Unmanned aerial vehicle path deception jamming method based on intelligent optimization algorithm

Abstract

The invention discloses an unmanned aerial vehicle path deception jamming method based on an intelligent optimization algorithm, belongs to the technical field of unmanned aerial vehicle path deception jamming, is used for unmanned aerial vehicle path deception jamming, and comprises the steps of establishing an unmanned aerial vehicle kinetic equation and a kinematics equation, and obtaining an expected flight trajectory of an unmanned aerial vehicle; a particle filtering method based on firefly algorithm optimization is introduced, and the state of the unmanned aerial vehicle is tracked and estimated; an improved particle swarm optimization algorithm is introduced to calculate the public delay amount in the forwarding interference, and the positioning information of the unmanned aerial vehicle is interfered and guided. Compared with the prior art, the real flight process and maneuvering change of the unmanned aerial vehicle are simulated, and the attitude change and dynamic response of the unmanned aerial vehicle are reflected; the problems of particle degradation and sample depletion are relieved, and the tracking stability and precision under noisy observation are improved; the deception positioning precision and the system robustness are improved, and the overall deception jamming effect is enhanced; and a technical support is provided for improving the deception jamming capability of a tail end defense system.

Description

TECHNICAL FIELD

[0001] The application discloses an unmanned aerial vehicle path deception jamming method based on an intelligent optimization algorithm and belongs to the technical field of unmanned aerial vehicle path deception jamming. BACKGROUND

[0002] The core of target tracking technology lies in effective estimation of target states, wherein, as a classical method for optimally estimating states of a linear Gaussian system, Kalman Filter (KF) has been widely applied to information fusion and tracking problems of multi-source sensors such as radars and infrareds due to its minimum mean square error criterion and recursive calculation capability. However, in actual battlefield environments, target movement usually has nonlinear and variable characteristics, and is affected by external interference and sensor measurement errors, so that the traditional KF method has obvious deficiencies in modeling capability and robustness, and is difficult to meet the accurate tracking requirements of modern fire control systems for complex dynamic targets.

[0003] With the wide application of satellite navigation technology, the global satellite navigation positioning system has become an indispensable positioning means in modern civilian and military systems. However, in a complex electromagnetic environment or a confrontation scene, satellite signals are easily affected by deception jamming, especially the low-technology threshold and strong concealment of the Meaconing deception jamming, which has attracted widespread attention. This jamming method receives real satellite signals and delays the forwarding, so that the unmanned aerial vehicle cannot distinguish the original and fake signals, thereby misjudging the propagation time, causing the pseudo-range calculation deviation and generating false positioning information. In the actual flight process, due to the noise in the radar observation data, directly using the original measurement trajectory for processing will cause the target state estimation accuracy to be reduced, and affect the implementation of the subsequent path planning and deception jamming strategy. The traditional particle filter method often faces the problems of particle degeneration and sample impoverishment when dealing with nonlinear and non-Gaussian systems, resulting in unstable filtering effect. In addition, in the process of deception jamming, the delay time of the forwarding device is difficult to accurately calculate due to the inability to obtain the real position information of the unmanned aerial vehicle, which affects the accuracy of the deception positioning. In the path jamming strategy, the current algorithm generally has weak global search ability and is easy to fall into local optimum, and it is difficult to generate a deception path with sufficient deception and jamming effect. The Meaconing method does not need to reconstruct the navigation signal, has the advantages of simple implementation and significant jamming effect, and is suitable for the demand of attracting and path deception of target position. SUMMARY

[0004] The application aims to provide an unmanned aerial vehicle path deception jamming method based on an intelligent optimization algorithm to solve the problems of weak global search ability, easy to fall into local optimum and poor jamming effect of the unmanned aerial vehicle path deception jamming algorithm in the prior art.

[0005] An unmanned aerial vehicle path deception jamming method based on an intelligent optimization algorithm comprises: S1 establishes the dynamics equation and kinematics equation of the unmanned aerial vehicle to obtain the expected flight trajectory of the unmanned aerial vehicle; S2 introduces the improved particle swarm optimization algorithm to track and estimate the state of the unmanned aerial vehicle; S3 introduces the particle filtering method based on the glowworm swarm optimization algorithm to calculate the public time delay in the relay interference and to interfere and guide the positioning information of the unmanned aerial vehicle.

[0006] S1 includes S1.1, the dynamics of the unmanned aerial vehicle is analyzed as a rigid body, and the mass center dynamics equation of the unmanned aerial vehicle in the body coordinate system is: ; In the formula, 、 and are the roll angular velocity, the yaw angular velocity and the pitch angular velocity respectively, 、 and are the velocity components of the velocity vector in the axis, axis and axis respectively, is the engine thrust, 、 and are the force drag, lift and side force in three directions decomposed in the airflow coordinate system, is the angle between the thrust vector and the longitudinal axis of the body, is the mass of the unmanned aerial vehicle, is the acceleration of gravity, is the time, is the pitch angle, is the roll angle, is the angle of attack, is the sideslip angle; S1.2, the dynamics equation of the unmanned aerial vehicle rotating around the mass center in the body coordinate system is: ; ; In the formula, 、 、 are the moments of inertia, is the inertia product, is the variable to be solved, 、 and are the components of the external moment acting on the unmanned aerial vehicle in the axis, axis and axis in the body coordinate system.

[0007] S1 includes S1.3 to establish the relationship between the speed components of the unmanned aerial vehicle in the ground coordinate system: ; In the formula, , and are the three-dimensional coordinates of the unmanned aerial vehicle in the axis, axis and axis, , , , , and are the three-dimensional speed of the unmanned aerial vehicle in the axis, axis and axis, is the yaw angle; S1.4 establishes the relationship between the angular velocity components of the unmanned aerial vehicle in the body coordinate system, which is equivalent to the kinematic equation of the unmanned aerial vehicle rotating around the center of mass: ; In the formula, , are the derivatives of the roll angular velocity, yaw angular velocity and pitch angular velocity, respectively, and the state quantity of the unmanned aerial vehicle is , is the control input.

[0008] S2 includes S2.1 to add observation errors to the position information matrix at time as the observation value of the state transition process : = ; In the formula, , , , , and represent the added observation error coefficients, represents a random number with a mean of 0 and a variance of 1; Calculate the particle filter; At the initial time, sample the system state to generate an initial particle set, initialize the particles through the prior distribution of the system , and assign equal initial weights to each particle: ; In the formula, is the state of the th particle at the initial time, is the total number of particles, is the initial state, is the initial time, is the weight of the i-th particle at time t0; At time t, each particle is sampled according to the state transition model, and the particle distribution at time t0is transferred to the current time t: ; where, is the state of the i-th particle at time t0, is the particle distribution at time t0is transferred to the prior distribution at time t: is the state value at time t0; The conditional posterior probability density of the state represented by the weighted particles is obtained: ; where, is the observation value at time t0, is the weight of the i-th particle at time t0, is the Dirac function; The weight of the particle is adjusted using the current observation data: ; where, is the proportional symbol, is the probability of the observation data at time t0when the particle state ; The updated particle weight is normalized: ; where, is the normalized weight, is the i-th particle; The state estimation value of the target is the weighted average value of the current state distribution: ; where, is the state estimation value at time t0; Finally, the error covariance is calculated: .​​​​​​​​​​​​​​​​

[0009] S2 includes S2.2 calculating the attraction value between each particle and the global optimal firefly individual, and updating the position of the particle; Introducing particle filtering into the brightness calculation of the firefly algorithm ,The higher the brightness value, the better the spatial position, the greater the weight of the corresponding particle, and the closer it is to the real position; Brightness of individual fireflies Calculated as: ; Where, is the observation noise covariance matrix; According to the brightness value, the attractiveness of firefly individuals The calculation is: ; Where, is the maximum attraction value, which is 1; is the light attenuation coefficient, and its value range is ; represents the distance between two fireflies, is a natural constant; exist moment, assuming For individuals with lower brightness values, For individuals with higher brightness values, The global optimal firefly individual at the moment redefines the individual's movement process as: ; Where, for time location, is the step size factor, and its value range is , is a random number.

[0010] After S2.3 updates the particle position, it calculates the particle brightness value and compares it with the current global optimal value to dynamically update the optimal solution. If the global optimal value meets the set convergence threshold, the optimal particle is selected as the final state estimation result. Otherwise, the particle swarm is resampled. The resampling process is as follows: Constructing the cumulative distribution function based on normalized weights ,make 、 ; ; Particles are selected by random sampling. Generated within the interval Random numbers , mapping the random number with the cumulative distribution function, for each random number , finding the index of the cumulative distribution function , satisfying: ; selecting the th particle as a particle in the new particle set, and continuing to perform the updating process based on the glowworm algorithm in the new particle set until a convergence condition is satisfied.

[0011] S3 includes S3.1 the real space distance between the unmanned aerial vehicle and the first satellite is , the distance measured by the unmanned aerial vehicle and the first satellite is , , and the relationship between , and is: ; In the formula, is the coordinate of the first satellite, and the position information in output by the improved particle swarm optimization algorithm is taken as the coordinate of the unmanned aerial vehicle , is the propagation delay error caused by the ionosphere and troposphere, is the clock error of the unmanned aerial vehicle, is the clock error of the satellite, is the speed of light; In the repeater deception jamming, the signal delay is artificially increased, the pseudo-range measurement value of the unmanned aerial vehicle is changed, and the relationship between and is: ; In the formula, is the signal delay amount of the first satellite introduced; determining the repeater coordinate After the satellite signal is received by the repeater device, the satellite position is calculated, and the propagation time delay of the repeater signal is obtained according to the satellite position and the repeater coordinate ; according to the satellite position coordinate and the position coordinate of the repeater device, the propagation time delay of the satellite signal to the repeater device is calculated ; according to the actual position of the unmanned aerial vehicle and the position of the repeater device, the propagation time delay of the signal is calculated , and the actual position of the unmanned aerial vehicle is obtained by the detection device; The repeater time delay amount of each satellite is: ;​​ According to the geometric relationship between the satellite, forwarding equipment and drone positions, a common delay is introduced. ,satisfy , is the total number of satellite signals being forwarded; When the common delay is introduced Afterwards, the forwarding pseudorange measurement equation is: ; According to the satellite positioning solution principle, the public delay The introduction of causes the change of positioning solution clock error, and the UAV clock error becomes: ; Where, After introducing the common delay .

[0012] S3 including S3.2 uses the objective function Optimizing public delay : ; Where, is the objective function, is the distance between the deceptive positioning position preset by the jammer and the actual deceptive positioning position, The deceptive positioning position preset for the jammer, The actual deceptive positioning position of the jammer, The common delay The decision vector formed satisfies , is the corrected forwarding delay.

[0013] S3, including S3.3, uses an intelligent optimization algorithm based on particle filtering and Levy flight strategy to seek the optimal solution; S3.3.1 Initialize algorithm parameters and set the maximum population size of the algorithm , maximum number of iterations , the upper bound of the search range and the Nether , the number of hyperparameters of the optimization problem ; Mutation probability in genetic algorithms ; Inertia weight in particle swarm optimization , learning factor and , random factors and yes Random numbers in the interval, particle speed ;Control parameters in Levy flight strategy ; Mapping coefficient in chaotic mapping strategy ; S3.3.2 uses real number coding to initialize all population individual parameters based on the Logistic chaotic mapping function and the benchmark individual , the expression of the Logistic function is: ; Where, For the Individual values; Individuals outside the upper and lower bounds of the search range are defined as the average of the upper and lower bounds; S3.3.3 According to The function calculates the fitness function value of all individuals. The fitness function constructs the distance between the preset deceptive positioning position of the jammer and the actual deceptive positioning position, and saves the optimal position of the individual in the iterative process. and the global optimal position ; S3.3.4 According to the setting Select some individuals from the population and introduce the Lévy flight strategy to perform mutation operations: ; Where, Indicates the current iteration number, For the The particle in The position of the iteration, is the scaling factor that controls the range of Levi's flight jump. , is a random vector from the Lévy flight distribution, is the tensor product operator symbol, and To control the parameters, which change with the number of iterations, the expression is: ; ; Where, is the maximum number of iterations of the algorithm; For the remaining individuals in the population, continue to update the speed and position through the particle swarm algorithm: ; Where, It is The particle in The speed of iterations; Control the impact of history speed; and regulating the weight of individual and group experiences; and Enhance search randomness; The position updating formula of the particle updates the current position of the particle by moving the particle in the current speed direction by a set step size: ; S3.3.5, the fitness values of all individuals after updating, the individual optimal positions and the global optimal position are calculated; S3.3.6, if the maximum number of iterations is reached or the threshold condition is met, the algorithm ends, and the common time delay is obtained ; otherwise, return to S3.3.4 for the next iteration loop.

[0014] The common time delay is obtained as input to obtain the clock error of the unmanned aerial vehicle and the , which makes the unmanned aerial vehicle obtain false pseudorange information, and further makes the positioning position calculated by the unmanned aerial vehicle be the B point, thereby realizing the relay deception jamming of the unmanned aerial vehicle.

[0015] Compared with the prior art, the present application has the following beneficial effects: the six-degree-of-freedom model of the unmanned aerial vehicle is modeled and simulated, thereby more accurately simulating the real flight process and maneuvering changes of the unmanned aerial vehicle, making the model better reflect the attitude changes and dynamic responses of the unmanned aerial vehicle in flight; the particle filtering method based on glowworm algorithm optimization is introduced, the particle sampling strategy and weight updating mechanism are improved, the problems of particle degradation and sample impoverishment are effectively alleviated, and the tracking stability and precision under noisy observation are significantly improved; the common time delay optimization calculation model based on the improved particle swarm optimization algorithm is constructed, the relay error is dynamically compensated and corrected, thereby improving the precision and system robustness of the deception positioning, and enhancing the overall deception jamming effect; the system is constructed to integrate trajectory generation, state tracking and path deception into an integrated unmanned aerial vehicle deception guidance, which provides effective technical support for improving the deception jamming capability of the terminal defense system. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 is a three-dimensional flight trajectory diagram of the unmanned aerial vehicle; Figure 2 is a curve diagram of the axis position change during the flight of the unmanned aerial vehicle; Figure 3 is a curve diagram of the axis position change during the flight of the unmanned aerial vehicle; Figure 4 is a curve diagram of the axis position change during the flight of the unmanned aerial vehicle; Figure 5 is a curve diagram of the angle of attack and the sideslip angle changing with time; Figure 6 is a curve diagram of the pitch angle, yaw angle and roll angle changing with time; Figure 7 is a graph showing the change of roll angular velocity, yaw angular velocity and pitch angular velocity with time; Figure 8 is a graph of the change of inertial position over time; Figure 9 It is the Y-axis error graph before filtering; Figure 10 is the Y-axis error graph after filtering; Figure 11 This is a comparison chart of the three-axis errors before and after filtering; Figure 12 It is a three-dimensional flight trajectory diagram of the UAV under forwarding deception interference. DETAILED DESCRIPTION

[0017] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention are described clearly and completely below. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0018] A UAV path deception jamming method based on an intelligent optimization algorithm includes: S1 establishes the UAV dynamic equation and kinematic equation to obtain the desired flight trajectory of the UAV; S2 introduces an improved particle swarm optimization algorithm to track and estimate the status of drones; S3 introduces a particle filtering method based on firefly algorithm optimization to calculate the common delay in forwarding interference, and interferes with and guides the drone positioning information.

[0019] S1 includes S1.1. The UAV is analyzed based on the dynamics of a rigid body. The center of mass dynamics equation of the UAV in the body coordinate system is: ; Where, 、 and are the roll angular velocity, yaw angular velocity and pitch angular velocity respectively, 、 and The velocity vectors are axis, Axis and The velocity component of the axis, is the engine thrust, 、 and The three directions of aerodynamic force decomposed in the airflow coordinate system are drag, lift and side force. is the angle between the thrust vector and the longitudinal axis of the body, is the mass of the UAV, is the acceleration of gravity, is time, is the pitch angle, is the roll angle, is the angle of attack, is the side slip angle; S1.2 The dynamic equation of the UAV rotating around the center of mass in the body coordinate system is established as: ; ; wherein, , , is the moment of inertia, is the inertia product, is the variable to be solved, , and are the components of the external moment acting on the UAV in the body coordinate system along the axis, axis and axis.

[0020] S1 includes S1.3 The relationship between the velocity components of the UAV in the ground coordinate system is established as: ; wherein, , and are the three-dimensional coordinates of the UAV along the axis, axis and axis, , , , , and are the three-dimensional velocities of the UAV along the axis, axis and axis, is the yaw angle; S1.4 The relationship between the angular velocity components of the UAV in the body coordinate system is established, which is equivalent to the kinematic equation of the UAV rotating around the center of mass: ; wherein, , are the derivatives of the roll angular velocity, yaw angular velocity and pitch angular velocity, respectively, and the state variables of the UAV are , For control input.

[0021] S2 includes S2.1 Position information matrix at each moment Add observation error as the observation value of the state transfer process : = ; Where, 、 、 、 、 and represents the added observation error coefficient, represents a normally distributed random number with mean 0 and variance 1; Computational particle filtering; At the initial moment, the system state is sampled to generate the initial particle set, and the system prior distribution is used to Initialize the particles and assign equal initial weights to each particle: ; Where, For the initial moment The state of a particle, is the total number of particles, is the initial state, For the initial moment The weight of each particle; exist At this moment, according to the state transition model, each particle is sampled and The particle distribution at time is transferred to the current time: ; Where, for Moment The state of a particle, for The particle distribution at time The prior distribution of time, for The state value at the moment; Obtain the conditional posterior probability density of the state represented by weighted particles : ; Where, for The observed value at time, for Moment The weight of a particle, is the Dirac function; with the current observation data adjust the weight of the particle: ; where, is the proportional symbol, is the state of the particle observation data at time ; ; where, is the normalized weight, is the particle; The state estimate of the target is the weighted average of the current state distribution: ; where, is the state estimate at time ; Finally, the error covariance : .

[0022] S2 includes S2.2 to calculate the attraction degree value between each particle and the global optimal firefly individual, and update the position of the particle; In the brightness calculation of the firefly algorithm, the of the particle filter is introduced, and the higher the brightness value, the better the spatial position, and the greater the weight of the corresponding particle, and closer to the true position; The brightness of the firefly individual is calculated as: ; where, is the observation noise covariance matrix; According to the brightness value, the calculation of the attraction degree of the firefly individual is: ; where, is the maximum attraction degree value, taking the value of 1; is the light attenuation coefficient, taking the value range of ; represents the distance between two fireflies, is a natural constant; At time , assume is the individual with lower brightness value, For the individual with higher brightness value, the movement process of the individual is redefined as: The global optimal firefly individual at time t redefines the movement process of the individual as: ; In the formula, is the position of the individual at time t, is a step factor, and the value range is , , is a random number.

[0023] S2 includes S2.3 updating the particle position, calculating the brightness value of the particle, and comparing with the current global optimal value to dynamically update the optimal solution; if the global optimal value meets the set convergence threshold, the optimal particle is selected as the final state estimation result, otherwise, the particle swarm is resampled, and the resampling process is: According to the normalized weight, the cumulative distribution function is constructed , , ; ; The particle is selected by random sampling, and random numbers are generated in the interval , the cumulative distribution function is used to map the random numbers, for each random number , the index of the cumulative distribution function is found, which satisfies: ; The th particle is selected as a particle in the new particle set, and the updating process based on the firefly algorithm is continued in the new particle set until the convergence condition is met.

[0024] S3 includes S3.1 the real space distance between the unmanned aerial vehicle and the first satellite is , the distance measured by the unmanned aerial vehicle between the first satellite and the first satellite is , , , and between them is: ; In the formula, is the coordinate of the first satellite, and the position information in output by the improved particle swarm optimization algorithm is taken as the coordinate of the unmanned aerial vehicle , The propagation delay error caused by the atmospheric ionosphere and troposphere, The clock error of the UAV, The clock error of the satellite, The speed of light; In the repeater deception jamming, the signal delay is artificially increased, the pseudo-range measurement value of the UAV is changed, and the relationship between the pseudo-range measurement value and the actual position of the UAV is calculated The relationship between the pseudo-range measurement value and the actual position of the UAV is calculated The relationship between the pseudo-range measurement value and the actual position of the UAV is calculated ; In the formula, is the signal delay of the first satellite introduced; The repeater coordinates are determined, the satellite position is calculated after the satellite signal is received by the repeater, and the propagation time delay of the repeater signal is calculated according to the satellite position and the repeater coordinates ; The propagation time delay of the satellite signal reaching the repeater is calculated according to the satellite position coordinates and the position coordinates of the repeater ; The propagation time delay of the signal is calculated according to the actual position of the UAV and the position of the repeater The actual position of the UAV is obtained by the detection equipment; The repeater time delay of each satellite is: ; According to the geometric relationship of the satellite, the repeater and the UAV position, a common time delay is introduced , which satisfies , The total number of satellite signals is When the common time delay is introduced, the repeater pseudo-range measurement equation is: ; According to the satellite positioning solution principle, the introduction of the common time delay causes the change of the positioning solution clock error, and the clock error of the UAV becomes: ; In the formula, is the after the introduction of the common time delay.

[0025] S3 includes S3.2 optimizing the common time delay with the objective function : ; In the formula, is the objective function, is the distance between the preset deception positioning position of the jammer and the actual deception positioning position, is the preset deception positioning position of the jammer, The actual deceptive positioning position of the jammer, The common delay The decision vector formed satisfies , is the corrected forwarding delay.

[0026] S3, including S3.3, uses an intelligent optimization algorithm based on particle filtering and Levy flight strategy to seek the optimal solution; S3.3.1 Initialize algorithm parameters and set the maximum population size of the algorithm , maximum number of iterations , the upper bound of the search range and the Nether , the number of hyperparameters of the optimization problem ; Mutation probability in genetic algorithms ; Inertia weight in particle swarm optimization , learning factor and , random factors and yes Random numbers in the interval, particle speed ;Control parameters in Levy flight strategy ; Mapping coefficient in chaotic mapping strategy ; S3.3.2 uses real number coding to initialize all population individual parameters based on the Logistic chaotic mapping function and the benchmark individual , the expression of the Logistic function is: ; Where, For the Individual values; Individuals outside the upper and lower bounds of the search range are defined as the average of the upper and lower bounds; S3.3.3 According to The function calculates the fitness function value of all individuals. The fitness function constructs the distance between the preset deceptive positioning position of the jammer and the actual deceptive positioning position, and saves the optimal position of the individual in the iterative process. and the global optimal position ; S3.3.4 According to the setting Select some individuals from the population and introduce the Lévy flight strategy to perform mutation operations: ; Where, Indicates the current iteration number, For the The particle in the position of the sub-iteration, is a scaling factor, controlling the amplitude of the Levy flight jumps, , is a random vector of the Levy flight distribution, is the tensor product operator, and is a control parameter, which varies with the iteration number, and is expressed as: ; ; where, is the maximum iteration number of the algorithm; For the rest of the population, the velocity and position are updated by the particle swarm optimization algorithm: ; where, is the velocity of the th particle in the th iteration; controls the influence of the historical velocity; and adjust the weights of the individual and group experience; and enhance the randomness of the search; The position of the particle is updated by moving the particle in the current velocity direction with a set step size: ; S3.3.5 Calculate the updated fitness value of all individuals, the individual optimal position, and the global optimal position; S3.3.6 If the maximum iteration number is reached or the threshold condition is met, the algorithm ends and the common time delay is obtained; otherwise, return to S3.3.4 for the next iteration loop.

[0027] The common time delay is obtained as input to obtain the clock error of the UAV and the that lures the UAV from point A to point B, so that the UAV obtains false pseudorange information, and the positioning position calculated by the UAV is point B, thereby realizing the relay deception jamming of the UAV.

[0028] In the actual flight process, the radar system inevitably introduces noise and error when measuring the trajectory of the unmanned aerial vehicle. Therefore, it is necessary to superimpose an observation error model on the ideal trajectory to construct observation data with certain errors. For this observation data, the improved particle swarm optimization algorithm and the particle filtering method based on the glowworm algorithm optimization are introduced to cooperatively improve the accuracy and robustness of the state estimation of the unmanned aerial vehicle, so as to realize accurate tracking and error suppression of the target trajectory. The high-precision estimated trajectory output by the above particle filtering system is used as the input of the deception jamming, and the common time delay in the interference signal is calculated by the particle swarm optimization algorithm. By means of the method, the target can be misled to the preset error position, so that after the position data with errors is received by the opponent system, significant positioning deviation is caused, and the purpose of effective jamming and tactical deception is achieved.

[0029] Based on the established unmanned aerial vehicle dynamics and kinematics model, any flight trajectory can be generated to verify the adaptability and effectiveness of the model. As shown in Figure 1 , the following flight path is preset: the unmanned aerial vehicle first flies horizontally along the axis for 50 seconds, then climbs to a height for 50 seconds, and finally hovers in a certain range. The position curves of the axis, axis and axis during the flight of the unmanned aerial vehicle are as shown in Figure 2 , Figure 3 and Figure 4 . The flight process simulates the complete task flow of the unmanned aerial vehicle from take-off, flight to the target area and implementation of reconnaissance.

[0030] Based on the established model, real-time monitoring and recording of each state variable of the unmanned aerial vehicle during flight can be realized. The curves of the angle of attack and the sideslip angle changing with time are as shown in Figure 5 , alpha represents the angle of attack, and beta represents the sideslip angle; the curves of the pitch angle, yaw angle and roll angle changing with time are as shown in Figure 6 , phi represents the pitch angle, theta represents the yaw angle, and psi represents the roll angle; the curves of the roll angular velocity, yaw angular velocity and pitch angular velocity changing with time are as shown in Figure 7 , p represents the roll angular velocity, q represents the yaw angular velocity, and r represents the pitch angular velocity; the curves of the inertial position changing with time are as shown in Figure 8 , Xe represents the axis position, Ye represents the axis position, and Ze represents the axis position; the simulation results show that the change trend of each state quantity conforms to the basic law of aerodynamics and flight dynamics, has good physical consistency, can truly reflect the dynamic characteristics of the unmanned aerial vehicle in actual flight, and verifies the rationality and effectiveness of the model.

[0031] To verify the effectiveness of the proposed particle filter method based on glowworm swarm optimization in the state estimation of UAV, comparative analysis is carried out in terms of trajectory error estimation. Figure 9 、 Figure 10 The error in the x-axis direction before and after the particle filter method based on glowworm swarm optimization is shown respectively. Before filtering, the error gradually increases with time, and the fluctuation is significant, indicating that there is a large observation noise and estimation bias in the system without filtering. After using the PF method, the trajectory error is greatly reduced, and the error interval is obviously contracted, indicating that PF has certain filtering ability and robustness.

[0032] Further comparison of errors in three-axis directions before and after filtering is given in Figure 11 . Blue points in the figure represent the error in the unfiltered state, and red points represent the error after the PF method. As can be seen from the figure, the PF method effectively reduces the fluctuation range of the error, making the error distribution more concentrated. This is because the brightness function and attraction degree mechanism based on the observation value are introduced in the particle update process, so that the particles can adaptively tend to the estimation direction with smaller error, significantly improving the estimation accuracy.

[0033] Based on the relay satellite signal deception model, the flight trajectory of the UAV in the interference environment is simulated and visualized using MATLAB. The initial position, target point and coordinates of four satellites are set, the signal propagation delay is optimized by the improved particle swarm optimization algorithm, the navigation system is misled, and the UAV gradually deviates from the true trajectory in control. The three-dimensional flight trajectory of the UAV under relay deception interference is shown in Figure 12 .

[0034] The above examples are only used to illustrate the technical solutions of the present application, and not to limit it. Although the present application has been described in detail with reference to the foregoing examples, those skilled in the art should understand that they can still modify the technical solutions recorded in the foregoing examples, or make equivalent replacement for part or all of the technical features, and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A UAV path deception jamming method based on intelligent optimization algorithm, characterized in that: include: S1 establishes the UAV dynamic equation and kinematic equation to obtain the desired flight trajectory of the UAV; S2 introduces an improved particle swarm optimization algorithm to track and estimate the status of drones; S3 introduces a particle filtering method based on firefly algorithm optimization to calculate the common delay in forwarding interference, and interferes with and guides the drone positioning information.

2. The method for deceiving and jamming a drone path based on an intelligent optimization algorithm according to claim 1 is characterized in that: S1 includes S1.

1. The UAV is analyzed as a rigid body. The center of mass dynamics equation of the UAV in the body coordinate system is: ； Where, 、 and are the roll angular velocity, yaw angular velocity and pitch angular velocity respectively, 、 and The velocity vectors are axis, Axis and The velocity component of the axis, is the engine thrust, 、 and The three directions of aerodynamic force decomposed in the airflow coordinate system are drag, lift and side force. is the angle between the thrust vector and the longitudinal axis of the aircraft, For drone quality, is the acceleration due to gravity, For time, is the pitch angle, is the roll angle, is the angle of attack, is the sideslip angle; S1.2 The dynamic equation of the UAV's rotation around its center of mass is established in the body coordinate system as follows: ； ； Where, 、 、 is the moment of inertia, is the product of inertia, is a variable that can be sought, 、 and The external torque acting on the UAV in the body coordinate system is axis, Axis and The axis's components.

3. The method for deceiving and jamming drone paths based on an intelligent optimization algorithm according to claim 2 is characterized in that: S1.3 includes establishing the relationship between the UAV velocity components in the ground coordinate system: ； Where, 、 and The drones are axis, Axis and The three-dimensional coordinates of the axes, , , , 、 and The drones are axis, Axis and The three-dimensional velocity of the axis, is the yaw angle; S1.4 Establish the relationship between the angular velocity components of the drone in the body coordinate system, which is equivalent to the kinematic equation for the drone's rotation about its center of mass: ； Where, 、 are the derivatives of the roll angular velocity, yaw angular velocity and pitch angular velocity respectively, and the state of the UAV is obtained as follows: , For control input.

4. The method for deceiving and jamming a UAV path based on an intelligent optimization algorithm according to claim 3 is characterized in that: S2 includes S2.1 Position information matrix at each moment Add observation error as the observation value of the state transfer process : = ； Where, 、 、 、 、 and represents the added observation error coefficient, represents a normally distributed random number with mean 0 and variance 1; Computational particle filtering; At the initial moment, the system state is sampled to generate the initial particle set, and the system prior distribution is used to Initialize the particles and assign equal initial weights to each particle: ； Where, For the initial moment The state of a particle, is the total number of particles, is the initial state, For the initial moment The weight of each particle; exist At this moment, according to the state transition model, each particle is sampled and The particle distribution at time is transferred to the current time: ； Where, for Moment The state of a particle, for The particle distribution at time The prior distribution of time, for The state value at the moment; Obtain the conditional posterior probability density of the state represented by weighted particles : ； Where, for The observed value at time, for Moment The weight of a particle, is the Dirac function; Using current observation data Adjust the weight of the particles: ； Where, is a proportional symbol, Particle state Time observation data probability; Normalize the updated particle weights: ； Where, is the normalized weight, For the particles; The target state estimate is the weighted average of the current state distribution: ； Where, for The estimated value of the state at the moment; Finally calculate the error covariance : 。 5. The method for deceiving and jamming a UAV path based on an intelligent optimization algorithm according to claim 4 is characterized in that: S2 includes S2.2 calculating the attraction value between each particle and the global optimal firefly individual, and updating the position of the particle; Introducing particle filtering into the brightness calculation of the firefly algorithm ,The higher the brightness value, the better the spatial position, the greater the weight of the corresponding particle, and the closer it is to the real position; Brightness of individual fireflies Calculated as: ； Where, is the observation noise covariance matrix; According to the brightness value, the attractiveness of firefly individuals The calculation is: ； Where, is the maximum attraction value, which is 1; is the light attenuation coefficient, and its value range is ; represents the distance between two fireflies, is a natural constant; exist moment, assuming For individuals with lower brightness values, For individuals with higher brightness values, The global optimal firefly individual at the moment redefines the individual's movement process as: ； Where, for time location, is the step size factor, and its value range is , is a random number.

6. The method for deceiving and jamming a UAV path based on an intelligent optimization algorithm according to claim 5 is characterized in that: After S2.3 updates the particle position, it calculates the particle brightness value and compares it with the current global optimal value to dynamically update the optimal solution. If the global optimal value meets the set convergence threshold, the optimal particle is selected as the final state estimation result. Otherwise, the particle swarm is resampled. The resampling process is as follows: Constructing the cumulative distribution function based on normalized weights ,make 、 ; ； Particles are selected by random sampling. Generated within the interval Random numbers , use the cumulative distribution function to map the random numbers, for each random number , find the index of the cumulative distribution function ,satisfy: ； Select of particles As a particle in the new particle set, the update process based on the firefly algorithm is continued in the new particle set until the convergence condition is met.

7. The method for deceiving and jamming a UAV path based on an intelligent optimization algorithm according to claim 6 is characterized in that: S3 includes S3.1 drone and The real space distance between the satellites is , the drone measured the same The distance between satellites is , and The relationship between them is: ； Where, For the The coordinates of the satellites are finally output by the improved particle swarm optimization algorithm. The location information in the image is used as the coordinates of the drone. , is the propagation delay error caused by the atmospheric ionosphere and troposphere, is the drone clock error, is the satellite clock error, is the speed of light; In the forwarding deception jamming, the signal delay is artificially increased to change the pseudo-range measurement value of the drone and calculate the and The relationship between them is: ； Where, For the introduction of The signal delay of the satellites; Determine forwarding coordinates After receiving the satellite signal, the forwarding device calculates the satellite position and obtains the propagation delay of the forwarding signal based on the satellite position and the forwarding coordinates. ; Calculate the propagation delay of the satellite signal to the forwarding device based on the satellite position coordinates and the forwarding device position coordinates ; Calculate the signal propagation delay based on the actual position of the drone and the position of the forwarding device ,The actual position of the UAV is obtained by the detection equipment; The forwarding delay of each satellite is: ； According to the geometric relationship between the satellite, forwarding equipment and drone positions, a common delay is introduced. ,satisfy , is the total number of satellite signals being forwarded; When the common delay is introduced Afterwards, the forwarding pseudorange measurement equation is: ； According to the satellite positioning solution principle, the public delay The introduction of causes the change of positioning solution clock error, and the UAV clock error becomes: ； Where, After introducing the common delay .

8. The method for deceiving and jamming a UAV path based on an intelligent optimization algorithm according to claim 7 is characterized in that: S3 including S3.2 uses the objective function Optimizing public delay : ； Where, is the objective function, is the distance between the deceptive positioning position preset by the jammer and the actual deceptive positioning position, The deceptive positioning position preset for the jammer, The actual deceptive positioning position of the jammer, The common delay The decision vector is composed of , is the corrected forwarding delay.

9. The method for deceiving and jamming a UAV path based on an intelligent optimization algorithm according to claim 8 is characterized in that: S3, including S3.3, uses an intelligent optimization algorithm based on particle filtering and Levy flight strategy to seek the optimal solution; S3.3.1 Initialize algorithm parameters and set the maximum population size of the algorithm , maximum number of iterations , the upper bound of the search range and the Nether , the number of hyperparameters of the optimization problem ; Mutation probability in genetic algorithms ; Inertia weight in particle swarm optimization , learning factor and , random factors and yes Random numbers in the interval, particle speed ; Control parameters in Levy flight strategy ; Mapping coefficient in chaotic mapping strategy ; S3.3.2 uses real number coding to initialize all population individual parameters based on the Logistic chaotic mapping function and the benchmark individual , the expression of the Logistic function is: ； Where, For the Individual values; Individuals outside the upper and lower bounds of the search range are defined as the average of the upper and lower bounds; S3.3.3 According to The function calculates the fitness function value of all individuals. The fitness function constructs the distance between the preset deceptive positioning position of the jammer and the actual deceptive positioning position, and saves the optimal position of the individual in the iterative process. and the global optimal position ; S3.3.4 According to the setting Select some individuals from the population and introduce the Lévy flight strategy to perform mutation operations: ； Where, Indicates the current iteration number, For the The particle in The position of the iteration, is the scaling factor that controls the range of Levi's flight jump. , is a random vector from the Lévy flight distribution, is the tensor product operator symbol, and To control the parameters, which change with the number of iterations, the expression is: ； ； Where, is the maximum number of iterations of the algorithm; For the remaining individuals in the population, continue to update the speed and position through the particle swarm algorithm: ； Where, It is The particle in The speed of iterations; Control the impact of history speed; and regulating the weight of individual and group experiences; and Enhance search randomness; The particle position update formula updates the particle's current position by moving the particle in the current velocity direction by a set step size: ； S3.3.5 Calculate the updated fitness values, individual optimal positions, and global optimal positions of all individuals; S3.3.6 If the maximum number of iterations is reached or the threshold condition is met, the algorithm ends and the common delay is obtained. ; Otherwise, return to S3.3.4 to proceed to the next iteration loop.

10. The method for deceiving and jamming a drone path based on an intelligent optimization algorithm according to claim 9 is characterized in that: Obtain the common delay , as input to obtain the drone's clock error and the method to lure the drone from point A to point B , causing the drone to obtain incorrect pseudo-range information, and then the drone's calculated positioning position is point B, thereby achieving forwarding deception interference on the drone.

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