An Intelligent Method for Matching Ammunition with Targets

Abstract

The present invention relates to an intelligent method for matching ammunition with targets, belonging to the technical field of combat targets. The present invention can allocate ammunition and targets under the constraints of maximum damage effectiveness and minimum ammunition value, and is a joint fire strike operation implemented on multiple combat targets, featuring good reliability and high accuracy, and meeting the requirements of war complexity, accuracy, and real-time performance.

Description

Technical Field

[0001] The present invention belongs to the technical field of combat targets, and particularly relates to an intelligent method for matching ammunition with targets. Background Art

[0002] It is very difficult to solve the problem of matching ammunition with targets. First of all, basically all the matching of ammunition with targets in battles is dynamic. Affected by the changeable factors on the battlefield, the types and quantities of available ammunition and targets are not fixed. More importantly, the striking effectiveness of ammunition against targets also changes at all times, increasing the uncertainty of the problem of matching ammunition with targets that is difficult to estimate. Secondly, there are various connections among various types of ammunition and various types of targets, forming a complex decision-making system. Like the combat effectiveness, some connections are time-varying, resulting in many connections being difficult to accurately quantify, and it is very difficult to make a decision on the optimal plan. Moreover, the requirements of combat missions are qualitative and it is difficult to quantify them. Especially when the requirements of combat missions are not very clear or there are too many combat missions, how to meet the requirements of combat missions in the process of solving the problem of matching ammunition with targets is a difficult problem. And, the core of the problem of matching ammunition with targets is to solve a large-scale non-linear integer programming model, which has been proved to be an NP-hard combinatorial optimization problem, and there is still a lot of work to do on how to design a reasonable algorithm with high efficiency for solving.

[0003] For a long time, the problem of matching ammunition with targets has been a research hotspot. Especially in terms of algorithms, a large number of solving methods have been proposed for different firepower distribution models to improve the solving speed and accuracy of the model to meet the requirements of the complexity, accuracy and real-time nature of war. Summary of the Invention

[0004] (1) Technical Problems to be Solved

[0005] The technical problem to be solved by the present invention is: how to design an intelligent method for matching ammunition with targets with good reliability and high accuracy.

[0006] (2) Technical Solutions

[0007] To solve the above technical problems, the present invention provides an intelligent method for matching ammunition with targets. This method is a method for realizing the intelligent matching of ammunition with targets, and includes the following steps:

[0008] S1: Establish a target value evaluation model based on the principal component analysis method;

[0009] S2: Establish a target threat degree evaluation model;

[0010] S3: Establish an ammunition value evaluation model;

[0011] S4: Construct a projectile-target matching model based on the target value evaluation model, the target threat level evaluation model, and the ammunition value evaluation model;

[0012] S5: Use the genetic algorithm to solve the projectile-target matching model to obtain the projectile-target matching result.

[0013] Preferably, in step S1, according to the index data describing the target, the value of the target is evaluated by the principal component analysis method.

[0014] Preferably, step S1 specifically includes:

[0015] S11: Suppose there are n targets, and each target is described by p indicators, and construct an index data matrix Z:

[0016]

[0017] S12: Use the Z-score method to perform a standardized transformation on the indicators;

[0018] S13: Calculate the correlation matrix R of the index data matrix Z:

[0019]

[0020] Where:

[0021]

[0022] S14: Solve the p eigenvalues λ g , g = 1, 2,.., p, and each eigenvalue corresponds to an eigenvector (I g1 , I g2 , …, I gp ), and convert the standardized indicators into principal components:

[0023] F g = I g1 z1 + I g2 z2 + … + I gp z p

[0024] In the formula, g is the gth principal component, and z p is the pth indicator of the target;

[0025] S15: Select q principal components so that the cumulative contribution rate reaches more than 85%:

[0026]

[0027] S16: Perform a weighted sum on the q principal components to obtain the final evaluation value of the target:

[0028]

[0029] Preferably, in step S2, the threat level is described by the speed and distance of the target.

[0030] Preferably, step S2 specifically includes:

[0031] S21: Let the speed of the target be v and the maximum speed be v max , then the speed threat level of the target is:

[0032] w v = v / v max

[0033] S22: Let the distance between the target and our weapon be D, the maximum attack distance of our weapon be D w , the maximum attack distance of the target be D d , then the distance threat level of the target is:

[0034]

[0035] Wherein, the weapon is the ammunition;

[0036] S23: The total threat level of the target is expressed as:

[0037] W = (w v + w d ) / 2

[0038] Preferably, in step S3, the total value of the ammunition is calculated through the quantity matrix and value matrix of the ammunition.

[0039] Preferably, step S3 specifically includes:

[0040] S31: Suppose there are n targets and m types of ammunition. The quantity matrix of each type of ammunition is expressed as C = [c1, c2,..., c m , and the value matrix is expressed as V = [v1, v2,..., v m , then the weapon - target allocation plan is expressed as:

[0041]

[0042] Wherein, x ij represents the quantity of the j-th type of ammunition used for the i-th target, i = 1, 2,..., n, j = 1, 2,..., m;

[0043] S32: The total value of all the used ammunition is:

[0044]

[0045] v j is the value of the j-th type of ammunition.

[0046] Preferably, in step S4, an objective function is constructed with the maximum damage effectiveness and the minimum ammunition value as the objectives.

[0047] Preferably, step S4 specifically includes:

[0048] S41: The kill probability of the j-th type of ammunition against the i-th target is e ij , according to the allocation plan X nm , the kill probability of using a certain number of the j-th type of ammunition to attack the i-th target is:

[0049]

[0050] Then the damage probability p of all m types of ammunition against target i i is:

[0051]

[0052] Then the damage effectiveness is:

[0053]

[0054] Among them, ω i is the total threat level of the i-th target calculated using step S2, and F i is the final evaluation value of the i-th target calculated using step S1;

[0055] S43: Establish a constrained optimization problem:

[0056]

[0057]

[0058]

[0059] Preferably, in step S5, the NAGA-II algorithm is used to solve the objective function, including:

[0060] S51: Adopt decimal coding. Each chromosome consists of weapon numbers arranged in the order of targets, representing a possible allocation plan;

[0061] S52: Initialize the parental population according to the number of targets and the types of ammunition;

[0062] S53: Perform a fast non-dominated sorting on the parental population to obtain the Pareto ranks of all individuals, and calculate the crowding degree at the same time;

[0063] S55: Cross and mutate to obtain the offspring population;

[0064] S56: Mix the parental population and the offspring population, and determine the Pareto rank and crowding degree of the individuals in the mixed population;

[0065] S57: Adopt the elitist strategy to select excellent individuals from the mixed population to generate a new parental population; the excellence of the mixed population is judged according to a preset index;

[0066] S58: Repeat steps S55 - S57 until the set number of evolutionary generations is reached, and finally obtain the missile - target matching result.

[0067] (III) Beneficial Effects

[0068] The present invention can allocate ammunitions and targets with the maximum damage effectiveness and the minimum ammunition value as the constraint conditions, and is a joint fire - strike operation on multiple combat targets, featuring good reliability and high accuracy, and meeting the requirements of the complexity, accuracy, and real - time nature of war. Brief Description of the Drawings

[0069] Figure 1 It is the flowchart of the method of the present invention. Detailed Embodiments

[0070] To make the objectives, contents, and advantages of the present invention clearer, the following further describes the detailed embodiments of the present invention in conjunction with the drawings and embodiments.

[0071] As Figure 1 shown, a method for intelligent matching of ammunitions and targets provided by the present invention includes the following steps:

[0072] S1: Establish a target value evaluation model based on the principal component analysis method;

[0073] S2: Establish a target threat degree evaluation model;

[0074] S3: Establish an ammunition value evaluation model;

[0075] S4: Construct a missile - target matching model based on the target value evaluation model, the target threat degree evaluation model, and the ammunition value evaluation model;

[0076] S5: Use an improved genetic algorithm to solve the missile - target matching model to obtain the missile - target matching result.

[0077] Furthermore, in step S1, according to the index data describing the target, the value of the target is evaluated by the principal component analysis method, which specifically includes:

[0078] S11: Assume there are n targets, and each target is described by p items of indexes, and construct an index data matrix Z,

[0079]

[0080] S12: Standardize the indicators using the Z-score method;

[0081] S13: Calculate the correlation matrix R of the indicator data matrix Z:

[0082]

[0083] Where:

[0084]

[0085] S14: Solve for the p eigenvalues λ of the correlation matrix R g (g = 1, 2,.., p), and each eigenvalue corresponds to an eigenvector (I g1 , I g2 , …, I gp ), and convert the standardized indicators into principal components:

[0086] F g = I g1 z1 + I g2 z2 + … + I gp z p

[0087] In the formula, g is the g-th principal component, and z p is the p-th indicator of the target;

[0088] S15: Select q principal components so that the cumulative contribution rate reaches more than 85%:

[0089]

[0090] S16: Perform weighted summation on the q principal components to obtain the final evaluation value of the target:

[0091]

[0092] Furthermore, in step S2, describe the threat level using the speed and distance of the target, specifically including:

[0093] S21: Let the speed of the target be v, and the maximum speed be v max , then the speed threat level of the target is:

[0094] w v = v / v max

[0095] S22: Let the distance between the target and our weapon (i.e., ammunition) be D, the maximum attack distance of our weapon be D w , and the maximum attack distance of the target be D d , then the distance threat level of the target is:

[0096]

[0097] S23: The total threat level of the target is expressed as:

[0098] W = (w v + w d ) / 2.

[0099] Furthermore, in step S3, the total value of the ammunition is calculated through the quantity matrix and value matrix of the ammunition, specifically including:

[0100] S31: Suppose there are n targets and m types of ammunition. The quantity matrix of each type of ammunition is expressed as C = [c1, c2,..., c m , and the value matrix is expressed as V = [v1, v2,..., v m . Then the weapon - target assignment plan is expressed as:

[0101]

[0102] where x ij represents the quantity of the j - th type of ammunition used for the i - th target, i = 1, 2,..., n, j = 1, 2,..., m;

[0103] S32: The total value of all the used ammunition is:

[0104]

[0105] v j is the value of the j - th type of ammunition.

[0106] Furthermore, in step S4, with the maximum damage effectiveness and the minimum ammunition value as the goals, an objective function is constructed, specifically including:

[0107] S41: The kill probability of the j - th type of ammunition against the i - th target is e ij . According to the assignment plan X nm , the kill probability of using a certain quantity of the j - th type of ammunition to attack the i - th target is:

[0108]

[0109] Then the damage probability p i of all m types of ammunition against target i is:

[0110]

[0111] Then the damage effectiveness is:

[0112]

[0113] where ωi F is the total threat level of the i-th target calculated using step S2 i is the final evaluation value of the i-th target calculated using step S1;

[0114] S43: Establish a constrained optimization problem:

[0115]

[0116]

[0117]

[0118] Furthermore, in step S5, the NAGA-II algorithm is used to solve the objective function, including:

[0119] S51: Adopt decimal coding. Each chromosome consists of weapon numbers arranged in the order of targets, representing a possible allocation scheme;

[0120] S52: Initialize the parental population according to the number of targets and types of ammunition;

[0121] S53: Perform fast non-dominated sorting on the parental population to obtain the Pareto ranks of all individuals, and calculate the crowding degree at the same time;

[0122] S55: Cross and mutate to obtain the offspring population;

[0123] S56: Mix the parental population and the offspring population, and determine the Pareto ranks and crowding degrees of the individuals in the mixed population;

[0124] S57: Adopt the elitist strategy to select excellent individuals from the mixed population to generate a new parental population;

[0125] S58: Repeat steps S55 to S57 until the set number of evolutionary generations is reached, and finally obtain the result of projectile-target matching.

[0126] The above are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the technical principle of the present invention, several improvements and deformations can be made, and these improvements and deformations should also be regarded as the protection scope of the present invention.

Claims

1. An intelligent method for matching ammunition with a target, characterized in that, This method is a method for realizing the intelligent matching of ammunition and targets, including the following steps: S1: Establish a target value evaluation model based on the principal component analysis method; S2: Establish a target threat level evaluation model; S3: Establish an ammunition value evaluation model; S4: Construct a projectile-target matching model based on the target value evaluation model, the target threat level evaluation model, and the ammunition value evaluation model; S5: Use the genetic algorithm to solve the projectile-target matching model to obtain the projectile-target matching result; In step S2, the threat level is described by using the speed and distance of the target; Step S2 specifically includes: S21: Let the speed of the target be v and the maximum speed be v max , then the speed threat degree of the target is: w v = v / v max S22: Let the distance between the target and our weapon be D, the maximum attack distance of our weapon be D w , and the maximum attack distance of the target be D d , then the distance threat level of the target is as follows: Among them, the weapon is the ammunition; S23: The total threat level of the target is expressed as: W = (w v + w d ) / 2 In step S3, the total value of the ammunition is calculated through the quantity matrix and value matrix of the ammunition; Step S3 specifically includes: S31: Suppose there are n targets and m types of ammunition. The quantity matrix of each type of ammunition is represented as C = [c1, c2, …, c m , and the value matrix is represented as V = [v1, v2, …, v m . Then the weapon-target assignment plan is represented as: where x ij represents the quantity of the j-th type of ammunition used by the i-th target, where i = 1, 2, …, n and j = 1, 2, …, m; S32: The total value of all used ammunition is: v j is the value of the j-th type of ammunition.

2. The method according to claim 1, characterized in that, In step S1, according to the index data describing the target, the value of the target is evaluated by the principal component analysis method.

3. The method according to claim 2, wherein Step S1 specifically includes: S11: Suppose there are n targets, each target is described by p items of indexes, and construct an index data matrix Z: S12: Use the Z-score method to perform a standardized transformation on the indexes; S13: Calculate the correlation matrix R of the index data matrix Z; Among them: S14: Solve for the p eigenvalues λ of the correlation matrix R g , where g = 1, 2,.., p, and each eigenvalue corresponds to an eigenvector (I g1 , I g2 , …, I gp ), and convert the standardized indicators into principal components: F g = I g1 z1 + I g2 z2 + … + I gp z p where F g is the g-th principal component, and z p is the p-th index of the target; S15: Select q principal components so that the cumulative contribution rate reaches more than 85%; S16: Perform a weighted sum on the q principal components to obtain the final evaluation value of the target:

4. The method according to claim 1, characterized in that In step S4, with the maximum damage effectiveness and the minimum ammunition value as the goals, construct an objective function.

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