A Method for Aiming Point Planning to Strike Cluster Targets

Abstract

The present invention relates to a method for aiming point planning for striking clustered targets, and relates to the field of projectile shooting theory. It addresses the problem of a lack of research related to aiming point planning for clustered targets in the prior art. The method comprises: determining an optimization region and the sector-shaped intervals for each weapon to aim at; randomly encoding the chromosomal genes of each individual to obtain an initial population; for the current generation population, setting corresponding constraint ranges based on the sector-shaped intervals for each weapon to aim at, decoding the corresponding chromosomal gene segments in the individuals to obtain each set of aiming point coordinates; determining the fitness value of the corresponding individual based on each set of aiming point coordinates; determining whether an end condition is met; if so, obtaining the individual with the highest fitness value, and using the set of aiming point coordinates decoded from the individual with the highest fitness value as the optimal set of aiming point coordinates to end the iteration; otherwise, generating a new generation population as the current generation population, and jumping to the step of decoding the current generation population.

Description

Technical Field

[0001] The present invention relates to the field of projectile shooting theory, and in particular to a method for planning aiming points for striking cluster targets. Background Art

[0002] The field of projectile firing theory primarily involves issues such as aimpoint planning, kill probability, and ammunition allocation, with aimpoint planning being the most important. Because all strikes against the same target by multiple weapons in the same round have the same error, the probability of hitting the target can be increased by firing around it rather than directly at it. Therefore, the study of aimpoint planning has become a key issue of considerable interest in recent years.

[0003] The aimpoint planning problem primarily consists of a combination of target damage probability and hit probability. Currently, most research on aimpoint planning involves optimizing the aimpoints of multiple weapons against a single target or a surface target containing multiple identical targets. Most approaches use a method that calculates and traverses the damage probability of the target area. However, such methods prolong computation time and are inefficient, failing to meet the technical requirements for rapid strike and assessment. Furthermore, research on aimpoint planning for multiple weapons against clustered targets composed of different sub-targets is lacking.

[0004] When a weapon strikes a target, it is affected by target position error and ballistic dispersion error. Target position error is caused by errors in identifying the target's position, while ballistic dispersion error is affected by the weapon's firing accuracy, which varies from weapon to weapon. When planning aimpoints for clustered targets, these errors must be taken into account. Existing methods use the Monte Carlo method to determine the target damage probability when no closed-form solution is available. In each calculation, the impact points of all shots are summed with the randomly assigned weapon launch accuracy based on the aimpoint coordinates. The weapon's damage effectiveness function between a specific weapon and a specific target is then used to evaluate the damage probability of the weapon on the target in each calculation, thus accounting for the impact of random errors in aimpoint planning. Currently, existing aimpoint planning methods, both domestically and internationally, focus on planning for single targets, and most employ methods such as traversal or uniformly distributing aimpoints. However, these methods increase computational time, reduce precision, and affect the accuracy of the results. Therefore, these methods are limited in their application to aimpoint planning for clustered targets. Summary of the Invention

[0005] In view of the above analysis, an embodiment of the present invention aims to provide an aiming point planning method for striking cluster targets, so as to solve the problem of lack of research related to aiming point planning for cluster targets in the prior art.

[0006] The present invention discloses a method for planning aiming points for striking cluster targets, comprising:

[0007] The optimization region is determined based on the damage level of each sub-target in the cluster target and the distance from each sub-target to the center of gravity of the cluster target; the optimized region is evenly divided to obtain the sector range for each weapon to aim at;

[0008] The initial population is obtained by randomly encoding the chromosome genes of each individual; the individual is composed of multiple chromosome gene segments, each chromosome gene segment corresponds to a target point coordinate, and each individual corresponds to a set of target point coordinates;

[0009] For the current generation population, a corresponding constraint range is set based on the sector interval of each weapon's aiming, and the corresponding chromosome gene segment in the individual is decoded to obtain each set of aiming point coordinates; based on each set of aiming point coordinates, multiple sets of actual hit point coordinates corresponding to each set of aiming point coordinates and the total damage probability of the cluster target corresponding to each set of actual hit point coordinates are obtained, and the fitness value of the corresponding individual is determined based on the total damage probability of the cluster target; it is judged whether the individual's fitness value meets the expectation, or whether the maximum number of iterations has been reached.

[0010] If yes, obtain the individual with the highest fitness value, and use the set of aiming point coordinates decoded by the individual with the highest fitness value as the best set of aiming point coordinates, and end the iteration;

[0011] Otherwise, generate a new generation population as the current generation population and jump to the step of decoding the current generation population.

[0012] Based on the above solution, the present invention also makes the following improvements:

[0013] Furthermore, based on each set of aiming point coordinates, obtaining multiple sets of actual hit point coordinates corresponding to each set of aiming point coordinates and the total damage probability of cluster targets corresponding to each set of actual hit point coordinates, and determining the fitness value of the corresponding individual based on the total damage probability of the cluster targets includes:

[0014] Each time the target aiming error and ballistic dispersion error are randomly generated, a set of actual impact point coordinates corresponding to the set of aiming points are obtained;

[0015] Based on each set of actual hit point coordinates, determine the corresponding total damage probability of the cluster target;

[0016] The average value of the total damage probability of cluster targets of multiple groups of actual hit point coordinates is obtained, and the average value is used as the fitness value of the individual corresponding to the group of aiming points.

[0017] Furthermore, determining the corresponding total probability of damage to cluster targets includes:

[0018] Based on each set of actual hit point coordinates, obtain the damage effectiveness function of each actual hit point on each sub-target;

[0019] Based on the damage effectiveness function, the total damage probability PD_total of the cluster target is determined:

[0020]

[0021] Among them, PD ij represents the damage effectiveness function of the jth actual hit point in a set of actual hit points on the i-th sub-target, α i represents the damage weight of the i-th sub-target, J represents the total number of weapons, and k represents the total number of sub-targets.

[0022] Furthermore, the damage effectiveness function is calculated as follows:

[0023]

[0024] x imp_j 、y imp_j Respectively represent the x-axis and y-axis coordinates of the actual hit point corresponding to the j-th aiming point in the center of gravity coordinate system with the center of gravity of the cluster target as the origin; WR r_ij WR d_ij They represent the damage effectiveness constants of the j-th weapon on the i-th sub-target in the range direction and the deflection direction respectively; M i 、N i They represent the center coordinates of the i-th sub-target in the barycentric coordinate system.

[0025] Furthermore, the actual hit point coordinates are calculated as follows:

[0026]

[0027] Among them, x aim_j 、y aim_j They represent the x-axis and y-axis coordinates of the j-th aiming point in a set of aiming points in the center of gravity coordinate system; S x_j 、S y_j Respectively represent the target aiming error of the j-th aiming point in the x-axis and y-axis directions, σ x_j ,σ y_j They represent the ballistic dispersion errors of the j-th aiming point in the x-axis and y-axis directions respectively, and randn() represents taking a random number.

[0028] Furthermore, combining formulas (4)-(6), we can obtain WR r_ij WR d_ij :

[0029]

[0030] a=MAX(1-0.8cosI,0.3) (5)

[0031] Where a represents the aspect ratio and I represents the corner;

[0032] A L_ij =π×WR r_ij ×WR d_ij (6)

[0033] A L_ij It represents the effective killing area of the j-th weapon on the i-th sub-target.

[0034] Further, the optimized region is determined by performing the following operations:

[0035] Based on the damage level of each sub-target in the cluster target, the damage effectiveness function of each weapon on each sub-target is established to obtain the maximum damage radius of the weapon on the sub-target;

[0036] Get the maximum distance from the sub-target to the center of gravity of the cluster target;

[0037] Taking the sum of the maximum damage radius and the maximum distance as the optimization constraint radius;

[0038] The inner area of a circle with the cluster target center as the circle center and the optimization constraint radius as the radius is used as the optimization area.

[0039] Furthermore, obtaining the maximum damage radius of the weapon to the sub-target includes:

[0040] Set the damage effectiveness function of each weapon on each sub-target to 0 to obtain the coordinates of the impact point of each target relative to each weapon;

[0041] Select the maximum absolute value in the x-axis and y-axis directions from the obtained coordinates of the impact points of each target relative to each weapon;

[0042] The maximum damage radius is calculated based on the maximum absolute values in the selected x-axis and y-axis directions.

[0043] Furthermore, the optimization area is evenly divided with the center of gravity of the cluster target as the center of the circle and the optimization constraint radius as the radius to obtain sector-shaped intervals with the same center angle and aimed at by each weapon.

[0044] Furthermore, the setting of corresponding constraint ranges based on the sector intervals of each weapon aiming includes:

[0045] Based on the starting angle, ending angle and radius of the central angle of the sector-shaped interval aimed at by each weapon as constraints, the corresponding constraint range is set to ensure that the coordinates of the aiming point obtained by decoding the corresponding chromosome gene segment fall within the corresponding sector-shaped interval.

[0046] Compared with the prior art, the present invention can achieve at least one of the following beneficial effects:

[0047] The aiming point planning method for striking cluster targets provided by the present invention has the following advantages:

[0048] 1. This method can plan the aiming points of different weapons for different sub-targets, and characterize the damage effectiveness of weapons on targets in the form of damage effectiveness functions, which has better scalability.

[0049] 2. This method further considers the target position error and trajectory dispersion error, uses the Monte Carlo method to simulate the hit point, and calculates the average target damage probability including these two errors, which is closer to actual combat conditions.

[0050] 3. This method uses a combination of Monte Carlo method and genetic algorithm to plan the target aiming point, without traversing all grids. While ensuring accuracy, it greatly saves computing time and computing resources.

[0051] 4. This method can effectively improve the accuracy of the target aiming point, reduce the optimization time, and can cause the best damage effect with the least ammunition, providing important guarantees for combat firepower planning.

[0052] In the present invention, the above-mentioned technical solutions can be combined with each other to achieve more preferred combinations. Other features and advantages of the present invention will be described in the following description, and some advantages will become apparent from the description or be learned through practice of the present invention. The objectives and other advantages of the present invention can be realized and obtained through the contents particularly pointed out in the description and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The accompanying drawings are only for the purpose of illustrating particular embodiments and are not to be considered limiting of the present invention. Like reference symbols denote like parts throughout the drawings.

[0054] Figure 1 A flow chart of a method for planning aiming points for striking cluster targets provided in an embodiment of the present invention;

[0055] Figure 2 A flow chart of another aiming point planning method for striking cluster targets provided in an embodiment of the present invention;

[0056] Figure 3 Schematic diagram of target optimization constraint radius in an embodiment of the present invention;

[0057] Figure 4 Schematic diagram of the aiming point error of a single weapon in an embodiment of the present invention;

[0058] Figure 5 Graph showing the relationship between the number of iterations, the best fitness value, and the average fitness value in an embodiment of the present invention;

[0059] Figure 6 Schematic diagram of the optimal aiming point of the target in an embodiment of the present invention. DETAILED DESCRIPTION

[0060] The preferred embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, and are not used to limit the scope of the present invention.

[0061] Example 1

[0062] A specific embodiment 1 of the present invention discloses a method for planning aiming points for attacking cluster targets, the flow chart of which is as follows: Figure 1 As shown, the following steps are included:

[0063] Step S1: Determine an optimization region based on the damage level of each sub-target in the cluster target and the distance from each sub-target to the center of gravity of the cluster target; evenly divide the optimization region to obtain the sector intervals targeted by each weapon;

[0064] Step S2: Randomly encode the chromosome genes of each individual to obtain an initial population; the individual is composed of multiple chromosome gene segments, each chromosome gene segment corresponds to a target point coordinate, and each individual corresponds to a set of target point coordinates;

[0065] Step S3: For the current generation population, set corresponding constraint ranges based on the sector intervals aimed at by each weapon, decode the corresponding chromosome gene segments in the individuals, and obtain each set of aiming point coordinates; based on each set of aiming point coordinates, obtain multiple sets of actual hit point coordinates corresponding to each set of aiming point coordinates and the total damage probability of the cluster target corresponding to each set of actual hit point coordinates; determine the fitness value of the corresponding individual based on the total damage probability of the cluster target; determine whether the fitness value of the individual meets the expectation, or whether the maximum number of iterations has been reached.

[0066] If yes, obtain the individual with the highest fitness value, and use the set of aiming point coordinates decoded by the individual with the highest fitness value as the optimal aiming point coordinates, and end the iteration;

[0067] Otherwise, a new generation population is generated as the current generation population, and step S3 is repeated.

[0068] It should be noted that this embodiment does not impose excessive restrictions on weapons, as long as they can achieve cluster target aiming point planning, such as kill bombs. This embodiment covers two implementation methods for multi-weapon cluster target aiming point planning: first, multiple weapons simultaneously fire bullets at cluster targets at the same time; second, the same weapon continuously fires multiple bullets at cluster targets, with each firing process being treated as an independent event. Multiple firing processes also constitute "multi-weapon cluster target aiming point planning."

[0069] Specifically, in step S1, in order to more conveniently optimize the aiming point planning problem of multiple weapons against cluster targets, it is necessary to establish multiple aiming points and each sub-target in the same coordinate system. Therefore, in step S1, first execute:

[0070] Step S11: Establish a barycentric coordinate system based on the barycentricity of the cluster target and calculate the coordinates of each sub-target in the barycentric coordinate system. The specific process is described as follows:

[0071] Step S111: randomly selecting the coordinate origin of the ground coordinate system and establishing the ground coordinate system, and determining the center coordinates of each sub-target in the ground coordinate system based on the relative positional relationship between the sub-targets in the cluster target;

[0072] Step S112: connecting the center coordinates of each sub-target in the ground coordinate system in a clockwise or counterclockwise manner to form a planar polygon;

[0073] Step S113: Connect the center coordinates of each sub-target with the coordinate origin of the ground coordinate system to form a plurality of finite triangles; the number of triangles is equal to the number of sub-targets in the cluster target;

[0074] For example, if the cluster target includes k sub-targets, after step S12 and step S13, k finite triangles can be obtained, which are respectively recorded as X1, X2, ..., X k , the coordinates of the center of gravity of each triangle are recorded as C1, C2, ..., C k , the areas are denoted as A1, A2, ..., A k .

[0075] Step S114: Based on the centroids and areas of all triangles, determine the coordinates of the centroid of the plane polygon in the ground coordinate system (C x ,C y ):

[0076]

[0077]

[0078] Among them, C i_x、C i_y A represents the x-axis and y-axis coordinates of the center of gravity of the i-th triangle in the ground coordinate system; i Represents the area of the i-th triangle; i ranges from 1 to k, and k represents the total number of sub-targets in the cluster target. Figure 3 As shown in the figure, the cluster target includes 5 sub-targets, which can be divided into 5 triangles, represented as X1-X5 respectively.

[0079] Step S115: Taking the center of gravity of the planar polygon as the center of gravity of the cluster target, establishing a center of gravity coordinate system with the center of gravity of the cluster target as the origin, and obtaining the center coordinates of each sub-target in the cluster target in the center of gravity coordinate system.

[0080] During specific implementation, the center coordinates of each sub-target in the ground coordinate system are converted into the barycentric coordinate system, thereby obtaining the center coordinates of each sub-target in the cluster target in the barycentric coordinate system.

[0081] Assume that the center coordinates of each sub-target in the cluster target in the ground coordinate system can be expressed as:

[0082] [(m1,n1),(m2,n2),...,(m i ,n i ),...,(m k ,n k )]

[0083] Then, the center coordinates of each sub-target in the cluster target in the barycentric coordinate system can be expressed as:

[0084] [(M1,N1),(M2,N2),...,(M i ,N i ),...,(M k ,N k )],in,

[0085]

[0086] In step S1, execute:

[0087] Step S12: Determine the optimized region according to the following operations:

[0088] Step S121: Based on the damage level of each sub-target in the cluster target, establish the damage effectiveness function of each weapon on each sub-target to obtain the maximum damage radius of the weapon on the sub-target;

[0089] Specifically, based on the preset damage level for each sub-target, a damage effectiveness function for each weapon against each sub-target is established. Each weapon's damage effectiveness function against each sub-target is set equal to the damage effectiveness failure threshold, and the damage radius of each weapon against each sub-target is obtained. The maximum of all damage radii is selected as the maximum damage radius. For example, the damage effectiveness failure threshold can be set to 0.05 or 0.01; at this point, the probability of damage reaching the corresponding damage level is already very low.

[0090] Step S122: Obtain the maximum distance between the sub-target and the center of gravity of the cluster target;

[0091] Specifically, based on the center coordinates of each sub-target in the cluster target in the barycentric coordinate system, the distance between each sub-target and the origin of the barycentric coordinate system is calculated, and the maximum distance between the sub-target in the cluster target and the origin of the barycentric coordinate system is obtained:

[0092]

[0093] Step S123: taking the sum of the maximum damage radius and the maximum distance as the optimization constraint radius;

[0094] Step S124: The inner area of the circle with the cluster target center as the center and the optimization constraint radius as the radius is used as the optimization area. Figure 3 shown.

[0095] In addition, step S13 is also executed: with the cluster target center of gravity as the circle center and the optimization constraint radius as the radius, the optimization area is evenly divided to obtain sector-shaped intervals with the same central angle and aimed at by each weapon, and the number of the sector-shaped intervals is the same as the number of weapons.

[0096] In step S2, the number of aiming points in a set of aiming points is equal to the number of weapons and also equal to the total number of segments of a chromosome gene; in the specific real-time process, after randomly encoding each segment of the chromosome gene of an individual, the complete chromosome gene of an individual can be obtained; the complete chromosome genes of multiple individuals form an initial population.

[0097] It should also be noted that in this step, there's no need to randomly generate an initial aiming point in advance; instead, the chromosomal genes are directly binary-encoded. In subsequent processing, the corresponding chromosomal gene segments are decoded within each sector to ensure that the coordinates of the aiming point obtained by decoding the corresponding chromosomal gene segments fall within the corresponding sector. This ensures that there is only one aiming point within each sector, preventing the genetic algorithm from reaching a single optimal solution due to a certain aiming point being close to the target, while other aiming points fail to reach the optimal solution.

[0098] The genetic algorithm uses a fitness function to determine the quality of each individual in the population (the closer to the optimal solution, the better). To address the issue of the weapon's damage probability being affected by errors in the weapon's impact point relative to the aiming point, in this embodiment, the Monte Carlo method is incorporated into the genetic algorithm's fitness function. The actual impact point locations of each weapon are simulated based on the errors. Each chromosome gene in the initial population in step S2 and subsequent generations of populations is decoded according to the corresponding constraint range to obtain the coordinates of each aiming point in the center of gravity coordinate system. Then, the distance of each sub-target relative to each actual impact point is calculated and brought into the damage effectiveness function to calculate the comprehensive damage probability of the cluster target. Therefore, in this embodiment, the fitness function in the genetic algorithm is set based on the damage effectiveness function to assess the quality of each individual. Specifically, the execution process of step S3 is described as follows:

[0099] Step S31: Setting corresponding constraint ranges based on the sector-shaped intervals targeted by each weapon, decoding the corresponding chromosome gene segments in the individual, and obtaining the coordinates of each set of aiming points; wherein, the corresponding constraint ranges are set based on the starting angle and ending angle of the central angle of the sector-shaped interval targeted by each weapon, and the radius of the sector-shaped area, to ensure that the coordinates of the aiming points obtained by decoding the corresponding chromosome gene segments fall within the corresponding sector-shaped intervals;

[0100] Specifically, in this embodiment, each chromosome gene segment is used to represent a target point of a polar coordinate system; therefore, each chromosome gene segment includes an angle gene segment and a length gene segment; wherein,

[0101] The angle gene segment is used to represent the angle of the aiming point in the polar coordinate system. The decoding range of the angle gene segment in each chromosome gene segment is different. When decoding the angle gene segment, the starting angle and ending angle of the central angle of the sector-shaped interval that the weapon is aiming at are used as the lower and upper limits of the angle decoding range, respectively. Based on the determined lower and upper limits of the angle decoding range, the current angle gene segment is decoded to ensure that the angle after decoding the angle gene segment falls between the above starting and ending angles.

[0102] The length gene segment is used to represent the length of the aiming point in the polar coordinate system. The decoding range of the length gene segment in each chromosome gene segment is the same, which is 0 to the optimized region radius. During the decoding of the length gene segment, 0 and the optimized region radius are used as the lower limit and upper limit of the length decoding range, respectively. The current length gene segment is decoded based on the determined lower limit and upper limit of the length decoding range to ensure that the angle after decoding the length gene segment falls between 0 and the optimized region radius.

[0103] After obtaining the angle and length of the aiming point, the aiming point is converted to the barycentric coordinate system to obtain the x-axis and y-axis coordinates of the aiming point in the barycentric coordinate system for use in step S32.

[0104] like Figure 4 As shown in Figure 1, there will be target aiming errors and ballistic dispersion errors between the weapon's aiming point and the actual impact point. Therefore, when planning the aiming point, the error factor should be taken into account to obtain the total probability of damage to the cluster target corresponding to the coordinates of each set of impact points. The process is summarized as follows:

[0105] Step S32: Based on each set of aiming point coordinates, obtain multiple sets of actual hit point coordinates corresponding to each set of aiming point coordinates and the total damage probability of the cluster target corresponding to each set of actual hit point coordinates, and determine the fitness value of the corresponding individual based on the total damage probability of the cluster target; specifically,

[0106] Step S321: randomly generate a target aiming error and a ballistic dispersion error each time, and obtain a set of actual impact point coordinates corresponding to the set of aiming points;

[0107] For each aiming point in each set of aiming points, the actual impact point coordinates are obtained by taking into account the target aiming error and the trajectory dispersion error. Specifically, the actual impact point coordinates can be determined according to formula (5):

[0108]

[0109] Among them, x aim_j ,y aim_j They represent the x-axis and y-axis coordinates of the j-th aiming point in the center of gravity coordinate system; j ranges from 1 to J; J represents the total number of weapons; x imp_j ,y imp_j They represent the x-axis and y-axis coordinates of the actual hit point corresponding to the j-th aiming point in the center of gravity coordinate system; S x_j ,S y_j Respectively represent the target aiming error of the j-th aiming point in the x-axis and y-axis directions, σ x_j ,σ y_j They represent the trajectory dispersion errors of the j-th aiming point in the x-axis and y-axis directions respectively; randn() represents taking a random number; in actual application, the range of the random number can be set first;

[0110] Step S322: Based on each set of actual impact point coordinates, determine the corresponding total damage probability of the cluster target;

[0111] Step S3221: Obtain the damage effectiveness function of each actual hit point on each sub-target:

[0112]

[0113] WR r_ij WR d_ijThey represent the damage effectiveness constants of the j-th weapon to the i-th sub-target in the range direction and the deflection direction respectively; for the Carleton damage effectiveness function, WR r_ij WR d_ij Satisfying formulas (7)-(9):

[0114]

[0115] a=MAX(1-0.8cosI,0.3) (8)

[0116] Where a represents the aspect ratio and I represents the corner;

[0117] According to the Carleton damage effectiveness function, the effective killing area of the weapon can be calculated. Therefore, the "damage effectiveness function" of each weapon on the target can be inferred from the effective killing area of the j-th weapon on the i-th sub-target. The effective killing area A of the j-th weapon on the i-th sub-target is L_i for:

[0118]

[0119] According to formulas (7)-(9), WR can be determined r_ij WR d_ij .

[0120] Step S3222: Based on the damage effectiveness function of each actual hit point on each sub-target, obtain the total damage probability of the cluster target corresponding to each group of aiming points;

[0121] Specifically, since the cluster target is composed of different types of sub-targets, each sub-target has a different contribution to the total damage level of the cluster target. A weight ratio should be set for each sub-target. The weight matrix is α=[α1,α2,...,α i ,...,α k ], α i Represents the damage weight of the i-th sub-target, thereby calculating the Hadamard product of the damage probability matrix of each target and the corresponding weight, and obtaining the weighted damage probability of the weapon to each target.

[0122]

[0123] The weighted probability of damage to each target by the weapon is obtained, and the total damage probability PD_total of the cluster target corresponding to the coordinates of each group of hit points is calculated according to the "survival rule".

[0124]

[0125] Step S323: Obtain the average value of the total damage probability of the cluster targets of multiple groups of actual hit point coordinates, and use the average value as the fitness value of the individuals corresponding to the group of aiming points.

[0126] Step S33: Determine whether the individual fitness value reaches the expectation, or whether the maximum number of iterations is reached.

[0127] If so, obtain the individual with the highest fitness value, and use the set of aiming point coordinates decoded by the individual with the highest fitness value as the set of optimal aiming point coordinates to end the iteration; when decoding, set the corresponding constraint range according to the sector interval aimed at by each weapon, decode the corresponding chromosome gene segment in the individual with the highest fitness value, and obtain a set of optimal aiming point coordinates, which will be used as the aiming point planning result.

[0128] Otherwise, generate a new generation population as the current generation population and jump to the step of decoding the current generation population.

[0129] The process of generating a new generation population is as follows: based on the fitness value of the current generation population, a portion of individuals are randomly selected from the current population for crossover and mutation to obtain a new generation population. For details, see steps S331-S334.

[0130] Step S331: Through the selection strategy, the replay random sampling method is used to select high-quality individual chromosomes from the parent population, so that they become parents with a certain probability and obtain a new generation of individual populations. The replay random sampling method determines the probability of an individual being selected by defining the proportion of each individual's fitness value in the population:

[0131]

[0132] Among them, p l represents the probability that the lth individual is selected, f l Represents the fitness value of the lth individual, l = 1, 2, ..., L, L represents the total number of individuals in the population; the larger the fitness value of an individual, the greater the probability of it being selected. This operation is repeated and eventually a better parent generation is selected.

[0133] Step S332: The crossover operator performs a gene exchange on the selected parent generation through a crossover operation, resulting in a new generation of individuals that inherit the characteristics of their parents. The algorithm uses a single-point crossover method to crossover the genes of the parent individuals. A single crossover point, cross_position, and crossover probability, cross_rate, are randomly set in the parent individual code string. At this point, at a certain crossover probability, the genes of the two parent generations are partially exchanged to form a new offspring population.

[0134] Step S333: The mutation operator sets a certain mutation probability for individual genes to prevent the algorithm from quickly reaching a local optimum and thus failing to reach the global optimum. This causes certain genes in the individual strings within the population to change, repairing and supplementing certain genetic genes that may have been lost during the crossover process. This algorithm uses binary mutation based on the encoding scheme, determining whether to mutate all individuals in the population with a certain mutation probability. It then randomly selects mutation bits for the individuals to be mutated, accelerating convergence to the optimal solution and thus forming a new population of individuals.

[0135] Step S334: Substitute the mutated new generation of individual population into step S31 again, perform multiple iterations, and finally calculate the individual with the highest multi-fitness value.

[0136] In addition, in some embodiments, before step S334, a process of calculating the fitness value and judging whether the fitness value reaches the expected value or the number of iterations reaches the maximum value is added; the flowchart is as follows: Figure 2 As shown, that is: based on the new generation individual population, step S31 is executed again, and the fitness value is recalculated to compare whether the fitness value reaches the expected fitness value or whether the number of iterations reaches the maximum value. If so, the best individual corresponding to the maximum fitness value is output as the best aiming point group. If not, the new generation population is mutated to obtain the individual population again to prevent the algorithm from entering the local optimal solution.

[0137] In summary, the aiming point planning method for striking cluster targets provided by this embodiment has the following advantages:

[0138] 1. This method can plan aiming points for different weapons on different sub-targets, and characterize the weapon's damage effectiveness on the target in the form of a damage effectiveness function, making aiming point planning more scalable.

[0139] 2. This method further considers the target position error and trajectory dispersion error, uses the Monte Carlo method to simulate the hit point, and calculates the average target damage probability including these two errors, which is closer to actual combat conditions.

[0140] 3. This method uses a combination of Monte Carlo method and genetic algorithm to plan the target aiming point, without traversing all grids. While ensuring accuracy, it greatly saves computing time and computing resources.

[0141] 4. This method can effectively improve the accuracy of the target aiming point, reduce the optimization time, and can cause the best damage effect with the least ammunition, providing important guarantees for combat firepower planning.

[0142] Example 2

[0143] A specific embodiment 2 of the present invention is used to verify the feasibility and effectiveness of the aiming point planning method for attacking cluster targets in embodiment 1. The description is as follows:

[0144] According to the Monte Carlo method and genetic algorithm based optimization method for planning the aiming points of multiple explosive bombs against cluster targets, the required input parameters are given, as shown in the following table.

[0145] Table 1 Monte Carlo algorithm input parameters

[0146]

[0147] Table 2 Genetic algorithm input parameters

[0148] Parameter name Parameter value Parameter name Parameter value Number of iterations 200 Number of genes 32 Population size 60 Mutation probability 0.2 Crossover probability 0.7

[0149] This example is a case study of optimizing aiming points for a multi-target cluster target, with four weapons expected to be launched in four directions. Substituting the aforementioned input parameters into the cluster target aiming point planning method described in Example 1, the optimal aiming point coordinates are calculated.

[0150] The coordinates of each aiming point in the optimal aiming point group of the cluster target calculated according to the above steps are shown in Table 3.

[0151] Table 3 Coordinates of each aiming point in the optimal aiming point group

[0152] Aim point number aiming point 1 (-15.8,48.8) 2 (23.3,40.4) 3 (-13.4,13.3) 4 (9.6,-45.7)

[0153] The relationship between the number of algorithm iterations and the optimal fitness value and the average fitness value is shown in the figure below: Figure 5 As shown in the figure, it can be seen that when the number of iterations is 166, the maximum value of the damage probability reaches 0.995, and the average value of each iteration tends to be stable, which shows that the algorithm has reached the global optimum. The damage probability contour image is drawn according to the calculated explosion point coordinates, as shown in Figure 6 As shown in Figure 3, the algorithm completes the aiming point planning of multiple weapons against cluster targets.

[0154] Those skilled in the art will appreciate that all or part of the process steps of the above-described embodiments can be implemented by instructing related hardware through a computer program, and the program can be stored in a computer-readable storage medium, such as a magnetic disk, an optical disk, a read-only memory, or a random access memory.

[0155] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by any technician familiar with this technical field within the technical scope disclosed by the present invention should be covered by the scope of protection of the present invention.

Claims

1. A method for aiming point planning for attacking cluster targets, characterized in that: include: Determine the optimization area based on the damage level of each sub-target in the cluster target and the distance from each sub-target to the center of gravity of the cluster target; average Divide the optimized area to obtain the sector-shaped intervals for each weapon to aim at; Randomly encode the chromosome genes of each individual to obtain the initial population; An individual is composed of multiple chromosome genes, each chromosome gene corresponds to a target point coordinate, and each individual corresponds to a set of target point coordinates; For the current generation population, a corresponding constraint range is set based on the sector interval of each weapon's aiming, and the corresponding chromosome gene segment in the individual is decoded to obtain each set of aiming point coordinates; based on each set of aiming point coordinates, multiple sets of actual hit point coordinates corresponding to each set of aiming point coordinates and the total damage probability of the cluster target corresponding to each set of actual hit point coordinates are obtained, and the fitness value of the corresponding individual is determined based on the total damage probability of the cluster target; it is judged whether the individual's fitness value meets the expectation, or whether the maximum number of iterations has been reached. If yes, obtain the individual with the highest fitness value, and use the set of aiming point coordinates decoded by the individual with the highest fitness value as the best set of aiming point coordinates, and end the iteration; Otherwise, generate a new generation population as the current generation population and jump to the step of decoding the current generation population; The method of obtaining, based on each set of aiming point coordinates, multiple sets of actual hit point coordinates corresponding to each set of aiming point coordinates and a total damage probability of cluster targets corresponding to each set of actual hit point coordinates, and determining a fitness value of a corresponding individual based on the total damage probability of the cluster targets includes: Each time the target aiming error and ballistic dispersion error are randomly generated, a set of actual impact point coordinates corresponding to the set of aiming points are obtained; Based on each set of actual impact point coordinates, determine the corresponding total damage probability of the cluster target; Obtain the average value of the total damage probability of cluster targets of multiple groups of actual hit point coordinates, and use the average value as the fitness value of the individuals corresponding to the group of aiming points; The optimized region is determined by performing the following operations: Based on the damage level of each sub-target in the cluster target, the damage effectiveness function of each weapon on each sub-target is established to obtain the maximum damage radius of the weapon on the sub-target; Get the maximum distance from the sub-target to the center of gravity of the cluster target; Taking the sum of the maximum damage radius and the maximum distance as the optimization constraint radius; The inner area of a circle with the cluster target center as the circle center and the optimization constraint radius as the radius is used as the optimization area.

2. The aiming point planning method for attacking cluster targets according to claim 1, characterized in that: Determining the total damage probability of the corresponding cluster targets includes: Based on each set of actual hit point coordinates, obtain the damage effectiveness function of each actual hit point on each sub-target; Based on the damage effectiveness function, the total damage probability PD_total of the cluster target is determined: Among them, PD ij represents the damage effectiveness function of the jth actual hit point in a set of actual hit points on the i-th sub-target, α i represents the damage weight of the i-th sub-target, J represents the total number of weapons, and k represents the total number of sub-targets.

3. The aiming point planning method for attacking cluster targets according to claim 2, characterized in that: The damage effectiveness function is calculated as follows: x imp_j 、y imp_j Respectively represent the x-axis and y-axis coordinates of the actual hit point corresponding to the j-th aiming point in the center of gravity coordinate system with the center of gravity of the cluster target as the origin; WR r_ij WR d_ij They represent the damage effectiveness constants of the j-th weapon on the i-th sub-target in the range direction and the deflection direction respectively; M i 、N i They represent the center coordinates of the i-th sub-target in the barycentric coordinate system.

4. The aiming point planning method for attacking cluster targets according to claim 3, characterized in that: The actual hit point coordinates are calculated as follows: Among them, x aim_j 、y aim_j They represent the x-axis and y-axis coordinates of the j-th aiming point in a set of aiming points in the center of gravity coordinate system; S x_j 、S y_j Respectively represent the target aiming error of the j-th aiming point in the x-axis and y-axis directions, σ x_j ,σ y_j They represent the ballistic dispersion errors of the j-th aiming point in the x-axis and y-axis directions respectively, and randn() represents taking a random number.

5. The aiming point planning method for attacking cluster targets according to claim 3, characterized in that: Combining formulas (4)-(6), we get WR r_ij WR d_ij : a=MAX(1-0.8cosI,0.3) (5) Among them, a represents the aspect ratio, and I represents the corner; A L_ij =π×WR r_ij ×WR d_ij (6) A L_ij It represents the effective killing area of the j-th weapon on the i-th sub-target.

6. The aiming point planning method for attacking cluster targets according to claim 1, characterized in that: The obtaining of the maximum damage radius of the weapon to the sub-target includes: Set the damage effectiveness function of each weapon on each sub-target to 0 to obtain the coordinates of the impact point of each target relative to each weapon; Select the maximum absolute value in the x-axis and y-axis directions from the obtained coordinates of the impact points of each target relative to each weapon; The maximum damage radius is calculated based on the maximum absolute values in the selected x-axis and y-axis directions.

7. The aiming point planning method for attacking cluster targets according to claim 6, characterized in that: The center of gravity of the cluster target is taken as the center of the circle and the optimization constraint radius is taken as the radius, and the optimization area is evenly divided to obtain the sector-shaped intervals with the same center angle and aimed at by each weapon.

8. The aiming point planning method for attacking cluster targets according to claim 7, characterized in that: The corresponding constraint range is set based on the sector interval of each weapon aiming, including: Based on the starting angle, ending angle and radius of the central angle of the sector-shaped interval aimed at by each weapon as constraints, the corresponding constraint range is set to ensure that the coordinates of the aiming point obtained by decoding the corresponding chromosome gene segment fall within the corresponding sector-shaped interval.

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