Method and system for evaluating penetration effectiveness under bait shield

Abstract

The invention provides a method and a system for evaluating penetration effectiveness under bait shielding, and relates to the technical field of combat effectiveness evaluation. The method comprises the following steps of: firstly, determining bait strategy parameters including speed difference, azimuth dispersion degree, distance dispersion degree and bait quantity, and simultaneously extracting defense environment parameters, namely defense system deployment density and detection capability; the method comprises the following steps: acquiring penetration data by simulating kinematics simulation, constructing evaluation indexes from two dimensions of defense party resource consumption efficiency and attack party penetration capability, and dynamically adjusting weights by combining the detection capability of a defense system to form a comprehensive evaluation result. A genetic algorithm is adopted to optimize bait strategy parameters, an optimal strategy combination is searched in a preset parameter range, the sensitivity degree of each parameter to an evaluation result is calculated, a suggestion for preferentially adjusting the parameters with relatively large influence is given, and the advantages and disadvantages of the strategies are quantitatively evaluated and the bait strategy parameters are optimized by comparing the efficiency difference between a new strategy and the optimal strategy.

Description

Technical Field

[0001] The present invention relates to the technical field of combat effectiveness evaluation, and in particular to a method and system for evaluating penetration effectiveness under decoy cover. Background Art

[0002] Faced with increasingly complex air defense and anti-missile systems, penetration operations have significantly increased their reliance on decoy technology. Against this background, decoy technology has been upgraded from an auxiliary means to the 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 deployment is mostly based on setting parameters based on empirical thresholds, lacking a quantitative response mechanism to dynamic defense environments. Static weight evaluation indicators are generally used, and the density distribution of defense units and the dynamic allocation characteristics of interception resources are not incorporated into the evaluation system. This results in systematic deviations between kinematic simulation results and actual combat effectiveness, leading to significant fluctuations in the effectiveness of the strategy. In modern air defense and anti-missile systems, decoys need to simultaneously achieve the dual goals of consuming defense resources and ensuring the penetration of real units. However, existing methods also lack a systematic quantitative evaluation of the two. Although increasing the number of decoys can improve the interference effect, excessive deployment will lead to excessive payload and a surge in costs. However, there is a lack of a quantitative balance model for decoy consumption and defense resource consumption. How can resource efficiency indicators be incorporated into the evaluation system to achieve the optimization goal of minimizing decoy costs in exchange for maximum defense resource loss?

[0004] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0005] The purpose of the present invention is to provide a method and system for evaluating penetration effectiveness under decoy cover to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] The specific steps of the penetration effectiveness evaluation method under decoy cover include:

[0008] Step 1: Set the defense environment parameters and release the decoys according to the initially formulated decoy strategy parameters. Perform kinematic simulations on the motion trajectories of the real units and the decoys based on the motion parameters of the real units and the decoy strategy parameters. Launch the interceptor units based on the set interception parameters based on the total number of decoys and real units found.

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

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

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

[0012] Step 5: Use the genetic algorithm, take the initially formulated decoy strategy parameters as the input layer, and the initial effectiveness evaluation formula as the fitness function, output the optimal decoy strategy parameters and the corresponding initial effectiveness evaluation results, and evaluate the 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.

[0013] Furthermore, according to the motion parameters of the real unit and the bait strategy parameters, the method for kinematic simulation of the motion trajectory of the real unit and the bait is:

[0014] When performing kinematic simulation, the velocity difference ΔV, azimuth angle dispersion θ, and distance standard deviation σ of the decoy strategy parameters are input into the kinematic simulation environment. d and the number of decoys n, assuming the speed of each decoy is the same. The speed difference refers to the difference in movement speed between the decoy and the real unit. The azimuth dispersion refers to the degree of dispersion of the decoy's azimuth distribution with the real unit as the center. The distance standard deviation refers to the standard deviation of the distance between the decoy and the real unit with the real unit as the benchmark. The number of defense units per unit area is calculated as follows:

[0015]

[0016] Where N defense represents the total number of defense units deployed in 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 for constructing the motion model of the real unit and the decoy is:

[0018]

[0019] Where V tar Indicates the movement speed of the real unit, V decoy represents the speed of the bait, θtar Represents the heading angle of the real unit, that is, the angle between the real unit velocity direction and the horizontal direction, θ decoy represents the heading angle of the bait, that is, the angle between the bait speed direction and the horizontal direction, x(t) represents the horizontal position of the real unit at the current moment, x * (t) represents the horizontal position of the bait at the current moment, Δt represents the time interval, x(t+1) represents the horizontal position of the real unit at the next moment, and x * (t+1) represents the horizontal position of the bait at the next moment, y(t) represents the vertical position of the real unit at the current moment, and y * (t) represents the vertical position of the bait at the current moment, y(t+1) represents the vertical position of the real unit at the next moment, and y * (t+1) represents the vertical position of the bait at the next moment.

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

[0021] The defense unit coordinates are used as the initial position of the interception unit, and the total number of decoys and real units found is set to 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, the coordinates of the meeting point between the interception unit and the decoy or real unit are (x meet ,y meet ), where the specific steps for calculating the coordinates of the meeting point are:

[0022] Calculate the time it takes for the interception unit to reach the coordinates of the encounter point:

[0023]

[0024] Where t1 represents the time when the interception unit reaches the coordinates of the encounter point, x Target Indicates the horizontal position of the real unit or decoy detected by the defense system at the current moment, y Target Indicates the vertical position of the real unit or decoy detected by the defense system at the current moment, V Target 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 coordinates of the encounter point:

[0026]

[0027] Where t2 represents the time when the decoy or real unit reaches the coordinates of the encounter point;

[0028] Let t1 = t2 and solve the simultaneous equations to calculate the coordinates of the meeting point between the interception unit and the decoy or real unit;

[0029] Calculate the interception unit launch angle:

[0030]

[0031] Where α represents the launch angle of the interception unit.

[0032] Furthermore, a kinematic simulation is performed on the motion trajectory of the launched interception unit. The method for judging the result of the interception is as follows based on the kinematic simulation results of the motion trajectories of the real unit, the decoy, and the interception unit:

[0033] Set the detection threshold d thresh1 =1000, in meters, interception threshold d thresh2 , in meters, calculate the distance between the real unit and the target:

[0034]

[0035] Where, d tar (t) represents the distance between the real unit 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 determined to be a successful penetration;

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

[0038]

[0039] Where 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 represents the movement speed of the interception unit, Indicates the direction angle of the interception unit at the current moment. The calculation formula for the direction angle of the interception unit at the current moment is:

[0040]

[0041] Where xk (t) represents the horizontal position of the decoy or real unit tracked by the interception unit, y k (t) represents the vertical position of the decoy or real unit tracked by the interception unit;

[0042] When the interception unit is tracking the real unit, the distance between the interception unit and the real unit is calculated in real time:

[0043]

[0044] Where D k，tar (t) represents the distance between the interception unit and the true unit;

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

[0046]

[0047] Where D k，decoy (t) represents the distance between the interception unit and the bait;

[0048] When D k，tar (t) <d thresh2 When the real unit is found, the interception result is considered as effective interception. k，decoy (t) <d thresh2 When the interception result is , it is determined that the bait is discovered.

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

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

[0051] Count the number of times the interception results under all kinematic simulations are judged to be successful penetration, and add up the results and take the average value as the number of successful penetrations, which is recorded as N. s ;

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

[0053] N dsurv =nn bfx

[0054] Where N dsurv Indicates the number of baits surviving in a single kinematic simulation, n bfxIt represents the number of baits found in the kinematic simulation. The number of baits surviving in each kinematic simulation is accumulated and the average value is taken as the number of baits surviving, which is recorded as N surv ;

[0055] Count the number of effective interceptions in all kinematic simulations, add up the results and take the average value as the number of effective interceptions, which is recorded as N. hit ;

[0056] Count the number of interception units launched by the defense system under all kinematic simulations, add up the results and take the average value as the total number of interceptions, which is recorded as N intercept ;

[0057] Constructing a defense resource consumption index:

[0058]

[0059] Where i res Indicates the defense resource consumption index.

[0060] Furthermore, the method for constructing the penetration core 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 for calculating the probability of successful penetration is:

[0062]

[0063] Where, P s Indicates the probability of successful penetration;

[0064] The effective interception rate is calculated based on the following formula:

[0065]

[0066] Where, P hit represents the effective interception rate;

[0067] Constructing the core penetration effectiveness index:

[0068] I eff =P s ·(1-P hit )

[0069] Where, I eff Represents the penetration core effectiveness index.

[0070] Furthermore, the initial effectiveness evaluation formula is constructed by weighted summation of the penetration core effectiveness index and the defense resource consumption index. The method of dynamically adjusting the weights based on the average detection probability of the defense system is as follows:

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

[0072]

[0073] Where, P ddet represents the average detection probability of the defense system under each kinematic simulation, n represents the number of decoys under the kinematic simulation, n0 represents the total number of decoys and real units found, and the total number of decoys and real units found under each kinematic simulation is counted. The results are accumulated and the average value is taken as the average detection probability of the defense system, which is recorded as P det ;

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

[0075] E=ω res I res +ω eff I eff

[0076] Where, E represents the initial effect evaluation result, ω res Represents the weight coefficient of the defense resource consumption index, ω eff represents the weight coefficient of the penetration core effectiveness index, and ω res +ω eff =1;

[0077] Constructing a defense strength index:

[0078]

[0079] Where R d represents the defense strength index, so that ω eff =R d ,ω res =1-R d .

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

[0081] Using a genetic algorithm, experts set the value range of each bait strategy parameter. Within the value range of each bait strategy parameter, the bait strategy parameter is uniformly randomly selected to generate a total of 100 individuals. Each individual consists of the bait strategy parameters:

[0082] C j =[ΔV j ,θ j ,σ d，j , n j ]

[0083] Where Cj represents the jth individual consisting of the decoy strategy parameters, ΔV j represents the speed difference of the jth individual composed of the decoy strategy parameters, θ j represents the azimuth dispersion of the jth individual consisting of the decoy strategy parameters, σ d，j represents the standard deviation of the distance of the jth individual consisting of the bait strategy parameters, n j The number of decoys in the jth individual composed of decoy strategy parameters, j = 1, 2, ..., 100, represents the individual's sequence number. For the speed difference, azimuth dispersion, and distance standard deviation, a uniformly distributed random number is generated within the set range. For the number of decoys, a random integer is generated within the set range. Kinematic simulation is performed on each individual parameter and defense environment parameter 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 the crossover operation is set to 0.8, and the trigger probability of the mutation operation is set to 0.05. Then, the tournament selection method is used to randomly select 3 individuals from the population each time, compare their fitness, retain the one with the highest fitness and enter the parent pool, repeat 100 times to generate the parent population, and perform arithmetic crossover on the selected parent with a probability of 0.8 to generate two offspring. The weight factor of the arithmetic crossover is randomly generated by the algorithm. For the number of baits, the integer crossover method is used. For the untriggered crossover, that is, the unselected parent, the offspring directly copies the parent. The Gaussian mutation method is used to perform a mutation operation on the bait strategy parameters of each offspring with a probability of 0.05. When the bait strategy parameters after the mutation operation exceed the value range, they are truncated back to the nearest value range. The individual with the highest fitness is selected from the parent and offspring as the optimal bait strategy parameter, and the corresponding initial evaluation value is recorded.

[0085] Calculate the sensitivity coefficient of bait strategy parameters to the initial effectiveness evaluation results:

[0086]

[0087] Where S i It represents the sensitivity coefficient of the i-th parameter, i represents the index of the bait strategy parameter, i=1, 2, 3, 4, respectively representing the speed difference, azimuth angle dispersion, distance standard deviation and number of baits. The sensitivity coefficients are sorted, and the i-th bait strategy parameter corresponding to the maximum sensitivity coefficient is regarded as a highly sensitive parameter.

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

[0089] The new decoy strategy parameters are evaluated through kinematic simulation to calculate the initial effectiveness and performance gap:

[0090]

[0091] Where Q represents the performance gap, E new represents the initial evaluation results of the new decoy strategy parameters calculated through kinematic simulation, E * It represents the initial evaluation results of the genetic algorithm output under the same kinematic simulation environment. When Q < 10%, the strategy is evaluated as excellent; when 10% ≤ Q < 25%, the strategy is evaluated as good; when Q ≥ 25%, the strategy is evaluated as poor.

[0092] In addition, a system for evaluating penetration effectiveness under decoy cover is provided. The system is used to execute the above-mentioned method for evaluating penetration effectiveness under decoy cover, comprising:

[0093] The kinematic simulation parameter construction module is used to set the defense environment parameters, release the decoys according to the initially formulated decoy strategy parameters, perform kinematic simulation on 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 total number of decoys and real units found;

[0094] A simulation result determination module is used to perform kinematic simulation on the motion trajectory of the launched interception unit and determine the result of the interception according to the kinematic simulation results of the motion trajectory 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, effective interceptions, total interceptions, and number of surviving decoys based on the results of multiple kinematic simulations. The defense resource consumption index is constructed based on the number of defense units per unit area, total interceptions, number of surviving decoys, and number of decoys. The penetration core effectiveness index is constructed based on the number of successful penetrations and effective interceptions.

[0096] The initial effectiveness evaluation calculation module is used to 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 weight based on the average detection probability of the defense system;

[0097] The penetration effectiveness evaluation module is used to adopt a genetic algorithm, take the initially formulated decoy strategy parameters as the input layer, and the initial effectiveness evaluation formula as the fitness function, output the optimal decoy strategy parameters and the corresponding initial effectiveness evaluation results, and evaluate the 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.

[0098] Compared with the prior art, the present invention has the following beneficial effects:

[0099] The present invention comprehensively considers speed difference, azimuth angle dispersion, distance standard deviation, number of decoys, number of defense units per unit area, and average detection probability, and calculates the number of successful penetrations, number of effective interceptions, and number of surviving decoys after kinematic simulation, thereby constructing a defense resource consumption index and a penetration core effectiveness index. The dynamic weight mechanism is used to weight the two to form an initial effectiveness evaluation formula, which enables the initial effectiveness evaluation formula to adapt to different scenarios, realizes a comprehensive evaluation of the defense resource consumption index and penetration performance, improves the matching degree between the evaluation results and the real scenario, avoids the one-sidedness of fixed weights, and evaluates the advantages and disadvantages of decoy strategy parameters through a genetic algorithm to improve penetration effectiveness. BRIEF DESCRIPTION OF THE DRAWINGS

[0100] Figure 1 Schematic diagram of the overall method flow of the present invention;

[0101] Figure 2 This is the analysis diagram of speed difference and initial efficiency evaluation results;

[0102] Figure 3 This is an analysis diagram of azimuth angle dispersion and initial effectiveness evaluation results;

[0103] Figure 4 It is a schematic diagram of the overall system module of the present invention. DETAILED DESCRIPTION

[0104] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is 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 the present invention should have the usual meanings understood by people with ordinary skills in the field to which the present invention belongs. The "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative position relationships. When the absolute position of the object being described changes, the relative position relationship may also change accordingly.

[0106] Example:

[0107] See also Figures 1 to 3 , the present invention provides a technical solution:

[0108] The specific steps of the penetration effectiveness evaluation method under decoy cover include:

[0109] Step 1: Set the defense environment parameters and release the decoys according to the initially formulated decoy strategy parameters. Perform kinematic simulations on the motion trajectories of the real units and the decoys based on the motion parameters of the real units and the decoy strategy parameters. Based on the number of decoys and real units found, launch the interception units according to the set interception parameters.

[0110] The speed difference of the decoy, that is, the absolute value difference between the speed of the decoy and the speed of the real unit, affects the consistency of the motion trajectory, and the defense system can easily identify the real unit through track association. When the speed difference is large, the motion trajectories of the decoy and the real unit are significantly different, which can increase the tracking filter error and the real unit association error rate of the defense system. The speed of the decoy needs to form a reasonable difference with the real unit to avoid being cleared by the defense system at one time due to speed synchronization. The azimuth angle discreteness is used to describe the degree of dispersion of the decoy in the azimuth dimension. The defense system usually determines the real unit orientation through multi-sensor fusion. The discrete distribution of the decoy in azimuth can create the illusion of multiple real units, forcing the defense system to allocate more resources for tracking and consuming its resources for real units. The processing capacity of the element is improved, thereby reducing the probability of the real unit being locked by the defense system; the distance standard deviation of the distance between the bait and the real unit describes the dispersion of the bait in the distance dimension. The discrete distribution of the bait in the distance can lead to the widening of the distance tracking gate of the defense system, increase the ranging error of the defense system and the difficulty of identifying the real unit; the number of baits directly affects the target saturation of the defense system. In theory, there is an upper limit to the multi-unit tracking capability of the defense system, thereby reducing the accuracy of tracking the real unit. Too many baits will increase the load weight and cost, and it is necessary to find the best between the interference effect and resource consumption. Therefore, in the kinematic simulation environment, the speed difference ΔV, azimuth angle dispersion θ, and distance standard deviation σ of the bait strategy parameters are input. d and the number of baits n as variables;

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

[0112]

[0113] Where N defense represents the total number of defense units deployed in the kinematic simulation area, such as radar stations and missile launch units, and A represents the area of the kinematic simulation area;

[0114] The formula for constructing the motion model of the real unit and the decoy is:

[0115]

[0116] Where V tar Indicates the movement speed of the real unit, V decoy represents the speed of the bait, θ tarRepresents the heading angle of the real unit, that is, the angle between the real unit velocity direction and the horizontal direction, θ decoy represents the heading angle of the bait, that is, the angle between the bait speed direction and the horizontal direction, x(t) represents the horizontal position of the real unit at the current moment, x * (t) represents the horizontal position of the bait at the current moment, Δt represents the time interval, x(t+1) represents the horizontal position of the real unit at the next moment, and x * (t+1) represents the horizontal position of the bait at the next moment, y(t) represents the vertical position of the real unit at the current moment, and y * (t) represents the vertical position of the bait at the current moment, y(t+1) represents the vertical position of the real unit at the next moment, and y * (t+1) represents the vertical position of the bait at the next moment.

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

[0118] The defense unit coordinates are used as the initial position of the interception unit, and the total number of decoys and real units found is set to 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, the coordinates of the meeting point between the interception unit and the decoy or real unit are (x meet ,y meet ), where the specific steps for calculating the coordinates of the meeting point are:

[0119] Calculate the time it takes for the interception unit to reach the coordinates of the encounter point:

[0120]

[0121] Where t1 represents the time when the interception unit reaches the coordinates of the encounter point, x Target Indicates the horizontal position of the real unit or decoy detected by the defense system at the current moment, y Target Indicates the vertical position of the real unit or decoy detected by the defense system at the current moment, V Target 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 coordinates of the encounter point:

[0123]

[0124] Where t2 represents the time when the decoy or real unit reaches the coordinates of the encounter point;

[0125] Let t1 = t2 and solve the simultaneous equations to calculate the coordinates of the meeting point between the interception unit and the decoy or real unit;

[0126] Calculate the interception unit launch angle:

[0127]

[0128] Where 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 the detection threshold d thresh1 = 1000, in meters, it is considered that the attacker is close to the target, and the interception threshold d thresh2 , in meters, is set based on the minimum effective interception distance of the interception unit, ensuring that the real unit can be effectively destroyed when approaching this distance. Calculate the distance between the real unit and the target:

[0130]

[0131] Where, d tar (t) represents the distance between the real unit and the target, x T Indicates the horizontal position of the target, y T Indicates the vertical position of the strike target, where the strike target is a fixed target that the defender needs to protect, such as an enemy command post, radar station, etc.

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

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

[0134]

[0135] Where 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 Drepresents the movement speed of the interception unit, Indicates the direction angle of the interception unit at the current moment. The calculation formula for the direction angle of the interception unit at the current moment is:

[0136]

[0137] Where x k (t) represents the horizontal position of the decoy or real unit tracked by the interception unit, y k (t) represents the vertical position of the decoy or real unit tracked by the interception unit;

[0138] When the interception unit is tracking the real unit, the distance between the interception unit and the real unit is calculated in real time:

[0139]

[0140] Where D k，tar (t) represents the distance between the interception unit and the true unit;

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

[0142]

[0143] Where D k，decoy (t) represents the distance between the interception unit and the bait;

[0144] When D k，tar (t) <d thresh2 When the real unit is found, the interception result is considered as effective interception. At this time, the interception unit successfully tracks the real unit and approaches it within the effective range, and has the ability to destroy the real unit. k，decoy (t) <d thresh2 When the interception result is , it is determined that the bait is discovered, so that the number of surviving baits can be calculated in the subsequent steps to quantify the interception resources wasted on the baits;

[0145] The kinematic simulation was performed using AnyLogic software. A Continuous2D model was created in AnyLogic. In the Parameters panel of the model, the following parameters were added: the area of the kinematic simulation area, the total number of defense units, the average detection probability of the defense system, the speed difference, the azimuth dispersion, the distance standard deviation, and the number of decoys. A real unit agent was created. After setting the speed of the real unit, it was placed at the starting point. The linear motion mode was selected and it was set to fly from the starting point to the kinematic simulation area. When the real unit moved to the boundary, it was judged to have successfully penetrated the defense. Based on the input number of decoys, a decoy agent was also created. Based on the set decoy strategy parameters, the number of decoys was selected and the real target was set as the center. The azimuth dispersion, distance standard deviation, distance to the real unit, and the same speed as the real unit were selected. A defense unit agent was created. Based on the total number of defense units, the defense units were uniformly and randomly distributed within the kinematic simulation area. A detection range was set, for example, to 50 km. When a real unit or decoy entered the detection range, as long as the discovered real unit was within the set interception threshold d thresh2 It is considered to be an effective interception if the real unit or the decoy is not discovered by any defense unit and successfully reaches the destination, or the real unit is discovered by the defense unit but the enemy intercepts the real unit but fails to hit it, and the distance d between the real unit and the target is tar (t) is less than the detection threshold d thresh1 In the case of D k，tar (t) <d thresh2 , that is, the number of interceptions of the real unit is determined as the effective number of interceptions; when D k，decoy (t) <d thresh2 When the interception result is determined as the decoy being 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 Statistics of simulation results

[0149] In the simulation result statistical table, these data columns cover the key parameters and result indicators that affect the penetration process. The speed difference, azimuth angle 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 input of its interference 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 the result indicators for measuring the penetration effect and resource consumption. By analyzing these data, the quality of the penetration effectiveness can be intuitively reflected, which helps to adjust the decoy strategy parameters and provide a data source for the subsequent calculation of the initial effectiveness evaluation formula.

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

[0151] Constructing a defense resource consumption index:

[0152]

[0153] Where, I res It represents the defense resource consumption index, which is used to measure the efficiency of the resources consumed by the defender in dealing with decoys and real units. The larger the value, the more resources the defender consumes to intercept real units and decoys. The defense system attacks the decoys and indirectly protects the real units. N intercept Indicates the total number of interceptions, reflecting the actual total amount of interception resources, that is, the direct resource consumption of the defense system to perform interception actions, such as the number of missile launches and radar tracking time. Therefore, N intercept The larger the value, the res The higher the value, the positive correlation is. surv represents the number of surviving decoys, n represents the number of real units and decoys, The ratio of the two indicates the survival efficiency of the bait. The more surviving baits are, the more baits have attracted the defense firepower and have not been destroyed, which requires the defender to continuously allocate resources to deal with them, and the defender needs to consume more resources. Therefore, the survival efficiency of the bait is related to I res There is a positive correlation. ρ reflects the density of the defense system, which can quantify the basic 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 the 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 the I resThe larger the value is, the three are multiplied together to form the effect of basic input resources, actual consumption resources and resource waste efficiency, so as to achieve the purpose of quantifying resource consumption under the synergistic influence of multiple factors.

[0154] First, calculate the probability of successful penetration, based on the formula:

[0155]

[0156] Where, P s represents the probability of successful penetration, N hit Indicates the number of effective interceptions, N s Indicates the number of successful penetrations;

[0157] Calculate the effective interception rate:

[0158]

[0159] Where, P hit Represents the effective interception rate, where the number of effective interceptions N hit It reflects the proportion of the defender's interception actions targeting real targets, which can reflect the degree of being misled by bait;

[0160] Constructing the core penetration effectiveness index:

[0161] I eff =P s ·(1-P hit )

[0162] Where, the probability of successful penetration P s 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. hit Reflects the proportion of intercepted real units, that is, the probability that the defender fails to effectively intercept real units, eliminating decoy interference and measuring the threat level of the defense system to real units. eff It represents the core penetration effectiveness index, which is used to measure the comprehensive ability of a real unit to penetrate the defense and avoid being effectively intercepted. It realizes the evaluation of the real unit that has both penetrated the defense boundary and not been effectively intercepted by the defense system. The two must be true at the same time to reflect the penetration effect of the real unit. Therefore, it is necessary to combine the two in the form of joint probability, that is, multiply the two. 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 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 penetration core effectiveness index, and construct the initial effectiveness evaluation formula through weighted summation:

[0165] E=ω res I res +ω eff I eff

[0166] Where, E represents the initial effect evaluation result, ω res Represents the weight coefficient of the defense resource consumption index, ω eff represents the weight coefficient of the penetration core effectiveness index, and ω res +ω eff = 1, comprehensively evaluate 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 to avoid the one-sided evaluation of the single dimension of the defender's resource consumption or the penetration success. res It is used to measure the resources consumed by the defender to intercept real units and decoys. The larger the value, the lower the probability of intercepting real units, and the more defense resources are invested. eff Measures the attacker's ability to break through defenses. A larger value indicates a better penetration effect. The two values quantify the combined impact of defense cost and penetration effect. When the E value is high, even if the penetration probability is low, the attacker may increase the ineffective consumption of defense resources through strategies. When the E value is low, it reflects a high probability of penetration failure under strong defense and limited defense system resource consumption. Therefore, it is necessary to carefully consider whether to follow this bait strategy parameter.

[0167] Constructing a defense strength index:

[0168]

[0169] Where R d Represents the defense strength index, P det represents the average detection probability of the defense system, R d The larger the value, the better the overall performance of the defense system in terms of detection and interception. eff =R d ,ω res =1-R d , where the average detection probability P det Reflects the ability of the defense system to detect real units or baits. When the defense strength R d When it is relatively high, it means that the defense system has superior performance. eff Larger, more emphasis is placed on the core effectiveness index of penetration, and successful penetration also makes I effA larger value further highlights the value of the initial effectiveness evaluation result, that is, when the performance of the defense system is superior, 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. On the contrary, even if the performance of the defense system is poor, but E is relatively small, that is, the defense system input resources and success rate are low, the bait strategy parameters should be checked.

[0170]

[0171]

[0172] Table 2 Simulation data calculation statistics

[0173] The preliminary effectiveness evaluation results in the simulation data calculation statistical 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 is exchanged for how much penetration effect is obtained, and then optimize the resource investment strategy to reach a decision with penetration success as the goal or resource consumption as the goal. For example, if the penetration core effectiveness index of an experimental group is high but the defense resource consumption index is high, the cost can be reduced by adjusting parameters such as the number of baits and speed difference.

[0174] Step 5: Use the genetic algorithm to take the initially formulated decoy strategy parameters as the input layer and the initial effectiveness evaluation formula as the fitness function, output the optimal decoy strategy parameters and the corresponding initial effectiveness evaluation results, and evaluate the 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.

[0175] Using a genetic algorithm, experts set the value range of each bait strategy parameter. Within the value range of each bait strategy parameter, the bait strategy parameter is uniformly randomly selected, and a total of 100 individuals are generated. This is a conventional setting that can effectively increase the possibility 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] Where C j represents the jth individual consisting of the decoy strategy parameters, ΔV j represents the speed difference of the jth individual composed of the decoy strategy parameters, θ j represents the azimuth dispersion of the jth individual composed of the decoy strategy parameters, σ d，jrepresents the standard deviation of the distance of the jth individual consisting of the bait strategy parameters, n j The number of decoys in the jth individual composed of decoy strategy parameters, j = 1, 2, ..., 100, represents the individual's sequence number. For the speed difference, azimuth dispersion, and distance standard deviation, a uniformly distributed random number is generated within the set range. For the number of decoys, a random integer is generated within the set range. Kinematic simulation is performed on each individual parameter and defense environment parameter to obtain the total number of interceptions, the number of surviving decoys, the number of successful penetrations, and the number of effective interceptions.

[0178] The maximum number of iterations is set to 200 to prevent the algorithm from falling into an infinite loop. The result of the initial evaluation formula is used as the fitness value. The trigger probability of the crossover operation is set to 0.8, and the trigger probability of the mutation operation is set to 0.05. Then, the tournament selection method is used to randomly select 3 individuals from the population each time, compare their fitness, and retain the one with the highest fitness to enter the parent pool. This is repeated 100 times to generate the parent population. With a probability of 0.8, arithmetic crossover is performed on the selected parent to generate two offspring. The weight factor of the arithmetic crossover is randomly generated by the algorithm. For the number of baits, the integer crossover method is used. For the untriggered crossover, that is, the unselected parent, the offspring directly copies the parent. The Gaussian mutation method is used to mutate the bait strategy parameters of each offspring with a probability of 0.05. When the bait strategy parameters after the mutation operation exceed the value range, they are truncated to the nearest value range. The individual with the highest fitness in the parent and offspring is selected as the optimal bait strategy parameter, and the corresponding initial evaluation value is 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 range 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 evaluation result shows an upward trend as the speed difference increases, and is positively correlated with the initial effectiveness evaluation result. The values of azimuth dispersion, range standard deviation, number of decoys, speed difference, and average detection probability of the defense system are all conventional and representative. This means that when other conditions remain unchanged, the greater the speed difference within a certain range, the higher the penetration effectiveness. This figure provides a basis for optimizing penetration strategies. In other words, increasing the speed difference can effectively improve the initial effectiveness evaluation results and consume enemy defense resources.

[0180] Figure 3It indicates that in order to observe the influence of azimuth angle dispersion on the initial effectiveness evaluation results alone, it is assumed that other independent variables take the following typical values: when the speed difference is 25m / s, the distance standard deviation is 400m, the number of decoys is 15, and the average detection probability of the defense system is 0.5, the figure shows that as the azimuth angle dispersion increases, the initial effectiveness evaluation results first rise and then fall, presenting a single-peak curve, which indicates that there is an optimal azimuth angle dispersion to maximize the initial effectiveness evaluation results. In the rising stage, the increase in azimuth angle dispersion makes the decoy more effectively consume the resources of the defense system, increases the difficulty of tracking and intercepting the defense system, and improves the initial effectiveness evaluation results. In the descending stage, the excessive azimuth angle dispersion causes the azimuth difference between the decoy and the real unit to be too obvious, which reduces the interference effect and the initial effectiveness evaluation result. This figure provides a basis for the optimization of the penetration strategy, that is, by adjusting the azimuth angle dispersion of the decoy to near the peak value, the optimal penetration effectiveness can be achieved.

[0181] Calculate the sensitivity coefficient of bait strategy parameters to the initial effectiveness evaluation results:

[0182]

[0183] Where S i It represents the sensitivity coefficient of the i-th parameter, i represents the index of the bait strategy parameter, i = 1, 2, 3, 4, respectively representing the speed difference, azimuth dispersion, distance standard deviation and number of baits. The sensitivity coefficients are sorted, and the i-th bait strategy parameter corresponding to the maximum sensitivity coefficient is regarded as a highly sensitive parameter and is adjusted first. The sensitivity coefficient S of the i-th parameter is i The sensitivity of the initial effect evaluation result E is measured. Can be split into The calculation reflects the rate of change of E when the parameters change slightly, S i The larger the value is, the more sensitive the initial evaluation result is to the i-th parameter, and the correlation is positive. The calculation not only considers the magnitude of the parameters themselves, but also normalizes the results. Highly sensitive parameters have a greater impact on the initial effectiveness evaluation results. Prioritizing parameter adjustment for new decoy strategies can achieve more effective penetration effects.

[0184] The new decoy strategy parameters are evaluated through kinematic simulation to calculate the initial effectiveness and performance gap:

[0185]

[0186] Where Q represents the performance gap, E new represents the initial evaluation results of the new decoy strategy parameters calculated through kinematic simulation, E *Indicates the initial effectiveness evaluation results of the genetic algorithm output under the same kinematic simulation environment. When Q < 10%, the strategy is evaluated as excellent; when 10% ≤ Q < 25%, the strategy is evaluated as good; when Q ≥ 25%, the strategy is evaluated as poor, and it is recommended to modify highly sensitive parameters;

[0187] Wherein, the performance gap reflects the degree of change in the initial effectiveness evaluation results of the new strategy relative to the optimal strategy. Although it is not necessarily the best penetration solution, it can intuitively judge the pros and cons 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 giving priority to modifying highly sensitive parameters.

[0188] See also Figure 4 The present invention further provides a system for evaluating penetration effectiveness under decoy cover, wherein the system is used to execute the above-mentioned method for evaluating penetration effectiveness under decoy cover, comprising:

[0189] The kinematic simulation parameter construction module is used to set the defense environment parameters, release the decoys according to the initially formulated decoy strategy parameters, perform kinematic simulation on 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 total number of decoys and real units found;

[0190] A simulation result determination module is used to perform kinematic simulation on the motion trajectory of the launched interception unit and determine the result of the interception according to the kinematic simulation results of the motion trajectory 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, effective interceptions, total interceptions, and number of surviving decoys based on the results of multiple kinematic simulations. The defense resource consumption index is constructed based on the number of defense units per unit area, total interceptions, number of surviving decoys, and number of decoys. The penetration core effectiveness index is constructed based on the number of successful penetrations and effective interceptions.

[0192] The initial effectiveness evaluation calculation module is used to 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 weight based on the average detection probability of the defense system;

[0193] The penetration effectiveness evaluation module is used to adopt a genetic algorithm, take the initially formulated decoy strategy parameters as the input layer, and the initial effectiveness evaluation formula as the fitness function, output the optimal decoy strategy parameters and the corresponding initial effectiveness evaluation results, and evaluate the 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.

[0194] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0195] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. Those skilled in the art will appreciate that the units and algorithm steps of each example 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 performed by 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, and the components shown as units may or may not be physical units, and may be located in one place or distributed across multiple grid units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0197] The above is only a specific implementation method of the present application, but the scope of protection of the present application is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed in this application, which should be covered by the scope of protection of the present application.

Claims

1. A method for evaluating penetration effectiveness under decoy cover, characterized by: The specific steps include: Step 1: Set the defense environment parameters and release the decoys according to the initially formulated decoy strategy parameters. Perform kinematic simulations on the motion trajectories of the real units and the decoys based on the motion parameters of the real units and the decoy strategy parameters. Launch the interceptor units based on the set interception parameters based on the total number of decoys and real units found. Step 2: Perform kinematic simulation on the motion trajectory of the launched interception unit, and determine the result of the interception based on the kinematic simulation results of the motion trajectory of the real unit, the decoy, and the interception unit; Step 3: Based on the results of multiple kinematic simulations, the number of successful penetrations, effective interceptions, total interceptions, and number of surviving decoys are obtained. A defense resource consumption index is constructed based on the number of defense units per unit area, total interceptions, number of surviving decoys, and number of decoys. A penetration core effectiveness index is constructed based on the number of successful penetrations and effective interceptions. Step 4: Construct an initial effectiveness evaluation formula by weighted summation of the penetration core effectiveness index and defense resource consumption index, and dynamically adjust the weights based on the average detection probability of the defense system. Step 5: Use the genetic algorithm, take the initially formulated decoy strategy parameters as the input layer, and the initial effectiveness evaluation formula as the fitness function, output the optimal decoy strategy parameters and the corresponding initial effectiveness evaluation results, and evaluate the 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.

2. The method for evaluating penetration effectiveness under decoy cover according to claim 1, characterized in that: According to the motion parameters of the real unit and the parameters of the bait strategy, the method for kinematic simulation of the motion trajectory of the real unit and the bait is: When performing kinematic simulation, the velocity difference ΔV, azimuth angle dispersion θ, and distance standard deviation σ of the decoy strategy parameters are input into the kinematic simulation environment. d and the number of decoys n, assuming the speed of each decoy is the same. The speed difference refers to the difference in movement speed between the decoy and the real unit. The azimuth dispersion refers to the degree of dispersion of the decoy's azimuth distribution with the real unit as the center. The distance standard deviation refers to the standard deviation of the distance between the decoy and the real unit with the real unit as the benchmark. The number of defense units per unit area is calculated as follows: Where N defense represents the total number of defense units deployed in the kinematic simulation area, A represents the area of the kinematic simulation area, and ρ represents the number of defense units per unit area; The formula for constructing the motion model of the real unit and the decoy is: Where V tar Indicates the movement speed of the real unit, V decoy represents the speed of the bait, θ tar Represents the heading angle of the real unit, that is, the angle between the real unit velocity direction and the horizontal direction, θ decoy represents the heading angle of the bait, that is, the angle between the bait speed direction and the horizontal direction, x(t) represents the horizontal position of the real unit at the current moment, x * (t) represents the horizontal position of the bait at the current moment, Δt represents the time interval, x(t+1) represents the horizontal position of the real unit at the next moment, and x * (t+1) represents the horizontal position of the bait at the next moment, y(t) represents the vertical position of the real unit at the current moment, and y * (t) represents the vertical position of the bait at the current moment, y(t+1) represents the vertical position of the real unit at the next moment, and y * (t+1) represents the vertical position of the bait at the next moment.

3. The method for evaluating penetration effectiveness under decoy cover according to claim 2, characterized in that: Based on the total number of decoys and real units found, the method for launching interception units according to the set interception parameters is as follows: The defense unit coordinates are used as the initial position of the interception unit, and the total number of decoys and real units found is set to 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, the coordinates of the meeting point between the interception unit and the decoy or real unit are (x meet ,y meet ), where the specific steps for calculating the coordinates of the meeting point are: Calculate the time it takes for the interception unit to reach the coordinates of the encounter point: Where t1 represents the time when the interception unit reaches the coordinates of the encounter point, x Target Indicates the horizontal position of the real unit or decoy detected by the defense system at the current moment, y Target Indicates the vertical position of the real unit or decoy detected by the defense system at the current moment, V Target 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 coordinates of the encounter point: Where t2 represents the time when the decoy or real unit reaches the coordinates of the encounter point; Let t1 = t2 and solve the simultaneous equations to calculate the coordinates of the meeting point between the interception unit and the decoy or real unit; Calculate the interception unit launch angle: Where α represents the launch angle of the interception unit.

4. The method for evaluating penetration effectiveness under decoy cover according to claim 2, characterized in that: The kinematic simulation of the motion trajectory of the launched interception unit is performed. The method for judging the result of the interception based on the kinematic simulation results of the motion trajectory of the real unit, the decoy, and the interception unit is as follows: Set the detection threshold d thresh1 =1000, in meters, interception threshold d thresh2 , in meters, calculate the distance between the real unit and the target: Where, d tar (t) represents the distance between the real unit and the target, x T Indicates the horizontal position of the target, y T Indicates the vertical position of the target; When d tar (t) <d thresh1 , the result of the interception is determined to be a successful penetration; Extract the positions pointing to the real unit and the decoy, that is, the heading angle of the interception unit. For each interception unit, calculate the position in real time: Where 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 represents the movement speed of the interception unit, Indicates the direction angle of the interception unit at the current moment. The calculation formula for the direction angle of the interception unit at the current moment is: Where x k (t) represents the horizontal position of the decoy or real unit tracked by the interception unit, y k (t) represents the vertical position of the decoy or real unit tracked by the interception unit; When the interception unit is tracking the real unit, the distance between the interception unit and the real unit is calculated in real time: Where D k，tar (t) represents the distance between the interception unit and the true unit; When the interception unit is tracking a decoy, the distance between the interception unit and the decoy is calculated in real time: Where D k，decoy (t) represents the distance between the interception unit and the bait; When D k，tar (t) <d thresh2 When the real unit is found, the interception result is considered as effective interception. k，decoy (t) <d thresh2 When the interception result is , it is determined that the bait is discovered.

5. The method for evaluating penetration effectiveness under decoy cover according to claim 2, characterized in that: The number of successful penetrations, effective interceptions, total interceptions, and number of surviving decoys were obtained from the results of multiple kinematic simulations. The method for constructing a defense resource consumption index based on the number of defense units per unit area, total interceptions, number of surviving decoys, and number of decoys is as follows: Set the number of kinematic simulations to M fz , and M fz is a positive integer ≥100; Count the number of times the interception results under all kinematic simulations are judged to be successful penetration, and add up the results and take the average value as the number of successful penetrations, which is recorded as N. s ; Calculate the number of surviving decoys in each kinematic simulation: N dsurv =n-n bfx Where N dsurv Indicates the number of baits surviving in a single kinematic simulation, n bfx It represents the number of baits found in the kinematic simulation. The number of baits surviving in each kinematic simulation is accumulated and the average value is taken as the number of baits surviving, which is recorded as N surv ; Count the number of effective interceptions in all kinematic simulations, add up the results and take the average value as the number of effective interceptions, which is recorded as N. hit ; Count the number of interception units launched by the defense system under all kinematic simulations, add up the results and take the average value as the total number of interceptions, which is recorded as N intercept ; Constructing a defense resource consumption index: Where, I res Indicates the defense resource consumption index.

6. The method for evaluating penetration effectiveness under decoy cover according to claim 5, characterized in that: The method for constructing the penetration core effectiveness index based on the number of successful penetrations, the total number of kinematic simulations, and the number of effective intercepts is as follows: The formula for calculating the probability of successful penetration is: Where, P s Indicates the probability of successful penetration; The effective interception rate is calculated based on the following formula: Where, P hit represents the effective interception rate; Constructing the core penetration effectiveness index: I eff =P s ·(1-P hit ) Where, I eff Represents the penetration core effectiveness index.

7. The method for evaluating penetration effectiveness under decoy cover according to claim 6, 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 method of dynamically adjusting the weights based on the average detection probability of the defense system is as follows: Calculate the average detection probability of the defense system under each kinematic simulation: Where, P ddet represents the average detection probability of the defense system under each kinematic simulation, n represents the number of decoys under the kinematic simulation, n0 represents the total number of decoys and real units found, and the total number of decoys and real units found under each kinematic simulation is counted. The results are accumulated and the average value is taken as the average detection probability of the defense system, which is recorded as P det ; Extract the defense resource consumption index and penetration core effectiveness index, and construct the initial effectiveness evaluation formula through weighted summation: E=ω res ·I res +oh eff ·I eff Where, E represents the initial effect evaluation result, ω res Represents the weight coefficient of the defense resource consumption index, ω eff represents the weight coefficient of the penetration core effectiveness index, and ω res +ω eff =1; Constructing a defense strength index: Where R d represents the defense strength index, so that ω eff =R d ,ω res =1-R d .

8. The method for evaluating penetration effectiveness under decoy cover according to claim 7, characterized in that: The method to output the optimal decoy strategy parameters and the corresponding initial effectiveness evaluation results is: Using a genetic algorithm, experts set the value range of each bait strategy parameter. Within the value range of each bait strategy parameter, the bait strategy parameter is uniformly randomly selected to generate a total of 100 individuals. Each individual consists of the bait strategy parameters: C j =[ΔV j ,the j ,s d，j ,n j ] Where C j represents the jth individual consisting of the decoy strategy parameters, ΔV j represents the speed difference of the jth individual composed of the decoy strategy parameters, θ j represents the azimuth dispersion of the jth individual consisting of the decoy strategy parameters, σ d，j represents the standard deviation of the distance of the jth individual consisting of the bait strategy parameters, n j The number of decoys in the jth individual composed of decoy strategy parameters, j = 1, 2, ..., 100, represents the individual's sequence number. For the speed difference, azimuth dispersion, and distance standard deviation, a uniformly distributed random number is generated within the set range. For the number of decoys, a random integer is generated within the set range. Kinematic simulation is performed on each individual parameter and defense environment parameter 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 the crossover operation is set to 0.8, and the trigger probability of the mutation operation is set to 0.

05. Then, the tournament selection method is used to randomly select 3 individuals from the population each time, compare their fitness, retain the one with the highest fitness and enter the parent pool, repeat 100 times to generate the parent population, and perform arithmetic crossover on the selected parent with a probability of 0.8 to generate two offspring. The weight factor of the arithmetic crossover is randomly generated by the algorithm. For the number of baits, the integer crossover method is used. For the untriggered crossover, that is, the unselected parent, the offspring directly copies the parent. The Gaussian mutation method is used to perform a mutation operation on the bait strategy parameters of each offspring with a probability of 0.

05. When the bait strategy parameters after the mutation operation exceed the value range, they are truncated back to the nearest value range. The individual with the highest fitness is selected from the parent and offspring as the optimal bait strategy parameter, and the corresponding initial evaluation value is recorded. Calculate the sensitivity coefficient of bait strategy parameters to the initial effectiveness evaluation results: Where S i It represents the sensitivity coefficient of the i-th parameter, i represents the index of the bait strategy parameter, i=1, 2, 3, 4, respectively representing the speed difference, azimuth angle dispersion, distance standard deviation and number of baits. The sensitivity coefficients are sorted, and the i-th bait strategy parameter corresponding to the maximum sensitivity coefficient is regarded as a highly sensitive parameter.

9. The method for evaluating penetration effectiveness under decoy cover according to claim 8, 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 new decoy strategy parameters are evaluated through kinematic simulation to calculate the initial effectiveness and performance gap: Where Q represents the performance gap, E new represents the initial evaluation results of the new decoy strategy parameters calculated through kinematic simulation, E * It represents the initial evaluation results of the genetic algorithm output under the same kinematic simulation environment. When A<10%, the strategy is evaluated as excellent; when 10%≤A<25%, the strategy is evaluated as good; when A≥25%, the strategy is evaluated as poor.

10. The penetration effectiveness evaluation system under decoy cover is characterized by: The system is used to execute the penetration effectiveness evaluation method under decoy cover according to any one of claims 1 to 9, comprising: The kinematic simulation parameter construction module is used to set the defense environment parameters, release the decoys according to the initially formulated decoy strategy parameters, perform kinematic simulation on 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 total number of decoys and real units found; A simulation result determination module is used to perform kinematic simulation on the motion trajectory of the launched interception unit and determine the result of the interception according to the kinematic simulation results of the motion trajectory of the real unit, the decoy and the interception unit; The core index calculation module is used to obtain the number of successful penetrations, effective interceptions, total interceptions, and number of surviving decoys based on the results of multiple kinematic simulations. The defense resource consumption index is constructed based on the number of defense units per unit area, total interceptions, number of surviving decoys, and number of decoys. The penetration core effectiveness index is constructed based on the number of successful penetrations and effective interceptions. The initial effectiveness evaluation calculation module is used to 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 weight based on the average detection probability of the defense system; The penetration effectiveness evaluation module is used to adopt a genetic algorithm, take the initially formulated decoy strategy parameters as the input layer, and the initial effectiveness evaluation formula as the fitness function, output the optimal decoy strategy parameters and the corresponding initial effectiveness evaluation results, and evaluate the 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.

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