Decoy cover under the evaluation method and system of penetration effectiveness

Abstract

The present application provides a decoy under cover of the penetration effectiveness evaluation method and system, relates to the operational effectiveness evaluation technical field. The present application first determines the decoy strategy parameter, including the speed difference, the azimuth dispersion degree, the distance dispersion degree and the decoy quantity, extracts the defense environment parameter, namely the defense system deployment density and the detection ability. The data of the penetration is obtained through the simulation kinematics simulation, the evaluation index is constructed from the two dimensions of the defense party resource consumption efficiency and the attack party penetration ability, the weight is dynamically adjusted in combination with the detection ability of the defense system, and the comprehensive evaluation result is formed. The genetic algorithm is used for optimizing the decoy strategy parameter, the optimal strategy combination is searched in the preset parameter range, the sensitivity degree of each parameter to the evaluation result is calculated, the suggestion of preferentially adjusting the parameter with greater influence is given, and the strategy advantages and disadvantages are quantitatively evaluated by comparing the effectiveness difference between the new strategy and the optimal strategy, so that the decoy strategy parameter is optimized.

Description

Technical Field

[0001] This invention relates to the field of combat effectiveness assessment technology, specifically to a method and system for assessing penetration effectiveness under decoy cover. Background Technology

[0002] Faced with increasingly complex air defense and anti-missile systems, penetration operations rely more heavily on decoy technology. Against this backdrop, decoy technology has evolved from an auxiliary means to a core support for penetration operations. By simulating the signal characteristics of real units, such as radar reflection and infrared radiation, decoys can effectively disperse defensive firepower and interfere with target identification, becoming a key means to improve the success rate of penetration.

[0003] Traditional decoy deployments are mostly based on empirical threshold parameter settings, lacking a quantitative response mechanism for dynamic defense environments. They generally use static weight evaluation indicators, failing to incorporate the density distribution of defense units and the dynamic allocation characteristics of interception resources into the evaluation system. This results in a systematic deviation between kinematic simulation results and actual combat effects, leading to significant fluctuations in strategy effectiveness. In modern air defense and anti-missile systems, decoys need to simultaneously achieve the dual objectives of consuming defense resources and ensuring the penetration of real units. However, existing methods also lack a systematic quantitative evaluation of both. While increasing the number of decoys can improve the jamming effect, over-deployment can lead to excessive payload and soaring costs. However, there is a lack of a quantitative balance model for decoy consumption and defense resource consumption. How can we integrate resource efficiency indicators into the evaluation system to achieve the optimization goal of maximizing defense resource loss with minimum decoy cost?

[0004] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for evaluating the penetration effectiveness under decoy cover, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] The method for evaluating the effectiveness of penetration under decoy cover includes the following steps:

[0008] Step 1: Set the defense environment parameters, release the decoys according to the initially set decoy strategy parameters, perform kinematic simulation of the motion trajectories of the real units and the decoys according to the motion parameters of the real units and the decoy strategy parameters respectively, and launch the interception units according to the set interception parameters for the total number of detected decoys and real units.

[0009] Step 2: Perform kinematic simulation on the trajectory of the launched interceptor unit, and determine the result of the interception based on the kinematic simulation results of the trajectories of the real unit, the decoy, and the interceptor unit.

[0010] Step 3: Based on the results of multiple kinematic simulations, obtain the number of successful penetrations, the number of effective interceptions, the total number of interceptions, and the number of surviving decoys. Construct a defense resource consumption index based on the number of defense units per unit area, the total number of interceptions, the number of surviving decoys, and the number of decoys. Construct a penetration core effectiveness index based on the number of successful penetrations and the number of effective interceptions.

[0011] Step 4: Construct an initial effectiveness evaluation formula by weighted summation of the penetration core effectiveness index and the defense resource consumption index, and dynamically adjust the weights based on the average detection probability of the defense system;

[0012] Step 5: Using a genetic algorithm, the initially formulated bait strategy parameters are used as the input layer, and the initial effectiveness evaluation formula is used as the fitness function. The optimal bait strategy parameters and the corresponding initial effectiveness evaluation results are output. The penetration effectiveness is evaluated based on the initial effectiveness evaluation results of the new bait strategy parameters and the initial effectiveness evaluation results corresponding to the optimal bait strategy parameters.

[0013] Furthermore, based on the motion parameters of the real unit and the decoy strategy parameters, the method for kinematic simulation of the motion trajectories of the real unit and the decoy is as follows:

[0014] When performing kinematic simulation, the input decoy strategy parameters in the kinematic simulation environment include the velocity difference ΔV, azimuth dispersion θ, and distance standard deviation σ. d Given the number of decoys, n, and assuming all decoys have the same speed, the speed difference refers to the difference in movement speed between the decoys and the real units. The azimuth dispersion refers to the dispersion of the decoys' distribution in the azimuth angle with the real unit as the center. The distance standard deviation is the standard deviation of the distance between the decoys and the real unit, with the real unit as the reference. The method for calculating the number of defense units per unit area is as follows:

[0015]

[0016] In the formula, N defense ρ represents the total number of defense units deployed within the kinematic simulation area, A represents the area of ​​the kinematic simulation area, and ρ represents the number of defense units per unit area.

[0017] The formula used to construct the motion model of the real unit and the decoy is:

[0018]

[0019] In the formula, V tar V represents the velocity of the actual unit. decoy θ represents the velocity of the decoy.tar The heading angle of the real element is represented by θ, which is the angle between the velocity direction of the real element and the horizontal direction. decoy Let x(t) represent the heading angle of the decoy, that is, the angle between the direction of the decoy's velocity and the horizontal direction, and let x(t) represent the horizontal position of the real cell at the current moment. * (t) represents the horizontal position of the decoy at the current moment, Δt represents the time interval, and x(t+1) represents the horizontal position of the real cell at the next moment. * (t+1) represents the horizontal position of the decoy at the next moment, and y(t) represents the vertical position of the real cell at the current moment. * (t) represents the vertical position of the decoy at the current moment, and y(t+1) represents the vertical position of the real cell at the next moment. * (t+1) represents the vertical position of the decoy at the next moment.

[0020] Furthermore, regarding the total number of detected decoys and real units, the method for launching interception units based on the set interception parameters is as follows:

[0021] Using the coordinates of the defense unit as the initial position of the interception unit, and setting the total number of detected decoys and real units as n0, the position of the real unit or decoy detected by the defense system at the current moment is (x... target y Target The speed of the interception unit is V. D For each decoy or real unit that passes through, the coordinates of the meeting point between the interceptor unit and the decoy or real unit are (x... meet y meet The specific steps for calculating the coordinates of the meeting point are as follows:

[0022] Calculate the time it takes for the interceptor unit to reach the meeting point coordinates:

[0023]

[0024] In the formula, t1 represents the time it takes for the interception unit to reach the coordinates of the meeting point, and x Target y represents the horizontal position of the real unit or decoy detected by the defense system at the current moment. Target V represents the vertical position of the real unit or decoy detected by the defense system at the current moment. Target This indicates the speed of the decoy or real unit detected by the defense system at the current moment;

[0025] Calculate the time it takes for the decoy or real unit to reach the meeting point coordinates:

[0026]

[0027] In the formula, t2 represents the time it takes for the decoy or the real unit to reach the coordinates of the meeting point;

[0028] By solving the equations t1 = t2 simultaneously, the coordinates of the meeting point between the interceptor unit and the decoy or real unit can be calculated.

[0029] Calculate the launch angle of the interceptor unit:

[0030]

[0031] In the formula, α represents the launch angle of the interception unit.

[0032] Furthermore, kinematic simulation is performed on the trajectory of the launched interceptor unit. The method for determining the interception result based on the kinematic simulation results of the actual unit, decoy, and interceptor unit's trajectories is as follows:

[0033] Set detection threshold d thresh1 =1000, unit is meters, interception threshold d thresh2 The unit is meters, used to calculate the distance between the actual unit and the target:

[0034]

[0035] In the formula, d tar (t) represents the distance between the real cell and the target, x T Indicates the horizontal position of the target, y T Indicates the vertical position of the target;

[0036] When d tar (t) <d thresh1 The result of the interception is judged as a successful breakthrough;

[0037] Extract the positions pointing to the real unit and the decoy, i.e., the heading angle of the interception unit. For each interception unit, calculate the position in real time:

[0038]

[0039] In the formula, x D (t) represents the horizontal position of the interception unit at the current moment, y D (t) represents the vertical position of the interception unit at the current moment, x D (t+1) represents the horizontal position of the interception unit at the next moment, y D (t+1) represents the vertical position of the interception unit at the next moment, V D Indicates the speed of movement of the interception unit. This represents the direction angle of the intercepting unit at the current moment. The formula for calculating the direction angle of the intercepting unit at the current moment is:

[0040]

[0041] In the formula, xk (t) represents the horizontal position of the decoy or real unit being tracked by the interceptor unit, y k (t) represents the vertical position of the decoy or real unit being tracked by the interceptor unit;

[0042] When the interceptor is tracking a real cell, the distance between the interceptor and the real cell is calculated in real time:

[0043]

[0044] In the formula, D k，tar (t) represents the distance between the intercepting unit and the real unit;

[0045] When the interceptor unit is tracking a decoy, the distance between the interceptor unit and the decoy is calculated in real time:

[0046]

[0047] In the formula, D k，decoy (t) represents the distance between the interceptor unit and the decoy;

[0048] When D k，tar (t) <d thresh2 When the real unit is detected, the result of the interception is considered a valid interception. k，decoy (t) <d thresh2 At that time, the result of the interception is determined to be that the decoy has been discovered.

[0049] Furthermore, based on the results of multiple kinematic simulations, the number of successful penetrations, the number of effective interceptions, the total number of interceptions, and the number of surviving decoys are obtained. The method for constructing a defense resource consumption index based on the number of defense units per unit area, the total number of interceptions, the number of surviving decoys, and the number of decoys is as follows:

[0050] Set the number of kinematic simulations to M fz And M fz It is a positive integer ≥ 100;

[0051] The number of successful penetrations in each interception under all kinematic simulations is counted, and the average of the summed results is denoted as N. s ;

[0052] Calculate the number of surviving decoys in each kinematic simulation:

[0053] N dsurv =nn bfx

[0054] In the formula, N dsurv n represents the number of decoys that survive a single kinematic simulation. bfxThis represents the number of baits detected under the kinematic simulation. The average number of surviving baits after summing the numbers from each kinematic simulation is taken as the number of surviving baits, denoted as N. surv ;

[0055] The number of interceptions deemed valid under all kinematic simulations is counted, and the sum of these counts is averaged to determine the number of valid interceptions, denoted as N. hit ;

[0056] The number of interception units launched by the defense system under all kinematic simulations is counted, and the sum of the results is averaged to obtain the total number of interceptions, denoted as N. intercept ;

[0057] Defense resource consumption index:

[0058]

[0059] In the formula, i res This indicates the index of defense resource consumption.

[0060] Furthermore, the method for constructing the core penetration effectiveness index based on the number of successful penetrations, the total number of kinematic simulations, and the number of effective interceptions is as follows:

[0061] The formula used to calculate the probability of a successful penetration is:

[0062]

[0063] In the formula, P s Indicates the probability of a successful penetration;

[0064] The formula used to calculate the effective interception rate is:

[0065]

[0066] In the formula, P hit Indicates the effective interception rate;

[0067] Constructing a core effectiveness index for penetration defense:

[0068] I eff =P s ·(1-P hit )

[0069] In the formula, I eff This indicates the core effectiveness index for penetration defense.

[0070] Furthermore, a preliminary effectiveness evaluation formula is constructed by weighted summation of the penetration core effectiveness index and the defense resource consumption index. The weights are then dynamically adjusted based on the average detection probability of the defense system.

[0071] Calculate the average detection probability of the defense system under each kinematic simulation:

[0072]

[0073] In the formula, P ddet Let P represent the average detection probability of the defense system under each kinematic simulation, n represent the number of decoys under that kinematic simulation, and n0 represent the total number of decoys and real units detected. The total number of decoys and real units detected under each kinematic simulation is counted, and the sum of these results is averaged to obtain the average detection probability of the defense system, denoted as P. det ;

[0074] Extract the defense resource consumption index and the penetration core effectiveness index, and construct an initial effectiveness evaluation formula by weighted summation:

[0075] E=ω res ·I res +ω eff ·I eff

[0076] In the formula, E represents the initial effect assessment result, ω res ω represents the weighting coefficient of the defense resource consumption index. eff This represents the weighting coefficient of the penetration core effectiveness index, and ω res +ω eff =1;

[0077] Build defense strength indicators:

[0078]

[0079] In the formula, R d This represents the defense strength index, making ω eff =R d ω res =1-R d .

[0080] Furthermore, the method for outputting the optimal decoy strategy parameters and the corresponding initial evaluation results is as follows:

[0081] A genetic algorithm is used, where experts set the value range for each bait strategy parameter. Within each value range, the bait strategy parameter is uniformly and randomly selected, generating a total of 100 individuals. Each individual is composed of the bait strategy parameters.

[0082] C j =[ΔV j θ j , σ d，j n j ]

[0083] In the formula, Cj Let ΔV represent the j-th individual whose parameters consist of the bait strategy parameters. j Let θ represent the velocity difference of the j-th individual, which is composed of the decoy strategy parameters. j Let σ represent the azimuth dispersion of the j-th individual composed of decoy strategy parameters. d，j Let n represent the standard deviation of the distance of the j-th individual, which consists of the bait strategy parameters. j The number of decoys for the j-th individual composed of decoy strategy parameters is represented by j = 1, 2, ..., 100, where j represents the individual's index. For velocity difference, azimuth dispersion, and distance standard deviation, uniformly distributed random numbers are generated within a set range. For the number of decoys, random integers are generated within a set range. Kinematic simulations are performed on the parameters of each individual and the defense environment parameters to obtain the total number of interceptions, the number of surviving decoys, the number of successful penetrations, and the number of effective interceptions.

[0084] The maximum number of iterations is set to 200. The result of the initial evaluation formula is used as the fitness value. The trigger probability of crossover is set to 0.8 and the trigger probability of mutation is set to 0.05. Then, a tournament selection method is used. Each time, three individuals are randomly selected from the population, their fitness is compared, and the one with the highest fitness is retained to enter the parent pool. This process is repeated 100 times to generate the parent population. Arithmetic crossover is performed on the selected parents with a probability of 0.8 to generate two offspring. The weight factor of arithmetic crossover is randomly generated by the algorithm. For the number of decoys, an integer crossover method is used. For parents that have not triggered crossover (i.e., not selected), the offspring directly copy the parents. Gaussian mutation is used to mutate the decoy strategy parameters of each offspring with a probability of 0.05. When the decoy strategy parameters after mutation exceed the value range, they are truncated back to the nearest value range. The individual with the highest fitness among the parents and offspring is selected as the optimal decoy strategy parameters, and the corresponding initial evaluation value is recorded.

[0085] Calculate the sensitivity coefficients of the decoy strategy parameters to the initial effectiveness evaluation results:

[0086]

[0087] In the formula, S i Let represent the sensitivity coefficient of the i-th parameter, where i represents the index of the decoy strategy parameter, i = 1, 2, 3, 4, representing the speed difference, azimuth dispersion, distance standard deviation, and number of decoys, respectively. The sensitivity coefficients are sorted, and the i-th decoy strategy parameter corresponding to the maximum sensitivity coefficient is taken as the highly sensitive parameter.

[0088] Furthermore, the method for evaluating penetration effectiveness based on the initial effectiveness evaluation results of the new decoy strategy parameters and the initial effectiveness evaluation results corresponding to the optimal decoy strategy parameters is as follows:

[0089] The initial performance evaluation results of the new decoy strategy parameters were calculated using kinematic simulation, and the performance gap was calculated:

[0090]

[0091] In the formula, Q represents the efficiency gap, and E new E represents the initial evaluation results of the new decoy strategy parameters calculated through kinematic simulation. * This represents the initial evaluation result of the genetic algorithm under the same kinematic simulation environment. When Q < 10%, the strategy evaluation is excellent; when 10% ≤ Q < 25%, the strategy evaluation is good; and when Q ≥ 25%, the strategy evaluation is poor.

[0092] Additionally, a system for evaluating the effectiveness of penetration under decoy cover is provided. This system is used to perform the aforementioned method for evaluating the effectiveness of penetration under decoy cover, including:

[0093] The kinematic simulation parameter construction module is used to set defense environment parameters, release decoys according to the initially set decoy strategy parameters, perform kinematic simulations on the motion trajectories of real units and decoys according to the motion parameters of real units and decoy strategy parameters respectively, and launch interception units according to the set interception parameters based on the total number of detected decoys and real units.

[0094] The simulation result determination module is used to perform kinematic simulation of the motion trajectory of the launched interception unit, and to determine the result of the current interception based on the kinematic simulation results of the motion trajectories of the real unit, the decoy and the interception unit.

[0095] The core index calculation module is used to obtain the number of successful penetrations, the number of effective interceptions, the total number of interceptions, and the number of surviving decoys based on the results of multiple kinematic simulations. It constructs a defense resource consumption index based on the number of defense units per unit area, the total number of interceptions, the number of surviving decoys, and the number of decoys. It also constructs a penetration core effectiveness index based on the number of successful penetrations and the number of effective interceptions.

[0096] The initial effectiveness assessment calculation module is used to construct an initial effectiveness assessment formula by weighted summation of the penetration core effectiveness index and the defense resource consumption index, and dynamically adjust the weights based on the average detection probability of the defense system.

[0097] The penetration effectiveness evaluation module uses a genetic algorithm to take the initially formulated bait strategy parameters as the input layer and the initial effectiveness evaluation formula as the fitness function. It outputs the optimal bait strategy parameters and the corresponding initial effectiveness evaluation results. The penetration effectiveness is evaluated based on the initial effectiveness evaluation results of the new bait strategy parameters and the initial effectiveness evaluation results corresponding to the optimal bait strategy parameters.

[0098] Compared with the prior art, the beneficial effects of the present invention are:

[0099] This invention utilizes a combination of factors including speed difference, azimuth dispersion, distance standard deviation, number of decoys, number of defense units per unit area, and average detection probability. Kinematic simulations are then used to statistically determine the number of successful penetrations, effective interceptions, and the number of surviving decoys. This constructs a defense resource consumption index and a penetration core effectiveness index. A dynamic weighting mechanism is employed to weight these two in an initial effectiveness evaluation formula, enabling the formula to adapt to different scenarios. This comprehensive evaluation of the defense resource consumption index and penetration performance improves the matching degree between the evaluation results and real-world scenarios, avoiding the bias of fixed weights. Furthermore, a genetic algorithm is used to evaluate the merits of the decoy strategy parameters, thereby enhancing penetration effectiveness. Attached Figure Description

[0100] Figure 1 This is a schematic diagram of the overall method flow of the present invention;

[0101] Figure 2 This is a graph showing the analysis of speed difference and initial performance evaluation results;

[0102] Figure 3 This is a graph showing the analysis of azimuth dispersion and initial performance evaluation results;

[0103] Figure 4 This is a schematic diagram of the overall system modules of the present invention. Detailed Implementation

[0104] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0105] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0106] Example:

[0107] Please see Figures 1 to 3 The present invention provides a technical solution:

[0108] The method for evaluating the effectiveness of penetration under decoy cover includes the following steps:

[0109] Step 1: Set the defense environment parameters, release the decoys according to the initially set decoy strategy parameters, perform kinematic simulation of the motion trajectories of the real units and the decoys according to the motion parameters of the real units and the decoy strategy parameters respectively, and launch the interception units according to the set interception parameters based on the number of decoys and real units detected.

[0110] The speed difference between the decoy and the real cell affects the consistency of their trajectories. Defense systems can easily identify real cells through trajectory correlation. When the speed difference is large, the trajectories of the decoy and the real cell differ significantly, increasing the tracking filtering error of the defense system and the error rate of real cell correlation. The decoy's speed needs to have a reasonable difference from the real cell's speed to avoid being wiped out by the defense system at once due to speed synchronization. Azimuth dispersion describes the degree of dispersion of the decoy in the azimuth dimension. Defense systems typically determine the azimuth of real cells through multi-sensor fusion. The discrete distribution of the decoy in the azimuth dimension can create the illusion of multiple real cells, forcing the defense system to allocate more resources for tracking and consuming its resources previously allocated to real cells. The processing power of the decoys reduces the probability of real units being locked by the defense system. The standard deviation of the distance between the decoys and real units describes the dispersion of the decoys in the distance dimension. The discrete distribution of the decoys in the distance dimension can cause the range tracking gate of the defense system to widen, increasing the ranging error of the defense system and the difficulty of identifying real units. The number of decoys directly affects the target saturation of the defense system. Theoretically, the multi-unit tracking capability of the defense system has an upper limit, which reduces the accuracy of tracking real units. Too many decoys will increase the payload weight and cost. An optimization between interference effect and resource consumption needs to be found. Therefore, in the kinematic simulation environment, the velocity difference ΔV, azimuth dispersion θ, and distance standard deviation σ are input as decoy strategy parameters. d The number of baits, n, is used as a variable;

[0111] The method for calculating the number of defense units per unit area is as follows:

[0112]

[0113] In the formula, N defense This represents the total number of defense units deployed within the kinematic simulation area, such as radar stations and missile launch units; A represents the area of ​​the kinematic simulation area.

[0114] The formula used to construct the motion model of the real unit and the decoy is:

[0115]

[0116] In the formula, V tar V represents the velocity of the actual unit. decoy θ represents the velocity of the decoy. tarThe heading angle of the real element is represented by θ, which is the angle between the velocity direction of the real element and the horizontal direction. decoy Let x(t) represent the heading angle of the decoy, that is, the angle between the direction of the decoy's velocity and the horizontal direction, and let x(t) represent the horizontal position of the real cell at the current moment. * (t) represents the horizontal position of the decoy at the current moment, Δt represents the time interval, and x(t+1) represents the horizontal position of the real cell at the next moment. * (t+1) represents the horizontal position of the decoy at the next moment, and y(t) represents the vertical position of the real cell at the current moment. * (t) represents the vertical position of the decoy at the current moment, and y(t+1) represents the vertical position of the real cell at the next moment. * (t+1) represents the vertical position of the decoy at the next moment.

[0117] Step 2: Perform kinematic simulation on the trajectory of the launched interceptor unit, and determine the result of the interception based on the kinematic simulation results of the trajectories of the real unit, the decoy, and the interceptor unit.

[0118] Using the coordinates of the defense unit as the initial position of the interception unit, and setting the total number of detected decoys and real units as n0, the position of the real unit or decoy detected by the defense system at the current moment is (x... Target y Target The speed of the interception unit is V. D For each decoy or real unit that passes through, the coordinates of the meeting point between the interceptor unit and the decoy or real unit are (x... meet y meet The specific steps for calculating the coordinates of the meeting point are as follows:

[0119] Calculate the time it takes for the interceptor unit to reach the meeting point coordinates:

[0120]

[0121] In the formula, t1 represents the time it takes for the interception unit to reach the coordinates of the meeting point, and x Target y represents the horizontal position of the real unit or decoy detected by the defense system at the current moment. Target V represents the vertical position of the real unit or decoy detected by the defense system at the current moment. Target This indicates the speed of the decoy or real unit detected by the defense system at the current moment;

[0122] Calculate the time it takes for the decoy or real unit to reach the meeting point coordinates:

[0123]

[0124] In the formula, t2 represents the time it takes for the decoy or the real unit to reach the coordinates of the meeting point;

[0125] By solving the equations t1 = t2 simultaneously, the coordinates of the meeting point between the interceptor unit and the decoy or real unit can be calculated.

[0126] Calculate the launch angle of the interceptor unit:

[0127]

[0128] In the formula, α represents the launch angle of the interception unit. Based on the launch speed and angle of the interception unit, the motion trajectory of the launched interception unit is simulated kinematically.

[0129] Set detection threshold d thresh1 =1000, in meters, indicating that the attacker is considered to be close to the target; interception threshold d. thresh2 The unit is meters, and it is set based on the minimum effective interception distance of the interception unit to ensure that the real unit can be effectively destroyed when approaching this distance. The distance between the real unit and the target is calculated as follows:

[0130]

[0131] In the formula, d tar (t) represents the distance between the real cell and the target, x T Indicates the horizontal position of the target, y T This indicates the vertical position of the target, which is a fixed target that the defending side needs to protect, such as an enemy command post, radar station, etc.

[0132] When d tar (t) <d thresh1 The result of the interception is judged as a successful penetration. In the simulation scenario, when the attacking unit flies at low altitude, 1000 meters is the near-boundary blind zone of most defense radars or the minimum effective range of the intercepting unit. At this distance, it is difficult for the defender to intercept. Even if it is not intercepted, it can still effectively strike the target. Judging the penetration as successful is logical.

[0133] Extract the positions pointing to the real unit and the decoy, i.e., the heading angle of the interception unit. For each interception unit, calculate the position in real time:

[0134]

[0135] In the formula, x D (t) represents the horizontal position of the interception unit at the current moment, y D (t) represents the vertical position of the interception unit at the current moment, x D (t+1) represents the horizontal position of the interception unit at the next moment, y D (t+1) represents the vertical position of the interception unit at the next moment, V DIndicates the speed of movement of the interception unit. This represents the direction angle of the intercepting unit at the current moment. The formula for calculating the direction angle of the intercepting unit at the current moment is:

[0136]

[0137] In the formula, x k (t) represents the horizontal position of the decoy or real unit being tracked by the interceptor unit, y k (t) represents the vertical position of the decoy or real unit being tracked by the interceptor unit;

[0138] When the interceptor is tracking a real cell, the distance between the interceptor and the real cell is calculated in real time:

[0139]

[0140] In the formula, D k，tar (t) represents the distance between the intercepting unit and the real unit;

[0141] When the interceptor unit is tracking a decoy, the distance between the interceptor unit and the decoy is calculated in real time:

[0142]

[0143] In the formula, D k，decoy (t) represents the distance between the interceptor unit and the decoy;

[0144] When D k，tar (t) <d thresh2 When the real unit is detected, the interception is considered a valid interception. At this point, the intercepting unit successfully tracks the real unit and approaches it to within its effective range, gaining the ability to destroy it. When D... k，decoy (t) <d thresh2 When the result of the interception is determined to be that the decoy has been discovered, the number of surviving decoys can be calculated in the subsequent process to quantify the interception resources wasted on the decoys.

[0145] The kinematic simulation was performed using AnyLogic software. A Continuous2D model was created in AnyLogic. In the model's Parameters panel, the following parameters were added: area of ​​the kinematic simulation region, total number of defense units, average detection probability of the defense system, velocity difference, azimuth dispersion, distance standard deviation, and number of decoys. Real unit agents were created, their velocities were set, and they were placed at the starting point. A linear motion mode was selected, and they were set to fly from the starting point towards the kinematic simulation region. When a real unit reached the boundary, the penetration was considered successful. Based on the input number of decoys, decoy agents were also created. According to the defined decoy strategy parameters, the number of decoys was selected, and they were set to be centered on the real target, with azimuth dispersion, distance standard deviation, distance from the real unit, and the same speed as the real unit. Defense unit agents were created, and based on the total number of defense units, the defense units were evenly and randomly distributed within the kinematic simulation region. A detection range was defined, for example, 50km. When a real unit or decoy entered the detection range, as long as the detected real unit was within the set interception threshold d... thresh2 For an interception to be considered effective, either the real unit or the decoy is not detected by any defensive unit and successfully reaches its destination, or the real unit is detected by a defensive unit but the enemy's interceptor fails to hit it. The distance d between the real unit and the target is considered valid. tar (t) is less than the detection threshold d thresh1 In the case of D, the breakthrough is considered successful; k，tar (t) <d thresh2 That is, the number of times a real unit is intercepted is considered a valid interception; when D k，decoy (t) <d thresh2 When the result of the interception is determined to be that the decoy has been discovered, the average detection probability of the defense system, the number of successful penetrations, the number of effective interceptions, the total number of interceptions, and the number of surviving decoys obtained through simulation and calculation are summarized to form a data report.

[0146]

[0147]

[0148] Table 1. Statistical Table of Simulation Results

[0149] The simulation results statistics table contains key parameters and performance indicators that affect the penetration process. Velocity difference, azimuth dispersion, and distance standard deviation describe the movement and distribution characteristics of the decoy and the real target, affecting the detection and tracking of the defense system. The number of decoys and the number of defense units per unit area represent the strength of the defender and the resource investment of the interfering party. The average detection probability of the defense system reflects the ability of the defense system to detect targets. The number of successful penetrations, the number of effective interceptions, the total number of interceptions, and the number of surviving decoys are performance indicators that measure the penetration effect and resource consumption. By analyzing these data, the quality of penetration effectiveness can be intuitively reflected, which can help adjust the decoy strategy parameters and provide a data source for the calculation of the subsequent initial effectiveness evaluation formula.

[0150] Step 3: Based on the results of multiple kinematic simulations, obtain the number of successful penetrations, the number of effective interceptions, the total number of interceptions, and the number of surviving decoys. Construct a defense resource consumption index based on the number of defense units per unit area, the total number of interceptions, the number of surviving decoys, and the number of decoys. Construct a penetration core effectiveness index based on the number of successful penetrations and the number of effective interceptions.

[0151] Defense resource consumption index:

[0152]

[0153] In the formula, I res This represents the defense resource consumption index, used to measure the resource efficiency of the defender in dealing with decoys and real units. The higher the value, the more resources the defender consumes to intercept real units and decoys. In other words, the defense system attacks the decoys, indirectly protecting the real units. N intercept N represents the total number of interceptions, reflecting the total amount of actual interception resources, i.e., the direct resource consumption of the defense system in performing interception actions, such as the number of missile launches and radar tracking time. intercept The larger the value, the better. res The higher the value of N, the stronger the positive correlation. surv This represents the number of surviving decoys, where n represents the total number of real units and decoys. This ratio represents the survival efficiency of the decoys. The more surviving decoys, the more likely they are to attract defensive fire without being destroyed, forcing the defenders to continuously allocate resources to counter this and thus consuming more resources. Therefore, the survival efficiency of the decoys is related to I... res There is a positive correlation, ρ reflects the density of the defense system, and realizes the fundamental impact of the deployment density of the defense system on resource consumption. High-density deployment means that the defender has invested more basic resources in physical space. Even if no actual interception has been carried out, the initial deployment itself is a kind of resource consumption, thus quantifying the basic stock of defense resources at the physical space level. Therefore, the higher the number of defense units per unit area, the higher I. resThe larger the value, the more the three factors multiply, resulting in the effects of basic input resources, actual resource consumption, and resource waste efficiency, thus achieving the goal of quantifying resource consumption under the synergistic influence of multiple factors.

[0154] First, the probability of a successful penetration is calculated using the following formula:

[0155]

[0156] In the formula, P s N represents the probability of a successful penetration. hit N represents the number of valid interceptions. s Indicates the number of successful penetrations;

[0157] Calculate the effective interception rate:

[0158]

[0159] In the formula, P hit This represents the effective interception rate, where N is the number of effective interceptions. hit The proportion of interception actions conducted by the defender against real targets reflects the degree to which they have been misled by decoys.

[0160] Constructing a core effectiveness index for penetration defense:

[0161] I eff =P s ·(1-P hit )

[0162] In the formula, the probability of successful penetration is P. s This directly reflects the defense system's basic ability to prevent penetration. The higher the value, the weaker the defense system's basic ability to prevent decoys and real units. 1-P hit This reflects the proportion of real units intercepted, i.e., the probability that the defender fails to effectively intercept real units. It eliminates decoy interference and separately measures the threat level of the defense system to real units. eff The penetration effectiveness index measures the overall ability of a real unit to break through the defense and avoid effective interception. It assesses whether a real unit can both break through the defense boundary and avoid being effectively intercepted by the defense system. Both conditions must be met simultaneously to reflect the penetration effect of the real unit. Therefore, the two need to be combined in the form of joint probability, that is, the two are multiplied. The larger the value, the better the penetration effect and the lower the interception efficiency of the real unit.

[0163] Step 4: Construct an initial effectiveness evaluation formula by weighted summation of the penetration core effectiveness index and the defense resource consumption index, and dynamically adjust the weights based on the average detection probability of the defense system;

[0164] Extract the defense resource consumption index and the penetration core effectiveness index, and construct an initial effectiveness evaluation formula by weighted summation:

[0165] E=ω res ·I res +ω eff ·I eff

[0166] In the formula, E represents the initial effect assessment result, ω res ω represents the weighting coefficient of the defense resource consumption index. eff This represents the weighting coefficient of the penetration core effectiveness index, and ω res +ω eff =1, comprehensively assessing the resource consumption efficiency of the defender and the penetration effectiveness of the attacker, forming an overall quantitative indicator of the confrontation effect between the defense system and the penetration system. Through weighted summation, the relationship between the two is balanced, avoiding the one-sidedness of a single-dimensional assessment of the defender's resource consumption or penetration success. res This value measures the resources consumed by the defender to intercept real units and decoys. A higher value indicates that because the probability of intercepting real units is low, more defensive resources are required. eff This measure assesses the attacker's ability to break through the defense. A higher value indicates a better breakthrough effect. The combined value quantifies the combined impact of defense costs and breakthrough effect. When the E value is high, even if the breakthrough probability is low, the attacker may still increase the ineffective consumption of defense resources through strategies. When the E value is low, it reflects that the probability of breakthrough failure is high under strong defense, and the resource consumption of the defense system is also limited. It is necessary to carefully consider whether to follow this decoy strategy parameter.

[0167] Build defense strength indicators:

[0168]

[0169] In the formula, R d P represents the defense strength index. det R represents the average detection probability of the defense system. d A higher value indicates better overall performance of the defense system in detection and interception, making ω... eff =R d ω res =1-R d Among them, the average detection probability P det Reflecting the defense system's ability to detect real units or decoys, when the defense strength R... d A relatively high ω indicates superior performance of the defense system. eff Larger, it places more emphasis on the core effectiveness index of penetration, and a successful penetration also makes I effA larger E value further highlights the initial assessment result. That is, when the defense system has excellent performance, the larger the E value, the better the penetration effect. Even if the penetration probability is low, failure can still increase the ineffective consumption of defense resources. Conversely, even if the defense system has poor performance, if the E value is relatively small, it means that the defense system has low resource investment and low success rate. The decoy strategy parameters should be checked.

[0170]

[0171]

[0172] Table 2 Simulation Data Calculation Statistics Table

[0173] The initial evaluation results in the simulation data calculation and statistics table provide an intuitive comprehensive judgment, helping to quickly identify high-value experimental combinations. After extracting high-value experimental combinations, by comparing the defense resource consumption index and penetration core effectiveness index of different experimental groups, it is possible to analyze how much defense resource consumption was exchanged for how much penetration effect, and thus optimize resource investment strategies. This allows for decisions that aim at successful penetration or resource consumption. For example, if an experimental group has a high penetration core effectiveness index but a high defense resource consumption index, the cost can be reduced by adjusting parameters such as the number of decoys and the speed difference.

[0174] Step 5: Using a genetic algorithm, the initially defined bait strategy parameters are used as the input layer, and the initial effectiveness evaluation formula is used as the fitness function. The optimal bait strategy parameters and the corresponding initial effectiveness evaluation results are output. The penetration effectiveness is evaluated based on the initial effectiveness evaluation results of the new bait strategy parameters and the initial effectiveness evaluation results corresponding to the optimal bait strategy parameters.

[0175] A genetic algorithm is used, with experts setting the value range for each bait strategy parameter. Within each parameter range, values ​​are uniformly and randomly selected to generate 100 individuals. This is a standard setup that effectively increases the likelihood of finding the optimal solution. Each individual is composed of the bait strategy parameters.

[0176] C j =[ΔV j θ j , σ d，j n j ]

[0177] In the formula, C j Let ΔV represent the j-th individual whose parameters consist of the bait strategy parameters. j Let θ represent the velocity difference of the j-th individual, which is composed of the decoy strategy parameters. j Let σ represent the azimuth dispersion of the j-th individual composed of decoy strategy parameters. d，jLet n represent the standard deviation of the distance of the j-th individual, which consists of the bait strategy parameters. j The number of decoys for the j-th individual composed of decoy strategy parameters is represented by j = 1, 2, ..., 100, where j represents the individual's index. For velocity difference, azimuth dispersion, and distance standard deviation, uniformly distributed random numbers are generated within a set range. For the number of decoys, random integers are generated within a set range. Kinematic simulations are performed on the parameters of each individual and the defense environment parameters to obtain the total number of interceptions, the number of surviving decoys, the number of successful penetrations, and the number of effective interceptions.

[0178] To prevent the algorithm from getting stuck in an infinite loop, the maximum number of iterations was set to 200. The result of the initial evaluation formula was used as the fitness value. The trigger probability of crossover was set to 0.8, and the trigger probability of mutation was set to 0.05. Then, a tournament selection method was used. Each time, three individuals were randomly selected from the population, their fitness was compared, and the one with the highest fitness was retained and added to the parent pool. This process was repeated 100 times to generate the parent population. Arithmetic crossover was performed on the selected parents with a probability of 0.8 to generate two offspring. The weight factor of the arithmetic crossover was randomly generated by the algorithm. For the number of decoys, an integer crossover method was used. For parents that were not selected and whose crossover was not triggered, the offspring directly copied the parents. Gaussian mutation was used to mutate the decoy strategy parameters of each offspring with a probability of 0.05. When the decoy strategy parameters after mutation exceeded the value range, they were truncated back to the nearest value range. The individual with the highest fitness among the parents and offspring was selected as the optimal decoy strategy parameters, and the corresponding initial evaluation value was recorded to provide a basis for subsequent strategy adjustment and evaluation.

[0179] Figure 2 The figure shows that when the azimuth dispersion is 20°, the distance standard deviation is 300m, the number of decoys is 10, and the average detection probability of the defense system is 0.5, the initial effectiveness assessment results show an upward trend as the speed difference increases, and are positively correlated with the initial effectiveness assessment results. The values ​​of azimuth dispersion, distance standard deviation, number of decoys, speed difference, and average detection probability of the defense system are all conventional values ​​and are representative. This means that, under the condition that other conditions remain unchanged, the greater the speed difference within a certain range, the higher the penetration effectiveness. This figure provides a basis for the optimization of penetration strategy, that is, increasing the speed difference can effectively improve the initial effectiveness assessment results and consume the enemy's defense resources.

[0180] Figure 3The figure shows that, to observe the impact of azimuth dispersion on the initial assessment results, other independent variables are assumed to take the following typical values: with a velocity difference of 25 m / s, a distance standard deviation of 400 m, 15 decoys, and an average detection probability of 0.5 for the defense system, the initial assessment results first rise and then fall as the azimuth dispersion increases, exhibiting a single-peak curve. This indicates that there exists an optimal azimuth dispersion that maximizes the initial assessment results. During the rising phase, the increased azimuth dispersion allows the decoys to more effectively consume the defense system's resources, increasing the difficulty of tracking and interception by the defense system, thus improving the initial assessment results. During the falling phase, excessively large azimuth dispersion leads to an overly significant difference in azimuth between the decoys and the real units, reducing the interference effect and lowering the initial assessment results. This figure provides a basis for optimizing penetration strategies, namely, by adjusting the azimuth dispersion of the decoys to near the peak value, the optimal penetration effectiveness can be achieved.

[0181] Calculate the sensitivity coefficients of the decoy strategy parameters to the initial effectiveness evaluation results:

[0182]

[0183] In the formula, S i This represents the sensitivity coefficient of the i-th parameter, where i represents the index of the bait strategy parameter, i = 1, 2, 3, 4, representing the speed difference, azimuth dispersion, distance standard deviation, and number of baits, respectively. The sensitivity coefficients are sorted, and the i-th bait strategy parameter corresponding to the maximum sensitivity coefficient is considered the most sensitive parameter and adjusted first. The sensitivity coefficient S of the i-th parameter... i The sensitivity of the initial effectiveness assessment result E was measured. Can be broken down into The calculation reflects the rate of change of E when the parameters change slightly, S i The larger the value, the higher the sensitivity of the initial effect assessment result to the i-th parameter, indicating a positive correlation. The calculation considers both the magnitude of the parameters themselves and normalizes the results. Highly sensitive parameters have a significant impact on the initial evaluation results. Prioritizing parameter adjustments for the new decoy strategy can achieve more effective penetration.

[0184] The initial performance evaluation results of the new decoy strategy parameters were calculated using kinematic simulation, and the performance gap was calculated:

[0185]

[0186] In the formula, Q represents the efficiency gap, and E new E represents the initial evaluation results of the new decoy strategy parameters calculated through kinematic simulation. *This represents the initial evaluation result of the genetic algorithm under the same kinematic simulation environment. When Q < 10%, the strategy evaluation is excellent; when 10% ≤ Q < 25%, the strategy evaluation is good; when Q ≥ 25%, the strategy evaluation is poor, and it is recommended to modify the high-sensitivity parameter.

[0187] In the formula, the effectiveness gap reflects the degree of change of the initial effectiveness evaluation result of the new strategy relative to the optimal strategy. Although it may not be the best penetration solution, it can intuitively judge the merits of the new strategy. When Q is small, it means that the new strategy is similar to the optimal strategy and is evaluated as excellent. When Q is large, it means that the new strategy is less effective and needs further adjustment, especially prioritizing the modification of highly sensitive parameters.

[0188] Please see Figure 4 The present invention also provides a system for evaluating the effectiveness of penetration under decoy cover, the system being used to perform the above-described method for evaluating the effectiveness of penetration under decoy cover, including:

[0189] The kinematic simulation parameter construction module is used to set defense environment parameters, release decoys according to the initially set decoy strategy parameters, perform kinematic simulations on the motion trajectories of real units and decoys according to the motion parameters of real units and decoy strategy parameters respectively, and launch interception units according to the set interception parameters based on the total number of detected decoys and real units.

[0190] The simulation result determination module is used to perform kinematic simulation of the motion trajectory of the launched interception unit, and to determine the result of the current interception based on the kinematic simulation results of the motion trajectories of the real unit, the decoy and the interception unit.

[0191] The core index calculation module is used to obtain the number of successful penetrations, the number of effective interceptions, the total number of interceptions, and the number of surviving decoys based on the results of multiple kinematic simulations. It constructs a defense resource consumption index based on the number of defense units per unit area, the total number of interceptions, the number of surviving decoys, and the number of decoys. It also constructs a penetration core effectiveness index based on the number of successful penetrations and the number of effective interceptions.

[0192] The initial effectiveness assessment calculation module is used to construct an initial effectiveness assessment formula by weighted summation of the penetration core effectiveness index and the defense resource consumption index, and dynamically adjust the weights based on the average detection probability of the defense system.

[0193] The penetration effectiveness evaluation module uses a genetic algorithm to take the initially formulated bait strategy parameters as the input layer and the initial effectiveness evaluation formula as the fitness function. It outputs the optimal bait strategy parameters and the corresponding initial effectiveness evaluation results. The penetration effectiveness is evaluated based on the initial effectiveness evaluation results of the new bait strategy parameters and the initial effectiveness evaluation results corresponding to the optimal bait strategy parameters.

[0194] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0195] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0196] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple grid units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0197] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method for evaluating the penetration effectiveness under decoy cover, characterized in that, The specific steps include: Step 1: Set the defense environment parameters, release the decoys according to the initially set decoy strategy parameters, perform kinematic simulation of the motion trajectories of the real units and the decoys according to the motion parameters of the real units and the decoy strategy parameters respectively, and launch the interception units according to the set interception parameters for the total number of detected decoys and real units. Step 2: Perform kinematic simulation on the trajectory of the launched interceptor unit, and determine the result of the interception based on the kinematic simulation results of the trajectories of the real unit, the decoy, and the interceptor unit. Step 3: Based on the results of multiple kinematic simulations, obtain the number of successful penetrations, the number of effective interceptions, the total number of interceptions, and the number of surviving decoys. Construct a defense resource consumption index based on the number of defense units per unit area, the total number of interceptions, the number of surviving decoys, and the number of decoys. Construct a penetration core effectiveness index based on the number of successful penetrations and the number of effective interceptions. Step 4: Construct an initial effectiveness evaluation formula by weighted summation of the penetration core effectiveness index and the defense resource consumption index, and dynamically adjust the weights based on the average detection probability of the defense system; Step 5: Using a genetic algorithm, the initially formulated bait strategy parameters are used as the input layer, and the initial effectiveness evaluation formula is used as the fitness function. The optimal bait strategy parameters and the corresponding initial effectiveness evaluation results are output. The penetration effectiveness is evaluated based on the initial effectiveness evaluation results of the new bait strategy parameters and the initial effectiveness evaluation results corresponding to the optimal bait strategy parameters. The method for launching interceptor units based on the total number of detected decoys and real units, according to the set interception parameters, is as follows: Using the coordinates of the defense unit as the initial position of the interception unit, the total number of detected decoys and real units is set to... The location of the real unit or decoy detected by the defense system at the current moment is The speed of the interception unit is For each decoy or real unit that passes through, the coordinates of the meeting point between the interceptor unit and the decoy or real unit are: The specific steps for calculating the coordinates of the meeting point are as follows: Calculate the time it takes for the interceptor unit to reach the meeting point coordinates: In the formula, This indicates the time it takes for the interceptor unit to reach the coordinates of the meeting point. This indicates the horizontal position of the actual unit or decoy detected by the defense system at the current moment. This indicates the vertical position of the actual unit or decoy detected by the defense system at the current moment. This indicates the speed of the decoy or real unit detected by the defense system at the current moment; Calculate the time it takes for the decoy or real unit to reach the meeting point coordinates: In the formula, This indicates the time it takes for the decoy or the real unit to reach the coordinates of the meeting point; make By solving the simultaneous equations, the coordinates of the meeting point between the interceptor unit and the decoy or real unit can be calculated; Calculate the launch angle of the interceptor unit: In the formula, Indicates the launch angle of the interceptor unit; The method for determining the interception result based on the kinematic simulation results of the motion trajectory of the launched interception unit, the actual unit, the decoy, and the interception unit is as follows: Set detection threshold The unit is meters, and the interception threshold is... The unit is meters, used to calculate the distance between the actual unit and the target: In the formula, Indicates the distance between the actual unit and the target. Indicates the horizontal position of the target. Indicates the vertical position of the target; when The result of the interception is judged as a successful breakthrough; Extract the positions pointing to the real unit and the decoy, i.e., the heading angle of the interception unit. For each interception unit, calculate the position in real time: In the formula, This indicates the current horizontal position of the interception unit. This indicates the current vertical position of the interception unit. This indicates the horizontal position of the interception unit at the next moment. This indicates the vertical position of the interception unit at the next moment. Indicates the speed of movement of the interception unit. This represents the direction angle of the intercepting unit at the current moment. The formula for calculating the direction angle of the intercepting unit at the current moment is: In the formula, This indicates the horizontal position of the decoy or real unit being tracked by the interceptor unit. Indicates the vertical position of the decoy or real unit being tracked by the interceptor unit; When the interceptor is tracking a real cell, the distance between the interceptor and the real cell is calculated in real time: In the formula, Indicates the distance between the intercepting unit and the real unit; When the interceptor unit is tracking a decoy, the distance between the interceptor unit and the decoy is calculated in real time: In the formula, Indicates the distance between the interceptor unit and the decoy; when When the real unit is considered to have been discovered, the result of the interception is judged as a valid interception. At that time, the result of the interception is determined to be that the decoy has been discovered.

2. The method for evaluating the penetration effectiveness under decoy cover according to claim 1, characterized in that: The method for kinematic simulation of the motion trajectories of the real cell and the decoy, based on the motion parameters of the real cell and the decoy strategy parameters, is as follows: During kinematic simulation, the velocity difference of the input decoy strategy parameters in the kinematic simulation environment Azimuth dispersion Distance from standard deviation and the number of baits All decoys are assumed to have the same speed. The speed difference refers to the difference in speed between the decoy and the real unit. The azimuth dispersion refers to the dispersion of the decoys' azimuth distribution centered on the real unit. The distance standard deviation is the standard deviation of the distance between the decoy and the real unit, with the real unit as the reference. The method for calculating the number of defense units per unit area is as follows: In the formula, This represents the total number of defense units deployed within the kinematic simulation area. This represents the area of ​​the kinematic simulation region. Indicates the number of defense units per unit area; The formula used to construct the motion model of the real unit and the decoy is: In the formula, Represents the velocity of the actual unit. Indicates the speed of the decoy's movement. This represents the heading angle of the real element, which is the angle between the velocity direction of the real element and the horizontal direction. This indicates the heading angle of the decoy, which is the angle between the direction of the decoy's velocity and the horizontal direction. This indicates the current horizontal position of the actual cell. This indicates the horizontal position of the decoy at the current moment. Indicates time interval, This indicates the horizontal position of the actual cell at the next moment. This indicates the horizontal position of the decoy at the next moment. This represents the vertical position of the actual cell at the current moment. This indicates the vertical position of the decoy at the current moment. This indicates the vertical position of the actual cell at the next moment. This indicates the vertical position of the decoy at the next moment.

3. The method for evaluating the penetration effectiveness under decoy cover according to claim 2, characterized in that: Based on the results of multiple kinematic simulations, the number of successful penetrations, the number of effective interceptions, the total number of interceptions, and the number of surviving decoys were obtained. The method for constructing a defense resource consumption index based on the number of defense units per unit area, the total number of interceptions, the number of surviving decoys, and the number of decoys is as follows: Set the number of kinematic simulations to ,and for Positive integers; The number of times a single interception was considered a successful penetration under all kinematic simulations was counted. The results were summed, and the average was taken as the total number of successful penetrations. ; Calculate the number of surviving decoys in each kinematic simulation: In the formula, This represents the number of baits that survive a single kinematic simulation. This represents the number of baits detected under the kinematic simulation. The average number of surviving baits from each kinematic simulation is taken as the total number of surviving baits, denoted as [missing information]. ; The number of interceptions deemed valid under all kinematic simulations is counted, and the sum of these counts is averaged to determine the number of valid interceptions. ; The number of interception units launched by the defense system under all kinematic simulations is counted, and the sum of the results is averaged to obtain the total number of interceptions. ; Defense resource consumption index: In the formula, This indicates the index of defense resource consumption.

4. The method for evaluating the penetration effectiveness under decoy cover according to claim 3, characterized in that: The method for constructing the core effectiveness index of penetration based on the number of successful penetrations, the total number of kinematic simulations, and the number of effective interceptions is as follows: The formula used to calculate the probability of a successful penetration is: In the formula, Indicates the probability of a successful penetration; The formula used to calculate the effective interception rate is: In the formula, Indicates the effective interception rate; Constructing a core effectiveness index for penetration defense: In the formula, This indicates the core effectiveness index for penetration defense.

5. The method for evaluating the penetration effectiveness under decoy cover according to claim 4, characterized in that: The initial effectiveness evaluation formula is constructed by weighted summation of the penetration core effectiveness index and the defense resource consumption index. The weights are dynamically adjusted based on the average detection probability of the defense system. Calculate the average detection probability of the defense system under each kinematic simulation: In the formula, This represents the average detection probability of the defense system under each kinematic simulation. This indicates the number of decoys under this kinematic simulation. This represents the total number of decoys and real units detected. The total number of decoys and real units detected in each kinematic simulation is counted, and the sum of these results is averaged to obtain the average detection probability of the defense system, denoted as . ; Extract the defense resource consumption index and the penetration core effectiveness index, and construct an initial effectiveness evaluation formula by weighted summation: In the formula, This indicates the initial effectiveness assessment results. This represents the weighting coefficient of the defense resource consumption index. This represents the weighting coefficient of the penetration defense core effectiveness index, and ; Build defense strength indicators: In the formula, Indicates the defense strength index, making , .

6. The method for evaluating the penetration effectiveness under decoy cover according to claim 5, characterized in that: The method for outputting the optimal decoy strategy parameters and the corresponding initial evaluation results is as follows: A genetic algorithm is used, where experts set the value range for each bait strategy parameter. Within each value range, the bait strategy parameter is uniformly and randomly selected, generating a total of 100 individuals. Each individual is composed of the bait strategy parameters. In the formula, Indicates the first An individual composed of decoy strategy parameters, Indicates the first The speed difference of an individual composed of decoy strategy parameters Indicates the first The azimuth dispersion of an individual composed of decoy strategy parameters, Indicates the first The standard deviation of the distance of an individual composed of bait strategy parameters. Indicates the first The number of baits for an individual, which is composed of bait strategy parameters. , representing the individual's serial number, generates uniformly distributed random numbers within a set range for speed difference, azimuth dispersion, and distance standard deviation, and generates random integers within a set range for the number of decoys. Kinematic simulations are performed on the parameters of each individual and the defense environment to obtain the total number of interceptions, the number of surviving decoys, the number of successful penetrations, and the number of effective interceptions. The maximum number of iterations is set to 200. The result of the initial evaluation formula is used as the fitness value. The trigger probability of crossover is set to 0.8 and the trigger probability of mutation is set to 0.

05. Then, a tournament selection method is used. Each time, three individuals are randomly selected from the population, their fitness is compared, and the one with the highest fitness is retained to enter the parent pool. This process is repeated 100 times to generate the parent population. Arithmetic crossover is performed on the selected parents with a probability of 0.8 to generate two offspring. The weight factor of arithmetic crossover is randomly generated by the algorithm. For the number of decoys, an integer crossover method is used. For parents that have not triggered crossover (i.e., not selected), the offspring directly copy the parents. Gaussian mutation is used to mutate the decoy strategy parameters of each offspring with a probability of 0.

05. When the decoy strategy parameters after mutation exceed the value range, they are truncated back to the nearest value range. The individual with the highest fitness among the parents and offspring is selected as the optimal decoy strategy parameters, and the corresponding initial evaluation value is recorded. Calculate the sensitivity coefficients of the decoy strategy parameters to the initial effectiveness evaluation results: In the formula, Indicates the first Sensitivity coefficient of the parameter Indicates the index of the decoy strategy parameters. Let represent the speed difference, azimuth dispersion, distance standard deviation, and number of decoys, respectively. The sensitivity coefficients are sorted, and the value corresponding to the highest sensitivity coefficient is selected as the first... The parameters of the bait strategy are used as highly sensitive parameters.

7. The method for evaluating the penetration effectiveness under decoy cover according to claim 6, characterized in that: The method for evaluating penetration effectiveness based on the initial effectiveness evaluation results of the new decoy strategy parameters and the initial effectiveness evaluation results corresponding to the optimal decoy strategy parameters is as follows: The initial performance evaluation results of the new decoy strategy parameters were calculated using kinematic simulation, and the performance gap was calculated: In the formula, Indicating performance gaps, This indicates the initial evaluation results of the new decoy strategy parameters calculated through kinematic simulation. This represents the initial evaluation result output by the genetic algorithm under the same kinematic simulation environment, when The strategy is rated as excellent; when The strategy assessment is good. The strategy was rated as poor.

8. A system for evaluating the effectiveness of penetration under decoy cover, characterized in that: The system is used to perform the method for evaluating the penetration effectiveness under decoy cover as described in any one of claims 1-7, including: The kinematic simulation parameter construction module is used to set defense environment parameters, release decoys according to the initially set decoy strategy parameters, perform kinematic simulations on the motion trajectories of real units and decoys according to the motion parameters of real units and decoy strategy parameters respectively, and launch interception units according to the set interception parameters based on the total number of detected decoys and real units. The simulation result determination module is used to perform kinematic simulation of the motion trajectory of the launched interception unit, and to determine the result of the current interception based on the kinematic simulation results of the motion trajectories of the real unit, the decoy and the interception unit. The core index calculation module is used to obtain the number of successful penetrations, the number of effective interceptions, the total number of interceptions, and the number of surviving decoys based on the results of multiple kinematic simulations. It constructs a defense resource consumption index based on the number of defense units per unit area, the total number of interceptions, the number of surviving decoys, and the number of decoys. It also constructs a penetration core effectiveness index based on the number of successful penetrations and the number of effective interceptions. The initial effectiveness assessment calculation module is used to construct an initial effectiveness assessment formula by weighted summation of the penetration core effectiveness index and the defense resource consumption index, and dynamically adjust the weights based on the average detection probability of the defense system. The penetration effectiveness evaluation module uses a genetic algorithm to take the initially formulated bait strategy parameters as the input layer and the initial effectiveness evaluation formula as the fitness function. It outputs the optimal bait strategy parameters and the corresponding initial effectiveness evaluation results. The penetration effectiveness is evaluated based on the initial effectiveness evaluation results of the new bait strategy parameters and the initial effectiveness evaluation results corresponding to the optimal bait strategy parameters.

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