claim 1, characterized in that, The determination of whether the drone has deviated from the predetermined flight path specifically includes: The system acquires the actual flight status of the drone, determines the actual flight trajectory, acquires the target-set flight trajectory, compares and analyzes the actual flight trajectory of the drone with the target-set flight trajectory, calculates the trajectory deviation coefficient based on the deviation value between the actual flight trajectory and the target-set flight trajectory, and compares the trajectory deviation coefficient with a preset threshold to determine whether the drone has deviated from the predetermined flight trajectory.

3. The method for countering and controlling a wrist-worn unmanned aerial vehicle according to claim 2, characterized in that, The logic for obtaining the trajectory deviation coefficient is as follows: Set target trajectory T target And the actual flight trajectory T actual Each trajectory contains three-dimensional coordinate points for a time step; Calculate the Euclidean distance between each point, representing the difference between the actual trajectory point and the target trajectory point in three-dimensional space; construct a distance matrix to represent the distance between the actual trajectory point and the target trajectory point; calculate the minimum cumulative distance between the two trajectories based on the distance matrix of the two trajectories; and obtain the trajectory deviation coefficient by calculating the ratio of the minimum cumulative distance to the trajectory length.

4. The method for countering and controlling a wrist-worn unmanned aerial vehicle according to claim 1, characterized in that, The assessment of the accuracy of the drone countermeasure control strategy specifically includes: The countermeasure accuracy coefficient is compared with a preset threshold. If the countermeasure accuracy coefficient is greater than or equal to the preset threshold, it means that the corresponding UAV countermeasure control is accurate and is recorded as accurate countermeasure. If the countermeasure accuracy coefficient is less than the preset threshold, it means that the corresponding UAV countermeasure control is inaccurate and is recorded as inaccurate countermeasure.

5. A wrist-worn drone countermeasure control system, characterized in that, The method for countering a wrist-worn drone as described in any one of claims 1-4 includes: The UAV target recognition and locking module integrates data from multiple sensors to monitor UAVs in flight in real time, accurately identify their flight status data, and lock onto the UAV target. The dynamic tracking and flight status judgment module performs dynamic tracking and judgment based on the UAV's flight status and the target's set flight trajectory, analyzes whether the UAV deviates from the predetermined flight trajectory, and ensures the accuracy of the flight status. The radio frequency interference strategy dynamic adjustment module, after confirming that the UAV has deviated from the predetermined flight trajectory, dynamically adjusts the frequency, intensity and transmission direction of the radio frequency interference signal based on the real-time flight status and trajectory to form an interference strategy for the UAV and an interference control link. The countermeasure accuracy coefficient calculation and evaluation module collects the countermeasure response time and countermeasure success rate of multiple interfered UAVs, and comprehensively calculates the countermeasure accuracy coefficient to evaluate the accuracy of the countermeasure control strategy and ensure the effectiveness of the countermeasure strategy. The countermeasure control status monitoring and manual intervention module is based on precise countermeasures. It uses the operation interface of the wrist-worn device to display the UAV target information, the execution status of the interference strategy and the countermeasure effect in real time. It allows the operator to manually intervene and adjust the strategy, and generates a countermeasure effect evaluation report to optimize subsequent countermeasure strategies.

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