Dynamic defense model construction method for unmanned aerial vehicle countering

Abstract

The invention relates to the technical field of unmanned aerial vehicle countering, discloses a dynamic defense model construction method for unmanned aerial vehicle countering, and aims to solve the safety management problem caused by unmanned aerial vehicle application and the limitation of an existing countering technical strategy. State changes of the unmanned aerial vehicle and countering equipment can be accurately presented, and environmental interference can be adaptively realized. By constructing an optimization model, resource allocation is optimized by taking maximization of a countering probability and minimization of resource consumption as targets and combining indexes such as a scientifically quantified countering success probability and the like. An improved genetic algorithm is used for solving a countering strategy, and the algorithm searching capability is enhanced. The method has the beneficial effects of improving countering accuracy and reliability, efficiently utilizing resources, adapting to a complex environment, realizing dynamic optimization of a countering strategy, effectively coping with security threats of the unmanned aerial vehicle, and providing a scientific and efficient solution for countering the unmanned aerial vehicle.

Description

Technical Field

[0001] This invention relates to the field of drone countermeasures technology, specifically to a method for constructing a dynamic defense model for drone countermeasures. Background Technology

[0002] As a new type of aircraft, drones have been widely used in many fields in recent years. In the civilian sector, they are frequently seen in aerial surveying, logistics delivery, agricultural plant protection, and power line inspection. Taking aerial surveying as an example, drones, with their flexibility and maneuverability, can quickly acquire high-resolution images and data, providing strong support for urban planning, topographic mapping, and other tasks. In agricultural plant protection, drones can efficiently spray pesticides and apply fertilizers, significantly improving agricultural production efficiency. In the military field, drones have also demonstrated enormous value, capable of performing critical tasks such as reconnaissance, surveillance, electronic jamming, and even precision strikes, and have become an indispensable combat force in modern warfare.

[0003] However, the widespread use of drones also brings with it a series of significant safety and management issues. In terms of public safety, the malicious use of drones could lead to serious consequences. For example, in airport airspace protection zones, drone intrusion could interfere with the normal takeoff and landing of civil aircraft, posing a major safety hazard; at large events, illegal drone flights could threaten the lives and property of personnel on site. In the field of information security, drones could be used to spy on sensitive areas and steal confidential information, posing risks to national security and corporate trade secrets. Furthermore, in airspace management, the rapid increase in the number of drones has made airspace use increasingly complex, with disorderly flight occurring frequently, posing a significant challenge to traditional airspace management models.

[0004] To address these issues, various drone countermeasures technologies have been developed. Radio frequency jamming is a common method, which works by emitting jamming signals to disrupt the communication link between the drone and its remote controller or its GPS positioning signal, causing the drone to lose control. While this technology can achieve a certain degree of countermeasure effectiveness, it suffers from the difficulty of precisely controlling the interference range, potentially negatively impacting legitimate wireless communication devices in the vicinity. Furthermore, electromagnetic pulse (EMP)-based countermeasures damage the drone's electronic components by generating high-intensity EMPs, but this can also damage other nearby electronic devices. Laser countermeasures utilize high-energy laser beams for hard-kill attacks on drones, but their effectiveness is significantly limited by weather conditions (such as heavy fog or rain), and their effectiveness is greatly reduced in adverse weather conditions.

[0005] Beyond technical issues, existing drone countermeasure strategies also suffer from shortcomings in their formulation. Most strategies are based on experience or simple rules, lacking a comprehensive consideration of the dynamic characteristics of drones and complex environmental factors. In actual countermeasures, due to the variability of drone flight states and the complexity of the environment, these strategies often fail to achieve efficient and precise countermeasures. Furthermore, they fail to adequately consider the optimal allocation of countermeasure resources, potentially leading to resource waste or inadequacy during the countermeasure process, and failing to achieve optimal resource utilization while ensuring countermeasure effectiveness. Summary of the Invention

[0006] The purpose of this invention is to provide a method for constructing a dynamic defense model for countering unmanned aerial vehicles (UAVs) in order to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a method for constructing a dynamic defense model for drone countermeasures, the method comprising:

[0008] Step S1: Collect real-time data of the UAV's flight environment, the UAV's own status data, and relevant data of the countermeasures equipment to determine the parameters required for model calculation; among which, the real-time data of the UAV's flight environment includes at least meteorological data and geographic information data; the UAV's own status data includes at least flight speed, flight altitude, and flight attitude data; the countermeasures equipment data includes at least equipment location, equipment power, and equipment effective countermeasures range data.

[0009] Step S2: Considering the dynamic characteristics of UAV flight and various changing factors in the countermeasure process, establish a UAV countermeasure dynamic defense model based on an adaptive state space model; by predicting the UAV flight trajectory, the response time of the countermeasure equipment, and the time-varying characteristics of the interference effect, establish time-varying state transition equations, observation equations, and control input equations to handle various constraints in dynamic defense.

[0010] Step S3: With the goal of maximizing the effective countermeasure probability against UAVs and minimizing the countermeasure resource consumption, and in combination with the actual operating environment of the UAV countermeasure system, establish a new optimization method and an optimization model of the dynamic defense model to achieve predictive control of the countermeasure strategy.

[0011] Step S4: For the constructed optimization model, the improved genetic algorithm is used to solve it, and finally the countermeasure strategy with the best comprehensive performance is obtained, so as to achieve effective dynamic defense against UAVs.

[0012] Preferably, the specific steps in S2 for establishing an adaptive state-space model include:

[0013] Step S21: Set the assumptions, assuming that the boundaries of the countermeasures area are clear, the flight behavior of the UAV in the countermeasures area follows the basic dynamics principle, and the propagation of the interference signal of the countermeasures equipment is not severely affected by complex terrain.

[0014] Step S22: Establish the time-varying state transition equation for the UAV flight; let... Indicates the drone at a certain time The state vector; Indicates the countermeasures equipment at any time The control input vector; The state transition matrix represents the change of the UAV's own dynamic characteristics over time. The control input matrix represents the influence of the countermeasure device's control input on the UAV's state; therefore, the time-varying state transition equation is: ;

[0015] Step S23: Establish the observation equation, let... Indicates at time The observation vector of the UAV's state. The observation matrix represents the mapping of the UAV's actual state to the observation space; the observation equation is: ,in This represents the observation noise vector, used to describe the uncertainties during the observation process;

[0016] Step S24: Considering the interference factors and environmental changes during the countermeasure process, adaptively adjust the state transition equation and observation equation; by introducing adaptive parameters. For the state transition matrix Control input matrix and observation matrix Real-time updates are performed to adapt to dynamic changes in the drone's flight status and the countermeasure environment; the update formula is: , , .

[0017] Preferably, the specific steps for establishing the optimization model in step S3 include:

[0018] Step S31: Set the assumptions of the model, assuming that each countermeasure device can only countermeasure one drone at a time, that the drone will not exhibit abnormal flight behavior after being subjected to countermeasure interference, and that the total amount of countermeasure resources is limited and can be monitored in real time.

[0019] Step S32: Set the signs of known parameters or sets in the model and the decision variables; This indicates the set of drones that currently require countermeasures. Indicates the first A drone, Indicates countermeasure equipment For drones The probability of a successful counterattack. Indicates countermeasure equipment Resource consumption coefficient, Indicates countermeasure equipment The maximum available resources, Indicates countermeasure equipment For drones The decision variable regarding whether to retaliate. This indicates that countermeasures will be taken. This indicates that no countermeasures will be taken. Indicates the time step;

[0020] Step S33: Construct the objective function, which is a comprehensive objective of maximizing the effective countermeasure probability against UAVs and minimizing the countermeasure resource consumption. The calculation formula is as follows:

[0021]

[0022] in These are weighting coefficients used to balance the relationship between the probability of counterattack and resource consumption;

[0023] Step S34: Establish the constraints for the model:

[0024] Constraint 1: Countermeasure equipment resource constraint, ensuring that the resource consumption of the countermeasure equipment in each countermeasure operation does not exceed its maximum available resource amount, i.e. ;

[0025] Constraint 2: Countermeasure equipment operating status constraint, ensuring that each countermeasure device can only counter one drone at a time, i.e. ;

[0026] Constraint 3: Value constraints for decision variables, decision variables It can only take the value 0 or 1, that is .

[0027] Preferably, the improved genetic algorithm in step S4 includes the following specific steps:

[0028] Step S41: Initialize the population. Based on the decision variable range of the countermeasure strategy, randomly generate a certain number of initial individuals. Each individual represents a combination of countermeasure strategies to form the initial population.

[0029] Step S42: Calculate the fitness value. Based on the objective function constructed in step S3, calculate the fitness value of each individual. The higher the fitness value, the better the countermeasure strategy.

[0030] Step S43: Using the roulette wheel selection method, a certain number of individuals are selected from the current population to enter the next generation population based on their fitness values. Individuals with higher fitness values ​​have a greater probability of being selected.

[0031] Step S44: Perform a crossover operation on the selected individuals with a certain crossover probability. Two individuals are randomly selected, and some of their genes are exchanged to generate new individuals, increasing the diversity of the population;

[0032] Step S45: With a certain mutation probability Mutation operations are performed on newly generated individuals to randomly change some gene values ​​of the individuals, preventing the algorithm from getting trapped in local optima.

[0033] Step S46: Repeat steps S42-S45 until the preset termination condition is met. At this point, the individual with the highest fitness value in the population is the optimal countermeasure strategy.

[0034] Preferably, the adaptive parameters in step S24 The update methods include:

[0035] Error in UAV state estimation based on Kalman filter algorithm Perform calculations. ,in This is an estimate of the drone's state;

[0036] Based on state estimation error Calculate adaptive parameters based on preset adaptive rules. .

[0037] Preferably, the success rate of countermeasures in step S33 is... The calculation method is as follows:

[0038] Considering the power of countermeasures equipment Distance from the drone The degree of matching between the interference signal frequency and the UAV's receiving frequency Factors affecting the probability of successful countermeasures The calculation formula is:

[0039]

[0040] in This is the influence coefficient.

[0041] Preferably, in step S44, a multi-point crossover method is adopted, specifically: multiple crossover points are randomly generated, and the genes of two parent individuals are exchanged at the crossover points to generate two offspring individuals.

[0042] Preferably, in step S45, an adaptive mutation probability is used, specifically as follows:

[0043] With the number of iterations The increase in mutation probability Gradually decrease, the calculation formula is: ,in The initial mutation probability, This represents the maximum number of iterations.

[0044] Preferably, the present invention further includes an electronic device, comprising:

[0045] Processor; memory used to store processor-executable instructions;

[0046] The processor is configured to invoke instructions stored in the memory to execute the aforementioned method for constructing a dynamic defense model for drone countermeasures.

[0047] Preferably, the present invention further includes a computer-readable storage medium storing computer program instructions thereon, which, when executed by a processor, implement the above-described method for constructing a dynamic defense model for UAV countermeasures.

[0048] Compared with the prior art, the beneficial effects of the present invention are:

[0049] A dynamic defense model is constructed based on an adaptive state-space model, fully considering the dynamic changes in speed, altitude, and attitude of UAVs during flight, as well as factors such as the response time of countermeasures equipment and the time-varying effects of interference. By establishing precise time-varying state transition equations, observation equations, and control input equations, compared with traditional simple fixed models, it can more accurately simulate the state changes of UAVs and countermeasures equipment, providing a solid and reliable basis for the formulation of countermeasure strategies, greatly improving the accuracy and timeliness of countermeasure actions, and effectively responding to the complex and ever-changing flight states of UAVs.

[0050] During model construction, adaptive parameters are introduced to update the state transition matrix, control input matrix, and observation matrix in real time, addressing interference factors and environmental changes during the countermeasure process. The Kalman filter algorithm is used to accurately calculate the UAV state estimation error, enabling adaptive model adjustment. This allows the model to maintain high accuracy and reliability even in complex and changing environments, such as those with multiple interference sources or varying weather conditions, significantly outperforming traditional models lacking adaptive capabilities.

[0051] An optimization model is established with the goal of maximizing the effective countermeasure probability and minimizing countermeasure resource consumption, comprehensively considering practical constraints such as limited countermeasure equipment resources and the fact that only one drone can be countered at a time. Unlike traditional strategies that focus solely on either the countermeasure effect or resource consumption, this invention achieves an optimal balance between the two, realizing efficient resource utilization. For example, when the total amount of countermeasure resources is limited, priority is given to countering key drones, avoiding resource waste and ensuring the best countermeasure effect with limited resources.

[0052] By scientifically calculating the probability of successful countermeasures and considering factors such as the power of countermeasure equipment, distance to the drone, and frequency matching degree of interference signals, precise quantitative indicators are provided for formulating countermeasure strategies. Based on these indicators, the correspondence between countermeasure equipment and drones can be rationally arranged, prioritizing combinations with high probability of successful countermeasures and low resource consumption, achieving precise allocation of countermeasure resources, and further improving resource utilization efficiency.

[0053] The establishment of an adaptive state-space model and the real-time updating of the correlation matrix enable the model to quickly adapt to the needs of UAV countermeasures under different environmental conditions. Whether in complex terrain with high-rise buildings in cities or in environments with severe weather conditions, the model can maintain an accurate description of the state of UAVs and countermeasure equipment by adjusting its own parameters, thereby providing reliable support for countermeasure strategies and enhancing the adaptability of the countermeasure system in different environments.

[0054] The improved genetic algorithm employs strategies such as multi-point crossover and adaptive mutation probability, increasing its search capability in the solution space. In complex environments, multi-point crossover can integrate the advantageous genes of more parent individuals, generating more diverse offspring and expanding the search range; adaptive mutation probability can explore new solution spaces with a high probability in the early stages and converge stably in the later stages, ensuring that the algorithm can quickly find the globally optimal countermeasure strategy in complex and ever-changing environments, effectively coping with the challenges brought by environmental changes.

[0055] An improved genetic algorithm is used to solve countermeasure strategies. Compared with traditional strategies based on experience or simple rules, it can search a wider strategy space to find the countermeasure strategy with optimal overall performance. Through multiple iterations and optimizations, the decision variables of the countermeasure equipment, such as the selection of drones and the timing of countermeasures, are continuously adjusted to achieve efficient dynamic defense against drones, improve the success rate of countermeasures, and reduce the probability of missed detections and false alarms.

[0056] During the countermeasure process, the model and algorithm can update the countermeasure strategy in a timely manner as the drone's status and environmental factors change in real time. For example, when a new drone enters the countermeasure area or an existing drone changes its flight trajectory, the system can quickly recalculate and adjust the countermeasure strategy to ensure that the best countermeasure effect is always maintained and to adapt to the needs of dynamically changing countermeasure scenarios. Attached Figure Description

[0057] Figure 1 This is a schematic diagram illustrating the working principle of the dynamic defense model construction method described in this invention.

[0058] Figure 2 This is a flowchart illustrating the construction process based on the adaptive state-space model.

[0059] Figure 3 To optimize the model construction flowchart;

[0060] Figure 4 A diagram illustrating the implementation steps of the improved genetic algorithm. Detailed Implementation

[0061] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0062] Please see Figure 1-4 This invention provides a method for constructing a dynamic defense model for drone countermeasures. The aim is to achieve efficient countermeasures against drones while reducing the consumption of countermeasure resources by constructing a dynamic defense model and optimizing countermeasure strategies. The overall implementation scheme is as follows:

[0063] Step S1: Data Collection and Model Parameter Determination: Collect real-time data on the UAV's flight environment, the UAV's own status data, and relevant data from countermeasures devices using various sensors and data acquisition devices. Utilize meteorological monitoring stations and satellite remote sensing technology to acquire meteorological data such as wind speed, wind direction, temperature, and air pressure; employ Geographic Information Systems (GIS) to acquire geographic information data, including terrain, landforms, and building distribution. Collect flight speed, altitude, and attitude data using the UAV's onboard sensors, such as gyroscopes, accelerometers, and GPS modules. For countermeasures device data, obtain the device's location using its own positioning device, and acquire device power and effective countermeasures range data from the device's parameter manual or real-time monitoring. Organize and analyze this data to determine the parameters required for model calculation.

[0064] Step S2: Establish a dynamic defense model based on an adaptive state-space model: Considering the dynamic characteristics of UAV flight and various changing factors in the countermeasure process, construct a UAV countermeasure dynamic defense model based on an adaptive state-space model. Establish time-varying state transition equations, observation equations, and control input equations to handle various constraints in dynamic defense. By analyzing and modeling the prediction of UAV flight trajectory, the response time of countermeasure equipment, and the time-varying characteristics of interference effects, ensure that the model can accurately reflect the state changes of UAVs and countermeasure equipment at different times.

[0065] Step S3: Establish an optimization model for predictive control: With the goal of maximizing the effective countermeasure probability against UAVs and minimizing countermeasure resource consumption, a new optimization method and an optimization model for the dynamic defense model are established, taking into account the actual operating environment of the UAV countermeasure system. In practical applications, different combinations of countermeasure equipment and UAVs will produce different countermeasure effects and resource consumption. Through the optimization model, the optimal countermeasure strategy can be found, achieving predictive control of the countermeasure strategy.

[0066] Step S4: Solve the countermeasure strategy using an improved genetic algorithm: For the constructed optimization model, an improved genetic algorithm is used to solve the problem. Through operations such as initializing the population, calculating fitness values, selection, crossover, and mutation, continuous iterative optimization is performed to finally obtain the countermeasure strategy with optimal overall performance, achieving effective dynamic defense against drones. In real-world scenarios, different initial population, crossover probability, and mutation probability settings will affect the algorithm's convergence speed and final result, and adjustments need to be made according to specific circumstances.

[0067] The implementation of the present invention will be further described below with reference to Examples 1 to 5.

[0068] Example 1:

[0069] This embodiment details the process of establishing an adaptive state-space model. Through reasonable assumptions, equation establishment, and adaptive adjustments, the model can more accurately simulate the dynamic changes during UAV flight and countermeasures. Specifically, it includes:

[0070] Step S21: Set Assumptions: Assume the countermeasures zone has clear boundaries. This is achieved by setting up clear markers around the countermeasures zone or using electronic fence technology to define the boundaries, making it easier to determine whether a drone has entered the countermeasures zone. Assume the drone's flight behavior within the countermeasures zone follows basic dynamic principles. In practical applications, most normally flying drones exhibit flight mechanics characteristics that conform to basic dynamic principles such as Newton's laws of motion. This assumption provides the theoretical basis for subsequent model building. Assume the propagation of the jamming signal from the countermeasures equipment is not significantly affected by complex terrain. When selecting deployment locations for the countermeasures equipment, choose open areas with simple terrain, or use signal enhancement and compensation techniques to reduce the impact of terrain on the propagation of the jamming signal.

[0071] Step S22: Establish the time-varying state transition equation for UAV flight: Let Indicates the drone at a certain time The state vector, for example, at a certain moment, the state vector of the UAV can be represented as: ,in , respectively drones in , Flight speed in direction, For flight altitude, Let be the flight attitude angle. Indicates the countermeasures equipment at any time The control input vector can be constructed from parameters such as the power and frequency of the jamming signal emitted by the countermeasure device. Let... This represents the state transition matrix, whose elements are determined based on the changes in the UAV's own dynamic characteristics over time. Assuming the UAV's horizontal flight speed is affected by air resistance, its dynamic equation is: ( For the quality of drones, (where the air drag coefficient is the value), after discretization, the state transition matrix is... The element corresponding to the change in horizontal velocity can be represented as ,in Let be the time interval. This represents the control input matrix, for example, when a countermeasure device alters the flight attitude of a drone by emitting jamming signals. The elements corresponding to attitude angle changes can be determined based on the degree of influence of the disturbance signal on the attitude angle. Therefore, the time-varying state transition equation is: .

[0072] Step S23: Establish the observation equation: Let Indicates at time In practical applications, the observation vector of the UAV's state can be obtained through devices such as radar and photoelectric sensors, for example... The values ​​marked with the subscript "obs" represent the observed values. Let... This represents the observation matrix, used to map the actual state of the UAV to the observation space. Assuming the observation equipment has a certain scaling error when measuring the UAV's horizontal velocity, the observation matrix... The element corresponding to the horizontal velocity can be represented as ,in This represents the proportion of the horizontal velocity measurement error. The observation equation is: ,in This represents the observation noise vector, which can be obtained through statistical analysis of multiple measurements from the observation equipment. For example, the covariance matrix of the noise vector can be determined by calculating the standard deviation of the measurement data.

[0073] Step S24: Adaptively adjust the state transition equation and observation equation: Considering the disturbance factors and environmental changes in the countermeasure process, adaptive parameters are introduced. For the state transition matrix Control input matrix and observation matrix Real-time updates are performed. The state estimation error of the UAV is assessed based on the Kalman filter algorithm. Perform calculations. ,in This is an estimate of the UAV's state. The Kalman filter algorithm continuously optimizes the estimated UAV state through two steps: prediction and update. In the prediction step, the state at the current moment is predicted based on the state estimate from the previous moment and the state transition equation. In the update step, the predicted values ​​are corrected based on the observed values ​​and the observation equation, i.e. , ,in To predict the error covariance, To observe the noise covariance, This is the Kalman gain. Based on the state estimation error... Calculate adaptive parameters based on preset adaptive rules. For example, the preset adaptive rule is: ,in These are the adaptive coefficients. Then, the matrix is ​​updated based on the adaptive parameters, using the following formula: Assuming (where is the identity matrix) (for adjustment factors), then ; Assumption ,but ; Assuming ,but .

[0074] Example 2:

[0075] This embodiment details the process of establishing the optimization model. By reasonably setting assumptions, defining parameters and variables, constructing the objective function, and establishing constraints, it provides an effective mathematical model for achieving the goals of maximizing the countermeasure probability and minimizing resource consumption. This gives the optimization of the countermeasure strategy a clear direction and basis, specifically including:

[0076] Step S31: Set the assumptions of the model: Assume that each countermeasure device can only counter one drone at a time. This is based on the technical implementation of most current countermeasure devices, such as common radio frequency jamming countermeasure devices, which can only transmit jamming signals of a specific frequency targeting one drone at a time. Assume that the drone will not exhibit abnormal flight behavior leading to unpredictable situations after being subjected to countermeasures. In practical applications, most normally designed drones, when subjected to reasonable countermeasures, still follow certain patterns in their flight behavior, such as changes in flight attitude or speed, without sudden loss of control or unpredictable flight trajectories. Assume that the total amount of countermeasure resources is limited and can be monitored in real time. By installing power monitoring modules and signal transmission power monitoring modules on the countermeasure devices, the resource consumption of the countermeasure devices can be obtained in real time. At the same time, an upper limit on the total amount of countermeasure resources is set, such as limited power of the countermeasure devices or limited consumables for transmitting jamming signals.

[0077] Step S32: Set the signs of known parameters or sets in the model and the decision variables: Let This represents the set of drones that currently require countermeasures. In a specific countermeasure scenario, assuming radar detects that three drones have entered the countermeasure zone, then... .set up Indicates the first A drone, for example This represents the first drone detected. Let... Indicates countermeasure equipment For drones The probability of a successful countermeasure will be calculated in detail later. Let... Indicates countermeasure equipment The resource consumption coefficient, for example, if a countermeasure device consumes 0.1 kWh of electricity to transmit an interference signal, and if electricity consumption is used as a resource metric, and the duration of each countermeasure operation is the same, then its resource consumption coefficient is... .set up Indicates countermeasure equipment The maximum available resource quantity, such as the power limit of the aforementioned countermeasure equipment being 1 kWh, then .set up Indicates countermeasure equipment For drones The decision variable regarding whether to retaliate. This indicates that countermeasures will be taken. This indicates that no countermeasures will be taken. Let... This represents the time step. In practical applications, the time step is determined based on the response speed and data update frequency of the countermeasure system. For example, if the countermeasure system updates data every second, then... Second.

[0078] Step S33: Construct the objective function: The objective function is a comprehensive objective that maximizes the effective countermeasure probability against UAVs and minimizes the countermeasure resource consumption. The calculation formula is as follows:

[0079]

[0080] in These are weighting coefficients used to balance the relationship between countermeasure probability and resource consumption. In practical applications, the values ​​of these weighting coefficients can be determined through multiple trials and data analysis. For example, in a specific experimental scenario, the following settings can be configured: , , Conduct multiple countermeasure simulations and select the optimal one based on the countermeasure effectiveness and resource consumption. value.

[0081] Step S34: Establish the constraints of the model

[0082] Constraint 1: Countermeasure Equipment Resource Constraint: Ensure that the resource consumption of the countermeasure equipment in each countermeasure operation does not exceed its maximum available resource amount, i.e. Suppose at a certain moment, there are 3 drones. , , Countermeasures equipment resource consumption coefficient Maximum available resources .like , , ,but If the constraints are not met, it means that the countermeasures equipment... We cannot counter these three drones simultaneously; we need to adjust our countermeasures strategy.

[0083] Constraint 2: Countermeasure Equipment Operating Status Constraint: Ensure that each countermeasure device can only counter one drone at a time, i.e. For example, at the same time, countermeasures equipment For drones To take countermeasures, that is ,So , The decision variables for other drones must be zero to prevent countermeasures from simultaneously sending jamming signals to multiple targets, resulting in poor jamming effectiveness.

[0084] Constraint 3: Value Constraints for Decision Variables It can only take the value 0 or 1, that is This is because the countermeasure device either performs a countermeasure operation (value 1) or does not perform a countermeasure (value 0) against a certain drone, and there are no other intermediate states, which ensures the clarity and operability of the decision.

[0085] Example 3:

[0086] This embodiment details the calculation method for the success probability of countermeasures, comprehensively considering multiple influencing factors to provide accurate quantitative indicators for optimizing the model, making the formulation of countermeasure strategies more scientific and precise. Specifically, it includes:

[0087] In step S33, the probability of successful countermeasure is... The calculation comprehensively considers the power of the countermeasures equipment. Distance from the drone The degree of matching between the interference signal frequency and the UAV's receiving frequency Factors such as these are considered; the calculation formula is as follows:

[0088]

[0089] in These are the influence coefficients. These influence coefficients are obtained through fitting a large amount of experimental data. For example, in a dedicated drone countermeasure testing ground, countermeasure devices with different power levels are set up, their distance from the drone is varied, and the matching degree between the interference signal frequency and the drone's receiving frequency is adjusted, conducting multiple countermeasure experiments. The countermeasure results (success or failure) of each experiment are recorded, and the values ​​of the influence coefficients are determined using nonlinear regression analysis.

[0090] Suppose that in a real-world countermeasure scenario, the countermeasure equipment... power For 80W, with drones distance The matching degree between the interference signal frequency and the UAV's receiving frequency is 800m. The value is 0.7. This influence coefficient was determined through extensive preliminary experiments. , , Substituting these values ​​into the formula yields:

[0091]

[0092] This calculation method can quantify the probability of successful countermeasures against different drones, providing a crucial basis for formulating subsequent countermeasure strategies. For example, in complex scenarios involving multiple countermeasure devices and multiple drones, based on the probability of successful countermeasures, priority can be given to operating countermeasure devices with a high probability of success against high-value target drones, thereby improving the overall countermeasure effectiveness.

[0093] Example 4:

[0094] This embodiment details the specific steps of improving the genetic algorithm by using a multi-point crossover method. By increasing the number of crossover points, the diversity of the population's genes is promoted, enabling the algorithm to explore the solution space more comprehensively during the search for the optimal solution, avoiding getting trapped in local optima, and improving its ability to find the global optimum countermeasure strategy. Specifically, it includes:

[0095] In step S44, a multi-point crossover method is used. The specific steps are as follows: First, multiple crossover points are randomly generated. Assuming the gene coding length of the current counter-strategy individual is 30 bits, four crossover points are randomly generated in one crossover operation, namely the 6th, 12th, 18th, and 24th positions. Then, the genes of the two parent individuals are exchanged at the crossover points to generate two offspring individuals.

[0096] For example, consider parent individual A: 110010 011011 100101 011011 001010, parent individual B: 001101100100 011010 100100 110111. After exchanging the intersections as described above, we obtain child individual C: 110010100100 011010 011011 110111, and child individual D: 001101 011011 100101 100100 001010.

[0097] Through multi-point crossover, offspring individuals incorporate gene fragments from different locations in their parents, increasing population diversity. During multiple iterations, different crossover point choices generate diverse offspring individuals, allowing the algorithm to explore a wider solution space. Compared to single-point crossover, multi-point crossover makes fuller use of information from parents, improving the algorithm's search efficiency and optimization capabilities. For example, in handling complex drone countermeasure scenarios, single-point crossover may fail to effectively integrate the advantageous genes of different parents, leading to slow convergence or getting stuck in local optima. Multi-point crossover, however, can combine genes over a larger scope, making it more likely to find the globally optimal combination of countermeasure strategies.

[0098] Example 5:

[0099] This embodiment illustrates the use of adaptive mutation probability in the improved genetic algorithm. By adjusting the mutation probability with the number of iterations, sufficient exploration capability is ensured in the early stage of the algorithm, and stable convergence is achieved in the later stage, thereby improving the overall performance and solution accuracy of the algorithm.

[0100] In step S45, an adaptive mutation probability is used. As the iteration number t increases, the mutation probability... Gradually decrease, the calculation formula is:

[0101] ,in The initial mutation probability, This represents the maximum number of iterations.

[0102] Assuming initial mutation probability Maximum number of iterations In the early stages of iteration, such as When the mutation probability is:

[0103]

[0104] In the later stages of iteration, such as When the mutation probability is:

[0105]

[0106] In practical applications, the initial mutation probability and maximum number of iterations should be set appropriately based on the complexity and scale of the countermeasure problem. For complex countermeasure scenarios, a larger maximum number of iterations and a relatively high initial mutation probability may be needed to ensure that the algorithm has sufficient time and capability to explore the solution space; for simple scenarios, these parameters can be appropriately reduced to improve the algorithm's running efficiency. Through this adaptive mutation probability approach, the algorithm can adjust the degree of mutation according to the actual situation at different stages.

[0107] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0108] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for constructing a dynamic defense model for drone countermeasures, characterized in that, The method includes: Step S1: Collect real-time data of the UAV's flight environment, the UAV's own status data, and relevant data of the countermeasures equipment to determine the parameters required for model calculation; among which, the real-time data of the UAV's flight environment includes at least meteorological data and geographic information data; the UAV's own status data includes at least flight speed, flight altitude, and flight attitude data; the countermeasures equipment data includes at least equipment location, equipment power, and equipment effective countermeasure range data. Step S2: Considering the dynamic characteristics of UAV flight and various changing factors in the countermeasure process, establish a UAV countermeasure dynamic defense model based on an adaptive state space model; by predicting the UAV flight trajectory, the response time of the countermeasure equipment, and the time-varying characteristics of the interference effect, establish time-varying state transition equations, observation equations, and control input equations to handle various constraints in dynamic defense. Step S3: With the goal of maximizing the effective countermeasure probability against UAVs and minimizing the countermeasure resource consumption, and in combination with the actual operating environment of the UAV countermeasure system, establish a new optimization method and an optimization model of the dynamic defense model to achieve predictive control of the countermeasure strategy. Step S4: For the constructed optimization model, the improved genetic algorithm is used to solve it, and finally the countermeasure strategy with the best comprehensive performance is obtained, so as to achieve effective dynamic defense against UAVs.

2. The method for constructing a dynamic defense model for drone countermeasures according to claim 1, characterized in that, The specific steps for establishing the adaptive state-space model in S2 include: Step S21: Set the assumptions, assuming that the boundaries of the countermeasures area are clear, the flight behavior of the UAV in the countermeasures area follows the basic dynamics principle, and the propagation of the interference signal of the countermeasures equipment is not severely affected by complex terrain. Step S22: Establish the time-varying state transition equation for the UAV flight; let... Indicates the drone at a certain time The state vector; Indicates the countermeasures equipment at any time The control input vector; The state transition matrix represents the change of the UAV's own dynamic characteristics over time. The control input matrix represents the influence of the countermeasure device's control input on the UAV's state; therefore, the time-varying state transition equation is: ; Step S23: Establish the observation equation, let... Indicates at time The observation vector of the UAV's state. The observation matrix represents the mapping of the UAV's actual state to the observation space; the observation equation is: ,in This represents the observation noise vector, used to describe the uncertainties during the observation process; Step S24: Considering the interference factors and environmental changes during the countermeasure process, adaptively adjust the state transition equation and observation equation; by introducing adaptive parameters. For the state transition matrix Control input matrix and observation matrix Real-time updates are performed to adapt to dynamic changes in the drone's flight status and the countermeasure environment; the update formula is: , , .

3. The method for constructing a dynamic defense model for drone countermeasures according to claim 1, characterized in that, The specific steps for establishing the optimization model in step S3 include: Step S31: Set the assumptions of the model, assuming that each countermeasure device can only countermeasure one drone at a time, that the drone will not exhibit abnormal flight behavior after being subjected to countermeasure interference, and that the total amount of countermeasure resources is limited and can be monitored in real time. Step S32: Set the signs of known parameters or sets in the model and the decision variables; This indicates the set of drones that currently require countermeasures. Indicates the first A drone, Indicates countermeasure equipment For drones The probability of a successful counterattack. Indicates countermeasure equipment Resource consumption coefficient, Indicates countermeasure equipment The maximum available resources, Indicates countermeasure equipment For drones The decision variable regarding whether to retaliate. This indicates that countermeasures will be taken. This indicates that no countermeasures will be taken. Indicates the time step; Step S33: Construct the objective function, which is a comprehensive objective of maximizing the effective countermeasure probability against UAVs and minimizing the countermeasure resource consumption. The calculation formula is as follows: in These are weighting coefficients used to balance the relationship between the probability of counterattack and resource consumption; Step S34: Establish the constraints for the model: Constraint 1: Countermeasure equipment resource constraint, ensuring that the resource consumption of the countermeasure equipment in each countermeasure operation does not exceed its maximum available resource amount, i.e. ; Constraint 2: Countermeasure equipment operating status constraint, ensuring that each countermeasure device can only counter one drone at a time, i.e. ; Constraint 3: Value constraints for decision variables, decision variables It can only take the value 0 or 1, that is .

4. The method for constructing a dynamic defense model for drone countermeasures according to claim 1, characterized in that, The specific steps of the improved genetic algorithm in step S4 include: Step S41: Initialize the population. Based on the decision variable range of the countermeasure strategy, randomly generate a certain number of initial individuals. Each individual represents a combination of countermeasure strategies to form the initial population. Step S42: Calculate the fitness value. Based on the objective function constructed in step S3, calculate the fitness value of each individual. The higher the fitness value, the better the countermeasure strategy. Step S43: Using the roulette wheel selection method, a certain number of individuals are selected from the current population to enter the next generation population based on their fitness values. Individuals with higher fitness values ​​have a greater probability of being selected. Step S44: Perform a crossover operation on the selected individuals with a certain crossover probability. Two individuals are randomly selected, and some of their genes are exchanged to generate new individuals, increasing the diversity of the population; Step S45: With a certain mutation probability Mutation operations are performed on newly generated individuals to randomly change some gene values ​​of the individuals, preventing the algorithm from getting trapped in local optima. Step S46: Repeat steps S42-S45 until the preset termination condition is met. At this point, the individual with the highest fitness value in the population is the optimal countermeasure strategy.

5. The method for constructing a dynamic defense model for drone countermeasures according to claim 2, characterized in that, Adaptive parameters in step S24 The update methods include: Error in UAV state estimation based on Kalman filter algorithm Perform calculations. ,in This is an estimate of the drone's state; Based on state estimation error Calculate adaptive parameters based on preset adaptive rules. .

6. The method for constructing a dynamic defense model for drone countermeasures according to claim 3, characterized in that, Successful countermeasure probability in step S33 The calculation method is as follows: Considering the power of countermeasures equipment Distance from the drone The degree of matching between the interference signal frequency and the UAV's receiving frequency Factors affecting the probability of successful countermeasures The calculation formula is: in This is the influence coefficient.

7. The method for constructing a dynamic defense model for drone countermeasures according to claim 4, characterized in that, In step S44, a multi-point crossover method is adopted. The specific steps are as follows: multiple crossover points are randomly generated, and the genes of the two parent individuals are exchanged at the crossover points to generate two offspring individuals.

8. The method for constructing a dynamic defense model for drone countermeasures according to claim 4, characterized in that, In step S45, an adaptive mutation probability is used, specifically as follows: With the number of iterations The increase in mutation probability Gradually decrease, the calculation formula is: ,in The initial mutation probability, This represents the maximum number of iterations.

9. An electronic device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to invoke instructions stored in the memory to execute the method according to any one of claims 1 to 8.

10. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, When the computer program instructions are executed by the processor, they implement the method described in any one of claims 1 to 8.

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