Unmanned aerial vehicle cluster high-fidelity cooperative jamming method and system for networking radar

Abstract

This invention discloses a high-fidelity cooperative jamming method and system for UAV swarms targeting networked radar. The method includes: constructing a UAV dynamics model; constructing a UAV energy consumption model, including UAV propulsion energy consumption and DRFM relay energy consumption; designing false flight paths based on the dynamic characteristics, RCS characteristics, and scene adaptability factors of the false target; calculating the required relay power to simulate RCS fluctuations by calculating the target attitude angle and radar observation angle, combined with three-dimensional RCS data; establishing an optimization model containing UAV dynamics constraints with the goal of minimizing the total energy consumption of the UAV swarm, using the flight heading angle as the optimization variable, and solving the optimization model using a genetic algorithm. This invention improves the credibility of false flight paths, increases the duration of flight path deception, ensures the stability of deception in complex battlefield environments, and enhances the practical adaptability of cooperative jamming.

Description

Technical Field

[0001] This invention belongs to the field of radar electronic countermeasures and UAV swarm cooperative combat technology, specifically involving a high-fidelity cooperative jamming method and system for UAV swarms targeting networked radar. Background Technology

[0002] Networked radar technology has developed rapidly in recent years. Through multi-station collaborative detection and a "same source verification" mechanism, it can cross-verify the consistency of target spatial states, effectively eliminate false targets, and significantly improve anti-jamming capabilities. Unmanned aerial vehicle (UAV) swarms, with their distributed deployment and collaborative combat advantages, have become a core means of countering networked radar. Among these, collaborative jamming technology, which generates realistic false flight paths through multi-platform collaboration, disrupts the target detection and situational awareness of networked radar and is one of the key countermeasures against it. In real battlefield environments, UAV swarms face multiple technical bottlenecks in conducting high-fidelity coordinated jamming against networked radars: First, the fidelity of false tracks is insufficient. Traditional false tracks have a fixed radar cross-section, which does not match the dynamic fluctuations of real targets, and the tactical rationality of the tracks is lacking. A single false track is difficult to adapt to actual needs. Second, no optimization model has been established with the goal of minimizing energy consumption. The energy consumption of UAV flight and DRFM forwarding is limited. Existing algorithms do not take into account both UAV swarm track deception and energy consumption optimization, resulting in insufficient endurance of jamming missions. Third, most existing research is based on track deception in ideal scenarios and does not consider the impact of multi-source errors on the stability of UAV swarm coordinated jamming.

[0003] In existing technologies, some studies utilize multi-aircraft collaboration for track deception, but they do not integrate high-fidelity track design and energy consumption optimization, nor do they consider the impact of multi-source errors on the deception effect, making it difficult to maintain a stable collaborative track deception interference effect in complex environments.

[0004] Therefore, there is an urgent need for a collaborative interference method that balances cluster coordination, high fidelity, and energy efficiency to address the shortcomings of existing technologies. Summary of the Invention

[0005] Purpose of the invention: The purpose of this invention is to provide a high-fidelity cooperative jamming method and system for UAV swarms targeting networked radar, thereby achieving cooperative track deception jamming of networked radar.

[0006] Technical solution: The high-fidelity cooperative jamming method for UAV swarms targeting networked radar, as described in this invention, includes the following steps:

[0007] A dynamic model of the UAV is constructed, and the key parameters of the UAV's trajectory are derived by the spatial relationship between the radar, the UAV and the false target.

[0008] Construct an energy consumption model for drones, where the total energy consumption of drones includes drone propulsion energy consumption and DRFM relay energy consumption;

[0009] Based on the dynamic characteristics, RCS characteristics, and scene adaptability factors of the false target, a false trajectory is designed.

[0010] By calculating the target attitude angle and radar observation angle, and combining RCS three-dimensional data, the required forwarding power is calculated to simulate RCS fluctuation characteristics. This includes: obtaining the radar line-of-sight vector from the coordinate positions of the radar and the UAV, then converting the line-of-sight vector into a vector in the target coordinate system through a rotation matrix to obtain the RCS angle of the radar-observed target, reading RCS data of different targets at specific frequencies, filtering the azimuth angle, elevation angle and RCS values ​​of the corresponding frequencies, constructing an RCS matrix indexed by angles and performing normalization processing, finding the corresponding weight value in the normalized RCS matrix according to the attitude angle, calculating the optimal forwarding power under the current attitude, and realizing the dynamic simulation of the RCS fluctuation characteristics of the real target.

[0011] With the goal of minimizing the total energy consumption of the UAV swarm, and with UAV dynamics constraints and DRFM forwarding power constraints as constraints, and flight heading angle as the optimization variable, a complete multi-parameter coupled optimization model is established.

[0012] A genetic algorithm is used to solve the optimization model. The heading angle parameter is optimized based on the genetic algorithm. At the initial moment, the UAV's heading angle is matched with the initial movement direction of the false target. At non-initial moments, the optimal heading angle adjustment is obtained through algorithm iteration. The adjustment is then superimposed to obtain the optimal heading angle for the next moment. Then, the UAV's motion parameters are updated and constraints are checked. The predicted motion parameters are calculated based on the optimized heading angle to verify whether all constraints are met. If not, the optimization is repeated. Finally, multi-source random error is introduced to back-infer the position of the false target to evaluate the deception effect.

[0013] Furthermore, the positional relationship between the radar, the drone, and the false target is expressed as follows:

[0014] ;

[0015] in, Indicates to Differentiating, Indicates to Differentiating, Indicates to Differentiating, The elevation angle of the line connecting the radar and the false target. The azimuth angle of the line connecting the radar and the false target. The distance between the radar and the drone. Indicates the speed of the drone. Indicates the heading angle of the drone. Indicates the drone's pitch angle;

[0016] The key parameters of the drone's trajectory are expressed as follows:

[0017] .

[0018] Furthermore, the energy consumption expression for drone propulsion is as follows:

[0019] ;

[0020] in, Energy consumption for drone propulsion For flight time, For flight speed, For acceleration, for transpose, For the total mass of the drone, It is the acceleration due to gravity. , These are the initial and final velocity vectors, respectively. For modulo operation, , These are inherent parameters of the UAV;

[0021] When the duration of the flight path exceeds the time threshold, the change in kinetic energy is negligible, and the above equation simplifies to:

[0022] .

[0023] Furthermore, the expression for the target's flight attitude angles is:

[0024] ;

[0025] in, The azimuth angle of the line connecting the radar and the target. The component of flight velocity along the x-axis. Let y be the component of the flight velocity along the y-axis. Let Z be the component of the flight velocity along the z-axis. For the target flight speed modulus, Indicates the target's flight speed. The elevation angle of the line connecting the radar and the target;

[0026] The radar's observation angle of the target is consistent with the direction of the radar's line of sight; the line-of-sight vector... Represented as:

[0027] ;

[0028] in, , and The line-of-sight vectors are respectively in , and Components in direction; For radar coordinates, Coordinates of the drone;

[0029] The RCS angle of a radar target is the azimuth and elevation angles of the line-of-sight vector in the target coordinate system. This involves converting the line-of-sight vector into a vector in the target coordinate system. :

[0030] ;

[0031] in, , , The vectors in the target coordinate system are respectively , and Components in direction, for transpose, For rotation matrix, , This is the rotation matrix for the azimuth angle. The rotation matrix is ​​the pitch angle. Let be the rotation matrix for the attitude angle.

[0032] Furthermore, the optimized model expression is as follows:

[0033] ;

[0034] in, This represents the total energy consumption of the drone, including drone propulsion energy consumption and DRFM relay energy consumption. This is the yaw angle of the drone. The pitch angle of the drone. For the instantaneous speed of the drone, The flight altitude of the drone. This is the upper limit of flight altitude. This is the lower limit of flight altitude. The yaw angle of the drone at the current moment. The yaw angle of the drone at the previous moment. For acceleration constraints, The current pitch angle of the drone. The pitch angle of the drone at the previous moment. For pitch angle constraints, For the acceleration of the drone, The minimum flight speed of the drone. The relay signal power of the UAV equipped with DRFM at time k. For power constraints.

[0035] Furthermore, a genetic algorithm is used to solve the optimization model, including the following steps:

[0036] (1) System initialization and trajectory generation: First, define the core simulation parameters, including the number of UAVs, the number of radars, and the basic configuration of deception duration. At the same time, set the UAV maneuverability constraints, including the maximum heading angle variation range, the maximum pitch angle variation range, the altitude boundary, the speed threshold, and the maximum acceleration constraint conditions. Clarify the position coordinates and range resolution parameters of the network radars. Pre-generate three types of false target trajectories: The first type is a runway-shaped trajectory at a fixed altitude, which achieves periodic movement by calculating the position of the straight flight and semi-circular turning stages in segments. The second and third types are trajectories with maneuverability characteristics, both of which include the initial straight flight, the mid-stage longitudinal maneuver, and the final stage lateral maneuver. The initial position is set at 1 / 3 of the line connecting the radar and the false target. Based on the generated initial position sequence, the instantaneous velocity vector of each false target is calculated using the difference method. Finally, the initial position of the UAV is deployed at 1 / 3 of the line connecting the initial position of the corresponding radar and the false target.

[0037] (2) Optimize the heading angle parameters of the UAV based on the GA algorithm: At the initial moment, the heading angle of the UAV is set to the initial movement direction of the corresponding false target. If it is not the initial moment, the current position of the UAV, the current and next position of the false target, the current heading angle and all constraints are input. The optimal heading angle adjustment is obtained by iterative optimization through the genetic algorithm. The adjustment is added to the current heading angle to form the optimal heading angle at the next moment.

[0038] (3) Update and check the motion parameters of the UAV: ​​Based on the optimized heading angle, combined with the current position and velocity state of the UAV, calculate the predicted position, velocity and pitch angle of the next moment; check for constraint violations and verify whether the predicted state meets the preset constraints of altitude boundary and velocity threshold; if the predicted state meets all the constraints, update the UAV position and enter the next time step; if there are constraint violations, reject the current optimization result and return to step two to re-execute the genetic algorithm optimization until a feasible heading angle that meets all constraints is obtained;

[0039] (4) Calculation of radar observation angle and target attitude angle: introduce random time delay error, random radar site error and random UAV jitter error respectively. Based on the time delay data with error and the actual position of UAV and radar, the position of false target observed by radar is inferred. The distance distribution between the actual generated false track points is analyzed to evaluate the deception effect. Calculate the flight attitude angle. Through the transformation from the geodetic coordinate system to the body coordinate system, with the false target velocity vector as a reference, calculate the azimuth and pitch angle of the radar relative to the false target at each moment.

[0040] (5) Calculation of forwarding power based on RCS: Read the RCS data of different targets at specific frequencies, filter the azimuth angle, pitch angle and RCS value corresponding to the target frequency, construct the RCS matrix indexed by angle and normalize it to obtain the weight matrix; set the rated forwarding power of the UAV, find the corresponding weight value in the normalized RCS matrix according to the attitude angle calculated in step (4), multiply the rated power with the weight to obtain the optimal forwarding power under the current attitude, and realize the power allocation of RCS matching.

[0041] The high-fidelity cooperative jamming system for UAV swarms targeting networked radar, as described in this invention, includes:

[0042] The UAV dynamics model building unit is used to construct the UAV dynamics model and derive the key parameters of the UAV's trajectory by the spatial relationship between radar, UAV and false target.

[0043] The UAV energy consumption model building unit is used to build a UAV energy consumption model. The total UAV energy consumption includes UAV propulsion energy consumption and DRFM relay energy consumption.

[0044] The false trajectory design unit is used to design false trajectories based on the dynamic characteristics, RCS characteristics, and scene adaptability factors of false targets.

[0045] The RCS characteristic simulation unit is used to calculate the required forwarding power by calculating the target attitude angle and radar observation angle, combined with RCS three-dimensional data, to simulate RCS fluctuation characteristics. It includes: obtaining the radar line-of-sight vector from the coordinate positions of the radar and the UAV, then converting the line-of-sight vector into a vector in the target coordinate system through a rotation matrix to obtain the RCS angle of the radar-observed target, reading RCS data of different targets at specific frequencies, filtering the azimuth angle, elevation angle and RCS values ​​of the corresponding frequencies, constructing an RCS matrix indexed by angle and performing normalization processing, finding the corresponding weight value in the normalized RCS matrix according to the attitude angle, calculating the optimal forwarding power under the current attitude, and realizing the dynamic simulation of the RCS fluctuation characteristics of the real target.

[0046] The optimization model building unit is used to establish a complete multi-parameter coupled optimization model with the goal of minimizing the total energy consumption of the UAV swarm, the constraints of UAV dynamics and DRFM forwarding power, and the flight heading angle as the optimization variable.

[0047] The optimization model solving unit is used to solve the optimization model using a genetic algorithm. Based on the genetic algorithm, the heading angle parameter is optimized. At the initial moment, the UAV's heading angle is matched with the initial movement direction of the false target. At non-initial moments, the optimal heading angle adjustment is obtained through algorithm iteration. The adjustment is then superimposed to obtain the optimal heading angle for the next moment. Then, the UAV's motion parameters are updated and constraints are checked. The predicted motion parameters are calculated based on the optimized heading angle, and it is verified whether all constraints are met. If not, the optimization is repeated. Finally, multi-source random error is introduced to back-infer the position of the false target to evaluate the deception effect.

[0048] The present invention also provides an electronic device, comprising:

[0049] Memory, used to store computer programs;

[0050] A processor for executing the computer program to implement the method.

[0051] The present invention also provides a non-volatile storage medium for storing a computer program, wherein the computer program implements the method described thereon when executed by a processor.

[0052] The present invention also provides a computer program product, including a computer program / instructions that, when executed by a processor, implement the method described.

[0053] Beneficial effects: Compared with the prior art, the significant technical effects of the present invention are as follows: (1) In view of the problem of insufficient realism of traditional false tracks, the present invention proposes a tactical track design and RCS dynamic simulation collaborative method; combining the tactical motion characteristics of real targets such as early warning aircraft and fighter jets to design false tracks, and dynamically adjusting the DRFM forwarding power by calculating the radar observation angle and target attitude angle in real time, simulating the RCS fluctuation characteristics of real targets, improving the motion realism and electromagnetic scattering consistency of false targets, solving the pain point that traditional false tracks are easily identified, and reducing the probability of being identified by networked radar; the advantage of the present invention is that it constructs a high-fidelity model from the dual dimensions of track characteristics and electromagnetic scattering characteristics, simulates the characteristics of real targets, and improves the credibility of false tracks; (2) In view of the problem of limited endurance of UAV swarms, the present invention proposes an UAV swarm energy consumption optimization model; constructing an energy consumption model that includes flight energy consumption and DRFM forwarding energy consumption, and dynamically allocating the flight parameters of each UAV through a genetic algorithm. The forwarding power weight minimizes the total energy consumption of the cluster under the premise of satisfying the deception effectiveness constraint, solves the pain point of the traditional algorithm ignoring energy consumption optimization and causing the short duration of the task, and extends the endurance of the trajectory deception task; the advantage of this invention is that it establishes a global optimization model with energy consumption optimization as the guide, and improves the duration of trajectory deception; (3) In view of the problem of false target splitting caused by multi-source error coupling, this invention considers the impact of multi-source error coupling on the trajectory deception effect. In the case of radar site error, UAV jitter error and DRFM forwarding delay error, by controlling the error within a reasonable range, it ensures that even if there is an error, the average splitting distance of the false target is still less than the range resolution of the network radar, and ensures the deception stability in complex battlefield environment. It can ensure that the false target does not split and the deception does not fail in complex battlefield environment through the same source verification mechanism based on the "rank 2 criterion" of the network radar, solve the problem of insufficient interference stability caused by the error in the existing technology, and improve the practical adaptability of cooperative interference. Attached Figure Description

[0054] Figure 1 This is a flowchart of the method of the present invention;

[0055] Figure 2 This is a common patrol route pattern for early warning aircraft;

[0056] Figure 3 Spatial distribution of radar, drones, and false flight paths;

[0057] Figure 4 The result of the optimized design for the flight altitude of the drone swarm in simulation scenario one;

[0058] Figure 5 The distance between the drone swarm and each radar unit in simulation scenario one;

[0059] Figure 6The heading angle optimization design results for the UAV swarm in simulation scenario one;

[0060] Figure 7 The result of the pitch angle optimization design for the UAV swarm in simulation scenario one;

[0061] Figure 8 The results of the speed optimization design for the drone swarm in simulation scenario one;

[0062] Figure 9 The acceleration optimization design results for the drone swarm in simulation scenario one;

[0063] Figure 10 The RCS three-dimensional distribution of a certain fighter jet (HH polarization, 4Hz);

[0064] Figure 11 The three-dimensional RCS distribution of a certain reconnaissance aircraft (HH polarization, 4Hz);

[0065] Figure 12 The DRFM relay power of UAVs 1-3 to radar 1;

[0066] Figure 13 The DRFM relay power of UAVs 4-6 to radar 2;

[0067] Figure 14 The DRFM relay power of UAVs 7-9 to radar 3;

[0068] Figure 15 This is a rendering of a radar image displaying a false flight path.

[0069] Figure 16 The distance between the false waypoints of the early warning aircraft actually generated in simulation scenario two;

[0070] Figure 17 The distance between the false waypoints of fighter jet 1 actually generated in simulation scenario 2;

[0071] Figure 18 The distance between the fake flight paths of fighter jet 2 actually generated in simulation scenario 2. Detailed Implementation

[0072] The structure and working process of the present invention will be further described below with reference to the accompanying drawings.

[0073] Based on the needs of practical engineering applications, this invention uses a genetic algorithm to solve the optimization model, obtaining the UAV flight parameters and DRFM forwarding power that meet the constraints. Thus, under the premise of meeting the pre-set UAV dynamic constraints and false target RCS fluctuation characteristics requirements and generating reasonable high-fidelity false tracks, it achieves cooperative track deception interference against networked radars.

[0074] This invention considers using M UAVs to implement high-fidelity cooperative jamming against a networked radar system composed of N pulse radars. This jamming strategy integrates three core elements: tactical decoy trajectory design, dynamic RCS simulation, and energy consumption optimization, aiming to effectively disrupt the target detection, identification, and situational awareness processes of the networked radar. It assumes that key parameters of each enemy radar (such as radar location, carrier frequency, range resolution, etc.) are already obtained, and that the radar locations, initial UAV positions, and tactical characteristics of the preset decoy targets are known, and that UAV flight and decoy trajectory motion satisfy dynamic constraints. Guided by trajectory deception effects, this invention aims to minimize the total energy consumption of the UAV swarm by jointly optimizing the configuration of UAV trajectory parameters and DRFM forwarding power, under the condition of limited UAV flight energy consumption and DRFM forwarding energy consumption. Considering the coupled effects of radar site errors, UAV jitter errors, and DRFM forwarding delay errors, a high-fidelity cooperative jamming mathematical model for UAV swarms oriented towards networked radar is established to ensure that the decoy trajectories pass the networked radar homology check, improving the tactical rationality and consistency of RCS fluctuation characteristics of the decoy targets.

[0075] like Figure 1 As shown, the method of the present invention includes the following steps:

[0076] 1. Construct a system model;

[0077] (1) Constructing a UAV dynamics model; deriving key parameters of the UAV's trajectory by considering the spatial relationship between the radar, the UAV, and the decoy target. First, the mathematical expression of the positional relationship between the three is determined. Combining the pitch and azimuth angles of the line connecting the radar and the decoy target, the distance between the radar and the UAV, and parameters such as the UAV's speed, heading angle, and pitch angle, the relationship is derived. Then, based on this positional relationship expression, the key parameter expressions of the UAV's trajectory are further derived. This model can determine the azimuth and pitch angles of the decoy target on the radar's line of sight by considering the relative spatial relationship between the three. Combined with the time-domain parameters of the deception signal, it can also determine the radial distance and relative altitude of the decoy target relative to the radar, providing a dynamic basis for subsequent UAV motion control.

[0078] The key parameters of the false targets generated in the track deception mission are jointly characterized by the relative spatial position relationship between the radar and the UAV, and the time-domain parameters of the deception signal. Among them, the relative spatial position relationship between the radar and the UAV directly determines the azimuth and elevation angles of the false target in the radar's line of sight; while the time-domain parameters of the deception signal determine the radial distance and relative altitude of the false target relative to the radar.

[0079] Based on the positional relationship between the radar, the drone, and the decoy target, we can deduce:

[0080] (1)

[0081] in, Indicates to Differentiating, Indicates to Differentiating, Indicates to Differentiating, Indicates the speed of the drone. Indicates the heading angle of the drone. Indicates the drone's pitch angle. The elevation angle of the line connecting the radar and the false target. The azimuth angle of the line connecting the radar and the false target. This refers to the distance between the radar and the drone.

[0082] The key parameters of the UAV's trajectory can be derived from equation (1), and their expressions are as follows:

[0083] (2)

[0084] (2) Construct an UAV energy consumption model. The total UAV energy consumption ignores the negligible communication energy consumption and only includes UAV propulsion energy consumption and DRFM relay energy consumption. Among them, UAV propulsion energy consumption consists of two parts: the work done to overcome air resistance and the change in kinetic energy. It is necessary to construct the original expression by combining flight time, flight speed, acceleration, total mass of UAV, gravitational acceleration and other inherent parameters. When the duration of the flight path is longer than the time threshold, the change in kinetic energy is ignored to simplify the original expression of propulsion energy consumption. DRFM relay energy consumption is the sum of the relay signal power of the DRFM carried by the UAV at each moment. Its calculation expression is constructed by accumulating the relay power at each moment, and finally the total energy consumption of the UAV swarm is obtained.

[0085] The total energy consumption of a fixed-wing UAV consists of propulsion energy consumption, DRFM energy consumption, and communication energy consumption. Communication energy consumption is relatively small compared to the total energy consumption and is therefore negligible here. Propulsion energy consumption arises from the energy consumed to overcome air resistance and changes in kinetic energy, and can be specifically expressed as:

[0086] (3)

[0087] in, Energy consumption for drone propulsion For flight duration, For flight speed, For acceleration, for transpose, For the total mass of the drone, It is the acceleration due to gravity. , These are the initial and final velocity vectors, respectively. For modulo operation, , These are inherent parameters of the drone, related to weight, wing area, air density, etc., and their expression is:

[0088] (4)

[0089] (5)

[0090] in, air density, Zero lift-drag coefficient, For wing reference area, Oswald efficiency factor For the wing aspect ratio, For the total mass of the drone, This is the acceleration due to gravity.

[0091] As can be seen from equation (3), the propulsion energy consumption of the UAV mainly consists of two parts: the work done to overcome air resistance and the change in kinetic energy. The former is the energy required by the engine to overcome air resistance, which is related to the flight speed and acceleration. The latter is the change in kinetic energy caused by the difference between the initial and final speeds of the UAV, which is only related to the initial and final speeds and has nothing to do with the intermediate trajectory.

[0092] When the trajectory lasts for a relatively long time, the change in kinetic energy can be ignored, and equation (3) can be simplified to:

[0093] (6)

[0094] (3) Design of false flight paths;

[0095] Based on the dynamic characteristics, RCS characteristics, and scenario adaptability of the false target, false flight paths are designed. The design references the tactical movement characteristics of real aircraft such as early warning aircraft and fighter jets. Runway-shaped paths are designed for early warning aircraft; paths with maneuvering characteristics are designed for fighter jets, including initial straight-line level flight, mid-course longitudinal maneuvers, and terminal lateral maneuvers, matching their strong tactical purpose and high maneuver complexity. During the design process, it is ensured that the paths conform to the dynamic characteristics of modern aircraft, so that the generated false paths possess the characteristics of real targets, thus improving the credibility of the false paths.

[0096] Designing a false track is a crucial step in track deception, requiring comprehensive consideration of factors such as the dynamic characteristics, RCS (Radar Cross Section) of the false target, and scene adaptability. A well-designed false track must meet certain dynamic characteristics, conform to the track characteristics and RCS of modern aircraft, and enable the generated false track to possess the track features of a real target. This increases the credibility of the false track and ensures that it can penetrate the radar's discrimination system, achieving the intended deception.

[0097] Considering a scenario where one early warning aircraft coordinates with two fighter jets, the patrol routes of the early warning aircraft typically fall into three common patterns: runway-shaped, figure-eight-shaped, and circular. The common patrol route patterns for early warning aircraft are as follows: Figure 2 As shown. O is the origin of the coordinate system, and A, B, C, and D are the turning points of the early warning aircraft's patrol route. If the early warning aircraft patrols along a runway-shaped route, it needs to follow... Flight, parallel flight path length Turning diameter The flight speed when patrolling along the runway-shaped flight path is If patrolling along a figure-eight pattern, it is necessary to follow... Flight, length of intersecting routes The angle between parallel lines and intersecting lines The flight speed when patrolling in a figure-eight pattern is If the turning speed of the early warning aircraft on both patrol routes is... The equivalent straight-line route length is The time required for an early warning aircraft to complete one lap along runway-shaped and figure-eight-shaped routes is:

[0098] (7)

[0099] in, It is the time required for an early warning aircraft to fly one lap along the runway-shaped flight path. It is the time required for an early warning aircraft to fly one circle in a figure-eight pattern. At that time, the runway-shaped and figure-eight-shaped routes degenerated into circular routes.

[0100] Fighter jets do not have fixed flight paths, but their flight paths are characterized by strong tactical objectives and high maneuverability. Based on these characteristics, false flight paths can be designed.

[0101] (4) Simulation of RCS characteristics of false tracks;

[0102] By calculating the target attitude angle and radar observation angle, combined with 3D RCS data, the required forwarding power is calculated to simulate RCS fluctuation characteristics. The radar line-of-sight vector is obtained from the coordinate positions of the radar and the UAV. Then, a rotation matrix is ​​used to convert the line-of-sight vector into a vector in the target coordinate system, obtaining the RCS angle of the radar-observed target. RCS data of different targets at specific frequencies are read, and the azimuth, elevation, and RCS values ​​for the corresponding frequencies are selected. An RCS matrix indexed by angles is constructed and normalized. Based on the attitude angle, the corresponding weight value is found in the normalized RCS matrix, and the optimal forwarding power under the current attitude is calculated, achieving dynamic simulation of the RCS fluctuation characteristics of a real target.

[0103] Radar Cross Section (RCS) is a measure of a target's ability to scatter radar electromagnetic waves and a key parameter for radar target identification. The RCS of false tracks generated by traditional DRFM jamming is typically fixed, while the RCS of a real target exhibits inherent fluctuations and is positively correlated with its physical size. Radar can infer the target size from the echo intensity. Furthermore, the RCS of a real target varies with its attitude and the observation angle. If false tracks do not simulate RCS fluctuations, even with perfect position and velocity deception, a single radar can identify false targets through RCS statistical characteristics, without relying on radar networks. Therefore, it is necessary to conduct in-depth research on how to ensure that false tracks exhibit consistent RCS characteristics across multiple radars in track deception.

[0104] Assuming target roll angle If the target's flight attitude angle is in the same direction as the target's flight velocity, then the target's flight velocity is... Then the target's flight attitude angle can be expressed as:

[0105] (8)

[0106] in, For the target flight speed modulus, , The azimuth angle of the line connecting the radar and the target. The component of flight velocity along the x-axis. Let y be the component of the flight velocity along the y-axis. Let be the component of the flight velocity in the z-axis direction, when , In the first quadrant, when , In the second quadrant, when , In the third quadrant, when , In the fourth quadrant, The elevation angle is the line connecting the radar and the target.

[0107] The radar's observation angle of the target is consistent with the line of sight (LOS). In track deception, the LOS can be obtained from the positional relationship between the radar and the UAV. If the radar coordinates are... The drone's coordinates are ,

[0108] Then the line of sight vector It can be represented as:

[0109] (9)

[0110] in, Let x be the component of the line-of-sight vector in the x-direction. Let be the component of the line-of-sight vector in the y-direction. This represents the component of the line-of-sight vector in the z-direction.

[0111] The RCS angle of a radar-observed target is the "azimuth" and "elevation" angles of the line-of-sight vector in the target coordinate system. It is necessary to convert the line-of-sight vector into a vector in the target coordinate system. :

[0112] (10)

[0113] in, Let x be the component of the vector in the target coordinate system along the x-direction. Let be the y-component of the vector in the target coordinate system. Let be the z-component of the vector in the target coordinate system. for transpose, For rotation matrix, , The rotation matrix is ​​the yaw angle. The rotation matrix is ​​the pitch angle. Let be the rotation matrix of the roll angle, when When, it can be represented as:

[0114] (11)

[0115] 2. Model construction optimization: With the goal of minimizing the total energy consumption of the UAV swarm, the UAV dynamics constraints and DRFM forwarding power constraints as constraints, and the flight heading angle as the optimization variable, a complete multi-parameter coupled optimization model is established.

[0116] This invention studies a high-fidelity cooperative jamming method for UAV swarms against networked radar systems. The proposed method aims to minimize the flight energy consumption of the UAV swarm while achieving track deception and simulating the RCS characteristics of the real target using false targets, thereby improving the deception effect on networked radar systems. First, considering strict UAV dynamics constraints and energy consumption models, the method is modeled as a multi-parameter coupled optimization model with the goal of minimizing the flight energy consumption of the UAV swarm. Second, a genetic algorithm is used to solve the model to obtain the energy-optimal UAV flight parameters and the power parameters for simulating the RCS of the false target under different error conditions.

[0117] From equation (6), it can be seen that the simplified flight energy consumption of the fixed-wing UAV can be expressed as:

[0118] (12)

[0119] in, Total flight energy consumption, Total duration for Time of the first The flight speed of the drone for Time of the first The acceleration of the drone For the first A time interval, For modulo operation, , These are inherent parameters of the drone.

[0120] To simulate the RCS characteristics of a false flight path, it is necessary to calculate the radar's observation angle of the false target and the false target's own attitude angle at each moment. This yields the corresponding RCS value. The UAV swarm can then simulate the RCS characteristics of the false target by controlling the transponder power. The energy required by its onboard DRFM (Radio Frequency Imaging) is also considered. This can be expressed as the sum of the forwarding power at each moment:

[0121] (13)

[0122] in, For DRFM energy consumption, for Time of the first The DRFM relay power carried by the drone.

[0123] The high-fidelity trajectory deception optimization model for UAV swarms based on energy consumption optimization is expressed as follows:

[0124] (14)

[0125] in, The total energy consumption of the drone. , Energy consumption for drone propulsion For DRFM's energy consumption, For the instantaneous speed of the drone, The flight altitude of the drone. This is the yaw angle of the drone. The pitch angle of the drone. For the acceleration of the drone, The relay signal power of the UAV equipped with DRFM at time k. This is the upper limit of flight altitude. This is the lower limit of flight altitude. For acceleration constraints, For pitch angle constraints, For power constraints, The minimum flight speed of the drone. The yaw angle of the drone at the previous moment. This represents the pitch angle of the drone at the previous moment.

[0126] 3. A genetic algorithm is used to solve the optimization model. The heading angle parameter is optimized based on the genetic algorithm. At the initial moment, the heading angle of the UAV is matched with the initial movement direction of the false target. At non-initial moments, the optimal heading angle adjustment is obtained through algorithm iteration. The adjustment is then superimposed to obtain the optimal heading angle at the next moment. Then, the UAV motion parameters are updated and constraints are checked. The predicted motion parameters are calculated based on the optimized heading angle to verify whether all constraints are met. If not, the optimization is repeated. Finally, multi-source random error is introduced to back-infer the position of the false target in order to evaluate the deception effect.

[0127] To address the energy consumption optimization problem of UAV swarms during track deception, considering that this optimization problem is a complex one involving nonlinearity, multiple constraints, and multivariate coupling, with a large number of local optima in the solution space, traditional solution methods are complex and prone to getting trapped in local optima. The GA algorithm, through parallel search by multiple individuals in the population, can explore a large range of solution spaces and has a higher probability of finding the globally optimal or near-globally optimal heading angle change, thus avoiding getting trapped in local optima. In addition, the GA algorithm can flexibly handle nonlinearity and complex constraints. The characteristics of GA are highly adapted to this type of problem. Therefore, this invention utilizes the GA algorithm to solve this optimization problem.

[0128] The specific solution steps are as follows:

[0129] Step 1: System Initialization and Track Generation. First, define core simulation parameters, including basic configurations such as the number of UAVs, the number of radars, and the deception duration. Simultaneously, set constraints on UAV maneuverability, including the maximum range of heading angle variation, maximum range of pitch angle variation, altitude boundaries, velocity thresholds, and maximum acceleration. Define the position coordinates and range resolution parameters of the networked radars. Three types of false target tracks are pre-generated: the first type is a runway-shaped track at a fixed altitude, achieving periodic movement through segmented calculation of the positions during straight flight and semi-circular turns; the second and third types are tracks with maneuvering characteristics, both including initial straight-line level flight, mid-course longitudinal maneuvering, and terminal lateral maneuvering. The initial position is set at 1 / 3 of the line connecting the radar and the false target. Based on the generated initial position sequence, the instantaneous velocity vector of each false target is calculated using the finite difference method. Finally, the UAV's initial position is deployed at 1 / 3 of the line connecting the corresponding radar and the initial position of the false target.

[0130] Step 2: Optimize the UAV's heading angle parameters based on the GA algorithm. Initially, the UAV's heading angle is set to the initial direction of motion of the corresponding false target. If not at the initial moment, the current UAV position, the current and next positions of the false target, the current heading angle, and all constraints are input. The optimal heading angle adjustment is obtained through iterative optimization using a genetic algorithm. This adjustment is then added to the current heading angle to form the optimal heading angle for the next moment.

[0131] Step 3: Update and check UAV motion parameters. Substitute the optimized heading angle into equation (2), and calculate the predicted position, velocity, and pitch angle for the next time step, combining the current position and velocity of the UAV. Check for constraint violations and verify whether the predicted state meets the preset constraints such as altitude boundary and velocity threshold. If the predicted state meets all constraints, update the UAV position and proceed to the next time step; if there are constraint violations, reject the current optimization result and return to step 2 to re-execute the genetic algorithm optimization until a feasible heading angle that meets all constraints is obtained.

[0132] Step 4: Calculation of the radar's observation angle of the target and the target's own attitude angle. To simulate the actual scenario of trajectory deception with errors, random time delay error, random radar site error, and random UAV jitter error are introduced respectively. Based on the time delay data with errors and the actual positions of the UAV and radar, the position of the false target observed by the radar is inferred, and the distance distribution between the actually generated false trajectory points is analyzed to evaluate the deception effect. The flight attitude angle is calculated by equation (8). Through the transformation from the geodetic coordinate system to the body coordinate system, with the false target velocity vector as a reference, the azimuth and pitch angles of the radar relative to the false target at each moment are calculated to provide angle basis for subsequent RCS query.

[0133] Step 5: RCS-based forwarding power calculation. Read RCS data for different targets at specific frequencies, filter the azimuth, pitch, and RCS values ​​corresponding to the target frequencies, construct an RCS matrix indexed by angles, and normalize it to obtain a weight matrix in the 0-1 interval. Set the UAV's rated forwarding power. Based on the attitude angles calculated in Step 4, find the corresponding weight value in the normalized RCS matrix, multiply the rated power by this weight, and obtain the optimal forwarding power for the current attitude, achieving power allocation based on RCS matching.

[0134] Finally, we can obtain the various motion parameters of the drone swarm and the forwarding power distribution of DRFM.

[0135] The high-fidelity cooperative jamming system for UAV swarms targeting networked radar, as described in this invention, includes:

[0136] The UAV dynamics model building unit is used to construct the UAV dynamics model and derive the key parameters of the UAV's trajectory by the spatial relationship between radar, UAV and false target.

[0137] The UAV energy consumption model building unit is used to build a UAV energy consumption model. The total UAV energy consumption includes UAV propulsion energy consumption and DRFM relay energy consumption.

[0138] The false trajectory design unit is used to design false trajectories based on the dynamic characteristics, RCS characteristics, and scene adaptability factors of false targets.

[0139] The RCS characteristic simulation unit is used to calculate the required forwarding power by calculating the target attitude angle and radar observation angle, combined with RCS three-dimensional data, to simulate RCS fluctuation characteristics. It includes: obtaining the radar line-of-sight vector from the coordinate positions of the radar and the UAV, then converting the line-of-sight vector into a vector in the target coordinate system through a rotation matrix to obtain the RCS angle of the radar-observed target, reading RCS data of different targets at specific frequencies, filtering the azimuth angle, elevation angle and RCS values ​​of the corresponding frequencies, constructing an RCS matrix indexed by angle and performing normalization processing, finding the corresponding weight value in the normalized RCS matrix according to the attitude angle, calculating the optimal forwarding power under the current attitude, and realizing the dynamic simulation of the RCS fluctuation characteristics of the real target.

[0140] The optimization model building unit is used to establish a complete multi-parameter coupled optimization model with the goal of minimizing the total energy consumption of the UAV swarm, the constraints of UAV dynamics and DRFM forwarding power, and the flight heading angle as the optimization variable.

[0141] The optimization model solving unit is used to solve the optimization model using a genetic algorithm. Based on the genetic algorithm, the heading angle parameter is optimized. At the initial moment, the UAV's heading angle is matched with the initial movement direction of the false target. At non-initial moments, the optimal heading angle adjustment is obtained through algorithm iteration. The adjustment is then superimposed to obtain the optimal heading angle for the next moment. Then, the UAV's motion parameters are updated and constraints are checked. The predicted motion parameters are calculated based on the optimized heading angle, and it is verified whether all constraints are met. If not, the optimization is repeated. Finally, multi-source random error is introduced to back-infer the position of the false target to evaluate the deception effect.

[0142] The present invention also provides an electronic device, comprising:

[0143] Memory, used to store computer programs;

[0144] A processor for executing the computer program to implement the method.

[0145] The present invention also provides a non-volatile storage medium for storing a computer program, wherein the computer program implements the method described thereon when executed by a processor.

[0146] The present invention also provides a computer program product, including a computer program / instructions that, when executed by a processor, implement the method described.

[0147] Simulation results:

[0148] To verify the effectiveness of the energy-optimized high-fidelity trajectory deception algorithm for UAV swarms, this study focuses on... A networked radar system composed of multiple radars is used to carry out deception and jamming. Assuming that the signal processing time of DRFM is negligible, the networked radar uses the rank 2 criterion for "same source detection". The initial position of each UAV is set at 1 / 3 of the line connecting the radar and the UAV. The specific parameter settings are shown in Table 1.

[0149] Table 1 Simulation Parameter Settings

[0150]

[0151] (1) Simulation Scenario 1:

[0152] In simulation scenario 1, the positions of the three radars are set as follows: , , The false flight path starting point of the early warning aircraft is set to The false flight path starting point of fighter jet 1 is set to The false flight path starting point of fighter jet 2 is set to The AWACS aircraft's decoy trajectory was set as a runway-shaped trajectory with constant speed and altitude. The two fighter jets' decoy trajectories were generally in a state of acceleration, followed by dive and turn maneuvers, before advancing to the flanks. The spatial distribution of radar, UAVs, and decoy trajectories was as follows: Figure 3 As shown. The parameters of the drone are as follows: Figure 4 Middle (a) to (c) ~ Figure 9 As shown in (a) to (c).

[0153] from Figure 3 It can be seen that nine drones created three false flight paths for the three radars. Each drone was always on the line connecting the false flight path and the radar, meeting the conditions for forming an effective false flight path. The false flight path was presented in the form of a formation of one early warning aircraft and two fighter jets. The early warning aircraft patrolled and reconnoited at high altitude along a runway-shaped path behind, while the two fighter jets moved forward and circled around to the flanks. Therefore, this false flight path is more deceptive than straight or parabolic false flight paths and can improve the effect of deception and jamming.

[0154] Depend on Figure 4 Middle (a) to (c) ~ Figure 9 The flight parameters of the UAV swarms in (a) to (c) show that the swarms fly at altitudes between 3 and 5 km with gradual altitude changes, satisfying the constraints. The heading angle, pitch angle, and acceleration of the swarms increase suddenly at certain times, while the changes are smaller at other times. This is because timely angle adjustments are needed to complete turns or climbs. Figure 3 It can be seen that even if the flight parameters of the drone swarm do not meet the constraints at certain times, it will not cause the flight trajectory of the drone swarm to become chaotic, nor will it affect the formation of the entire false flight path. The speed of the drone swarm basically meets the constraint that it is greater than the minimum flight speed of 20 m / s.

[0155] Figure 10 and Figure 11 The figures show the 3D RCS distribution of a fighter jet and a reconnaissance aircraft, respectively. It can be seen from the figures that the RCS value of a real target varies significantly with angle. Therefore, the RCS of a real target can be simulated by controlling the amplitude of the relay signal power. The relay power of the DRFM of the UAV swarm to the three radars is as follows: Figure 12~Figure 14 As shown. Figure 15 Figures (a) to (c) show the polar coordinate distribution of false tracks under the detection perspectives of radars 1 to 3. This figure illustrates the differences in detection characteristics of the "same batch of false tracks" under different radar perspectives.

[0156] (2) Simulation Scenario 2:

[0157] Based on the boundary conditions for track deception under comprehensive error, it can be calculated that when the network radar resolution is set to 200m, effective false tracks can be formed by controlling the radar site error within 20m, the UAV jitter error within 30m, and the DRFM forwarding delay error within 200ns. Therefore, in simulation scenario 2, a random radar site error within 20m, a UAV jitter error within 30m, and a DRFM forwarding delay error within 200ns are simultaneously introduced, and the distances between each false track point at each time point are plotted as follows. Figure 16~Figure 18 As shown.

[0158] Depend on Figure 16~Figure 18 It can be seen that when all three types of errors exist simultaneously, and the radar site error is within 20m, the UAV jitter error is within 30m, and the DRFM forwarding delay error is within 200ns, the distance between the false waypoints of fighter jets 1 and 2 is less than the range resolution of the networked radar (200m), thus forming effective false waypoints. Since the networked radar uses the rank-2 criterion for waypoint fusion, it can successfully deceive the networked radar by forming effective waypoints for two of the three radars at the same time. Even if the distance between some false waypoints of the AWACS exceeds the range resolution of the networked radar, effective false waypoints can still be formed. Therefore, the results of simulation scenario 2 show that by controlling the radar site error, UAV jitter error, and DRFM forwarding delay error within a certain range, this method can effectively deceive the networked radar system.

[0159] The working principle and process of this invention:

[0160] This invention proposes a high-fidelity cooperative jamming method for UAV swarms targeting networked radar. It integrates three core mechanisms: tactical false trajectory design, dynamic RCS characteristic simulation, and global energy consumption optimization. Combined with a genetic algorithm to solve a multi-parameter coupled optimization model, it achieves stable, highly realistic, and long-endurance cooperative trajectory deception jamming while considering the impact of multi-source errors. Specifically, after obtaining key parameters such as enemy radar position and carrier frequency, clarifying the initial position of the UAV and the tactical characteristics of the false target, and satisfying UAV dynamic constraints, four system models are first constructed: First, a UAV dynamic model, which clarifies the UAV's motion state through the positional relationship of the three elements; second, an energy consumption model, which integrates UAV propulsion energy consumption and DRFM relay energy consumption, ignoring the negligible communication energy consumption; third, false trajectory design, which generates three types of highly realistic trajectories by referencing the patrol routes of early warning aircraft and the tactical maneuver characteristics of fighter jets; and fourth, dynamic RCS simulation, which calculates the target attitude angle and radar observation angle, combined with three-dimensional RCS data, to calculate the corresponding power to simulate RCS fluctuation characteristics. Based on this, an optimization model incorporating UAV dynamics constraints was established with the goal of minimizing the total energy consumption of the UAV swarm. The flight heading angle was used as the optimization variable, and a genetic algorithm was employed to solve the model. Simulation results show that the UAV swarm, working collaboratively according to the optimized parameters, consistently stays on the line connecting the false track and the radar, dynamically adjusting its flight state and relay power to generate false tracks with realistic tactical characteristics and RCS fluctuations. Subsequently, radar site error, UAV jitter error, and DRFM delay error were introduced, and these errors were controlled within a reasonable range. The splitting distance of the generated false targets is less than the radar range resolution, and the generated false tracks can pass the homology test based on the "rank-2 criterion" of the networked radar, demonstrating their effectiveness in generating false tracks.

Claims

1. A high-fidelity cooperative jamming method for UAV swarms targeting networked radar, characterized in that, Includes the following steps: A dynamic model of the UAV is constructed, and the key parameters of the UAV's trajectory are derived by the spatial relationship between the radar, the UAV and the false target. Construct an energy consumption model for drones, where the total energy consumption of drones includes drone propulsion energy consumption and DRFM relay energy consumption; Based on the dynamic characteristics, RCS characteristics, and scene adaptability factors of the false target, a false trajectory is designed. By calculating the target attitude angle and radar observation angle, and combining RCS three-dimensional data, the required forwarding power is calculated to simulate RCS fluctuation characteristics. This includes: obtaining the radar line-of-sight vector from the coordinate positions of the radar and the UAV, then converting the line-of-sight vector into a vector in the target coordinate system through a rotation matrix to obtain the RCS angle of the radar-observed target, reading RCS data of different targets at specific frequencies, filtering the azimuth angle, elevation angle and RCS values ​​of the corresponding frequencies, constructing an RCS matrix indexed by angles and performing normalization processing, finding the corresponding weight value in the normalized RCS matrix according to the attitude angle, calculating the optimal forwarding power under the current attitude, and realizing the dynamic simulation of the RCS fluctuation characteristics of the real target. With the goal of minimizing the total energy consumption of the UAV swarm, and with UAV dynamics constraints and DRFM forwarding power constraints as constraints, and flight heading angle as the optimization variable, a complete multi-parameter coupled optimization model is established. A genetic algorithm is used to solve the optimization model. The heading angle parameter is optimized based on the genetic algorithm. At the initial moment, the UAV's heading angle is matched with the initial movement direction of the false target. At non-initial moments, the optimal heading angle adjustment is obtained through algorithm iteration. The adjustment is then superimposed to obtain the optimal heading angle for the next moment. Then, the UAV's motion parameters are updated and constraints are checked. The predicted motion parameters are calculated based on the optimized heading angle to verify whether all constraints are met. If not, the optimization is repeated. Finally, multi-source random error is introduced to back-infer the position of the false target to evaluate the deception effect.

2. The method according to claim 1, characterized in that, The positional relationship between the radar, the drone, and the decoy target is expressed as follows: ； in, Indicates to Differentiating, Indicates to Differentiating, Indicates to Differentiating, The elevation angle of the line connecting the radar and the false target. The azimuth angle of the line connecting the radar and the false target. The distance between the radar and the drone. Indicates the speed of the drone. Indicates the heading angle of the drone. Indicates the drone's pitch angle; The key parameters of the drone's trajectory are expressed as follows: 。 3. The method according to claim 1, characterized in that, The expression for the propulsion energy consumption of a drone is: ； in, Energy consumption for drone propulsion For flight time, For flight speed, For acceleration, for transpose, For the total mass of the drone, It is the acceleration due to gravity. , These are the initial and final velocity vectors, respectively. For modulo operation, , These are inherent parameters of the UAV; When the duration of the flight path exceeds the time threshold, the change in kinetic energy is negligible, and the above equation simplifies to: 。 4. The method according to claim 1, characterized in that, The expression for the target's flight attitude angles is: ； in, The azimuth angle of the line connecting the radar and the target. The component of flight velocity along the x-axis. The component of the flight velocity along the y-axis. The component of the flight velocity along the z-axis. For the target flight speed modulus, Indicates the target's flight speed. The elevation angle of the line connecting the radar and the target; The radar's observation angle of the target is consistent with the direction of the radar's line of sight; the line-of-sight vector... Represented as: ； in, , and The line-of-sight vectors are respectively in , and Components in direction; For radar coordinates, Coordinates of the drone; The RCS angle of a radar-observed target is the azimuth and elevation angles of the line-of-sight vector in the target coordinate system. This involves converting the line-of-sight vector into a vector in the target coordinate system. : ； in, , , The vectors in the target coordinate system are respectively , and Components in direction, for transpose, For rotation matrix, , This is the rotation matrix for the azimuth angle. The rotation matrix is ​​the pitch angle. Let be the rotation matrix for the attitude angle.

5. The method according to claim 1, characterized in that, The optimized model expression is: ； in, This represents the total energy consumption of the drone, including drone propulsion energy consumption and DRFM relay energy consumption. The yaw angle of the drone. The pitch angle of the drone. For the instantaneous speed of the drone, The flight altitude of the drone. This is the upper limit of flight altitude. This is the lower limit of flight altitude. The yaw angle of the drone at the current moment. The yaw angle of the drone at the previous moment. For acceleration constraints, The current pitch angle of the drone. The pitch angle of the drone at the previous moment. For pitch angle constraints, For the acceleration of the drone, The minimum flight speed of the drone. The relay signal power of the UAV equipped with DRFM at time k. For power constraints.

6. The method according to claim 1, characterized in that, Solving the optimization model using a genetic algorithm includes the following steps: (1) System initialization and trajectory generation: First, define the core simulation parameters, including the number of UAVs, the number of radars, and the basic configuration of deception duration. At the same time, set the UAV maneuverability constraints, including the maximum heading angle variation range, the maximum pitch angle variation range, the altitude boundary, the speed threshold, and the maximum acceleration constraint conditions. Clarify the position coordinates and range resolution parameters of the network radars. Pre-generate three types of false target trajectories: The first type is a runway-shaped trajectory at a fixed altitude, which achieves periodic movement by calculating the position of the straight flight and semi-circular turning stages in segments. The second and third types are trajectories with maneuverability characteristics, both of which include the initial straight flight, the mid-stage longitudinal maneuver, and the final stage lateral maneuver. The initial position is set at 1 / 3 of the line connecting the radar and the false target. Based on the generated initial position sequence, the instantaneous velocity vector of each false target is calculated using the difference method. Finally, the initial position of the UAV is deployed at 1 / 3 of the line connecting the initial position of the corresponding radar and the false target. (2) Optimize the heading angle parameters of the UAV based on the GA algorithm: At the initial moment, the heading angle of the UAV is set to the initial movement direction of the corresponding false target. If it is not the initial moment, the current position of the UAV, the current and next position of the false target, the current heading angle and all constraints are input. The optimal heading angle adjustment is obtained by iterative optimization through the genetic algorithm. The adjustment is added to the current heading angle to form the optimal heading angle at the next moment. (3) Update and check the motion parameters of the UAV: ​​Based on the optimized heading angle, combined with the current position and velocity state of the UAV, calculate the predicted position, velocity and pitch angle of the next moment; check for constraint violations and verify whether the predicted state meets the preset constraints of altitude boundary and velocity threshold; if the predicted state meets all the constraints, update the UAV position and enter the next time step; if there are constraint violations, reject the current optimization result and return to step two to re-execute the genetic algorithm optimization until a feasible heading angle that meets all constraints is obtained; (4) Calculation of radar observation angle and target attitude angle: introduce random time delay error, random radar site error and random UAV jitter error respectively. Based on the time delay data with error and the actual position of UAV and radar, the position of false target observed by radar is inferred. The distance distribution between the actual generated false track points is analyzed to evaluate the deception effect. Calculate the flight attitude angle. Through the transformation from the geodetic coordinate system to the body coordinate system, with the false target velocity vector as a reference, calculate the azimuth and pitch angle of the radar relative to the false target at each moment. (5) Calculation of forwarding power based on RCS: Read the RCS data of different targets at specific frequencies, filter the azimuth angle, pitch angle and RCS value corresponding to the target frequency, construct the RCS matrix indexed by angle and normalize it to obtain the weight matrix; set the rated forwarding power of the UAV, find the corresponding weight value in the normalized RCS matrix according to the attitude angle calculated in step (4), multiply the rated power with the weight to obtain the optimal forwarding power under the current attitude, and realize the power allocation of RCS matching.

7. A high-fidelity cooperative jamming system for UAV swarms targeting networked radar, characterized in that, include: The UAV dynamics model building unit is used to construct the UAV dynamics model and derive the key parameters of the UAV's trajectory by the spatial relationship between radar, UAV and false target. The UAV energy consumption model building unit is used to build a UAV energy consumption model. The total UAV energy consumption includes UAV propulsion energy consumption and DRFM relay energy consumption. The false trajectory design unit is used to design false trajectories based on the dynamic characteristics, RCS characteristics, and scene adaptability factors of false targets. The RCS characteristic simulation unit is used to calculate the required forwarding power by calculating the target attitude angle and radar observation angle, combined with RCS three-dimensional data, to simulate RCS fluctuation characteristics. It includes: obtaining the radar line-of-sight vector from the coordinate positions of the radar and the UAV, then converting the line-of-sight vector into a vector in the target coordinate system through a rotation matrix to obtain the RCS angle of the radar-observed target, reading RCS data of different targets at specific frequencies, filtering the azimuth angle, elevation angle and RCS values ​​of the corresponding frequencies, constructing an RCS matrix indexed by angle and performing normalization processing, finding the corresponding weight value in the normalized RCS matrix according to the attitude angle, calculating the optimal forwarding power under the current attitude, and realizing the dynamic simulation of the RCS fluctuation characteristics of the real target. The optimization model building unit is used to establish a complete multi-parameter coupled optimization model with the goal of minimizing the total energy consumption of the UAV swarm, the constraints of UAV dynamics and DRFM forwarding power, and the flight heading angle as the optimization variable. The optimization model solving unit is used to solve the optimization model using a genetic algorithm. Based on the genetic algorithm, the heading angle parameter is optimized. At the initial moment, the UAV's heading angle is matched with the initial movement direction of the false target. At non-initial moments, the optimal heading angle adjustment is obtained through algorithm iteration. The adjustment is then superimposed to obtain the optimal heading angle for the next moment. Then, the UAV's motion parameters are updated and constraints are checked. The predicted motion parameters are calculated based on the optimized heading angle, and it is verified whether all constraints are met. If not, the optimization is repeated. Finally, multi-source random error is introduced to back-infer the position of the false target to evaluate the deception effect.

8. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor for executing the computer program to implement the method as claimed in any one of claims 1 to 6.

9. A non-volatile storage medium, characterized in that, Used to store a computer program, wherein the computer program, when executed by a processor, implements the method as described in any one of claims 1 to 6.

10. A computer program product comprising a computer program / instructions, 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 6.

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