A guidance ammunition attack point distribution method for a dry beach scene

Abstract

The application discloses a guiding ammunition attack point distribution method for a dry beach scene, and the position of a theoretical attack point of ammunition is obtained according to constraint conditions of the guiding ammunition and a damage model of the guiding ammunition; and the attack sequence of the ammunition is obtained according to the position of the theoretical attack point of the ammunition, so that different guiding ammunition has the minimum influence on each other when attacking a track barrier. The guiding ammunition attack point distribution method for the dry beach scene improves the damage probability to typical targets and solves the problem of mutual interference of adjacent launching guiding ammunition when attacking targets.

Description

Technical Field

[0001] This invention relates to a method for allocating attack points of guided munitions in dry beach scenarios, belonging to the field of missile control technology. Background Technology

[0002] On dry beaches, a large number of rail forts are built to impede medium or light landings. The rail fort consists of two parts: a base and piles. The base is generally a reinforced concrete structure, and the piles are generally made of steel rails or I-beams with sharp tips, forming an anti-landing obstacle.

[0003] Rail forts are usually set up in multiple rows. When landing on dry beach, a passage needs to be created among the multiple rows of rail forts set up in the dry beach area, that is, the rail forts in the passage area need to be destroyed.

[0004] The traditional method is to use guided munitions for random fire coverage strikes. However, this method not only wastes ammunition, but also results in a large fluctuation in the damage rate of rail fortifications, making it difficult to guarantee the damage effect.

[0005] Therefore, it is necessary to study a better method for attack point allocation to solve the above problems. Summary of the Invention

[0006] To overcome the above problems, the inventors conducted in-depth research and designed a method for allocating attack points of guided munitions in dry beach scenarios. Based on the constraints and damage models of the guided munitions, the theoretical attack points of the munitions are obtained. Based on the theoretical attack point positions of the munitions, the attack order of the munitions is obtained through a graph search method, so that the impact of different guided munitions on each other when attacking the track fortress is minimized.

[0007] The constraints on the guided munitions include the spacing between adjacent rail forts and the ratio of rail forts to munitions within the dry beach area;

[0008] The damage model of the guided munition is used to describe the degree of damage to the rail fort by a single guided munition.

[0009] In a preferred embodiment, the damage model of the guided munition is:

[0010]

[0011]

[0012] Where, τ v r represents the probability of a guided missile v damaging the rail fortress u. vu This represents the distance between the guided munition v and the track fort u, (x mu y mu ) indicates the position of the rail fortress u, (x bv y bv) represents the landing point of the guided munition v, a represents the radius of the first-level impact area, and a<r≤b represents the radius of the second-level damage area.

[0013] In a preferred embodiment, the theoretical attack point of the munition is obtained by: S11, determining the target trajectory fortress of different guided munitions;

[0014] S12. When the total probability of damage to the target rail fort is maximized by the guided munition, the landing point of the guided munition is taken as the theoretical attack position of the guided munition.

[0015] In a preferred embodiment, when a guided munition targets two rail forts, the maximum total probability of damage to the guided munition is expressed as:

[0016]

[0017] in, Let v represent the probability of damage to rail forts u1 and u2 by guided munitions; when two guided munitions target three rail forts, the maximum total probability of damage by the guided munitions is expressed as:

[0018]

[0019] in, This represents the probability of guided munition v1 damaging the rail fortress u1. This represents the probability of guided munition v1 damaging the rail fortress u2. This indicates the probability of guided munition v1 damaging the rail fortress u3; This represents the probability of guided munition v2 damaging the rail fortress u1. This represents the probability of guided munition v2 damaging the rail fortress u2. This represents the probability of guided munition v2 damaging the rail fortress u3.

[0020] In a preferred embodiment, by setting a dispersion error model for the ammunition's attack point location, the theoretical attack point location of the ammunition is transformed into the ideal attack point location of the ammunition.

[0021] The ammunition attack point location dispersion error model is used to simulate the degree to which ballistic ammunition deviates from the target upon impact.

[0022] In a preferred embodiment, the ammunition attack point location dispersion error model is expressed as:

[0023] x i ~(0, k_sigma / crror_aim)

[0024] Where k_sigma is the deviation probability coefficient, error_aim is the landing point deviation, and xi Let represent the error amount of the i-th attack, which follows a Gaussian distribution, and the subscript i indicates the i-th munition attack.

[0025] The ideal attack point position X of the ammunition e It can be obtained through the following steps:

[0026] S21. Based on the ammunition attack point location dispersion error model, obtain the i-th deviation landing point X. i ;

[0027] X i =X t +x i

[0028] Among them, X t This refers to the theoretical attack point location for the ammunition;

[0029] S22, Based on the multiple deviation points X i Obtain the ideal attack point location X for the ammunition. e :

[0030]

[0031] or

[0032] Where N represents the total number of deviation landing points, and the specific value is set based on experience.

[0033] In a preferred embodiment, the order of ammunition attacks is obtained by using the longest distance between adjacent ideal attack points as a constraint.

[0034] In a preferred embodiment, the attack sequence is obtained through the following steps:

[0035] S31. Construct a connected graph with all attack points in the attack area as vertices, and use the distance between vertices as edge weights to obtain all edge weights in the connected graph.

[0036] S32. Taking the middle vertex i of the connected graph as the starting point, search for the attack point j with the maximum edge weight formed with the starting point, take the attack point j as the new starting point and reset the maximum edge weight to 0.

[0037] S33. Repeat S32 to search for the next attack point until all edge weights are 0. Sort the searched attack points in order of priority. This order is the attack order.

[0038] The beneficial effects of this invention include:

[0039] (1) It solves the problem of target firepower allocation in specific scenarios and greatly improves the probability of destroying typical targets;

[0040] (2) By designing the attack sequence of guided munitions, the problem of mutual interference between adjacent guided munitions when attacking targets was solved;

[0041] (3) In the attack point allocation method of the present invention, a single guided munition can destroy multiple rail forts, which greatly saves the number of guided munitions. Attached Figure Description

[0042] Figure 1 A schematic flowchart of a method for allocating attack points of guided munitions in a dry beach scenario according to a preferred embodiment of the present invention is shown.

[0043] Figure 2 A schematic diagram of a damage model according to a preferred embodiment of the present invention is shown;

[0044] Figure 3 This diagram illustrates the attack sequence acquisition process.

[0045] Figure 4 This diagram illustrates the final attack sequence obtained in Example 1.

[0046] Figure 5 The simulation results of Example 1 are shown. Detailed Implementation

[0047] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Through these descriptions, the features and advantages of the present invention will become clearer and more apparent.

[0048] The term “exemplary” as used herein means “serving as an example, embodiment, or illustration.” Any embodiment illustrated herein as “exemplary” is not necessarily to be construed as superior to or better than other embodiments. Although various aspects of embodiments are shown in the accompanying drawings, the drawings are not necessarily drawn to scale unless specifically indicated otherwise.

[0049] According to the present invention, a method for allocating the attack points of guided munitions in a dry beach scenario is provided. Based on the constraints and damage model of the guided munitions, the theoretical impact point position of the munitions is obtained. Based on the theoretical attack point position of the munitions, the attack sequence of the munitions is obtained, so that the different guided munitions have the least impact on each other when attacking the track fort.

[0050] The damage model of the guided munition is used to describe the degree of damage to the rail fort by a single guided munition.

[0051] Destroying the rail fort according to the theoretical impact point of the munitions and the order of attack obtained by the munitions can not only avoid interference with adjacent guided munitions during the destruction of the rail fort, but also achieve the best destruction effect.

[0052] Specifically, the constraints on the guided munitions include the spacing between adjacent rail forts in the dry beach area, the size of the rail fort base, and the ratio of rail forts to munitions.

[0053] Preferably, the ratio of the number of rail fortifications to the number of guided munitions is less than 1:2.

[0054] Generally, rail forts are arranged in rows and columns. In this invention, the row direction of the rail forts is denoted as the z-axis direction, and the distance between adjacent rail forts in the z-axis direction is denoted as p; the column direction of the rail forts is denoted as the x-axis direction, and the distance between adjacent rail forts in the x-axis direction is denoted as q.

[0055] The damage model of the guided munition is used to describe the degree of damage to the rail fort by a single guided munition.

[0056] Furthermore, after a guided munition attack, the damage to the center of the attack is the most severe, gradually decreasing towards the outer perimeter. Therefore, the damage from guided munitions is divided into two levels: Level 1 is the impact area, where the rail fortifications are considered completely destroyed; Level 2 is partial damage, such as... Figure 2 As shown.

[0057] Preferably, the damage model of the guided munition is as follows:

[0058]

[0059]

[0060] Where, τ vu r represents the probability of a guided missile v damaging the rail fortress u. vu This represents the distance between the guided munition v and the track fort u, (x mu y mu ) indicates the position of the rail fortress u, (x bv y bv ) represents the landing point of the guided munition v, a represents the radius of the first-level impact area, and a<r≤b represents the radius of the second-level damage area.

[0061] Theoretical attack points for ammunition are obtained through the following methods:

[0062] S11. Determine the target trajectory fortress for different guided munitions;

[0063] According to the present invention, one guided munition attacks two rail forts, or two guided munitions attack three rail forts.

[0064] In a preferred embodiment, when the number of single-row rail forts is even, the ratio of guided munitions to rail forts is 1 / 2, that is, one munition attacks two adjacent rail forts.

[0065] When there are 3 single-row rail forts, they are attacked by two rounds of ammunition;

[0066] When the number of single-row rail forts is odd and greater than 3, the three middle rail forts are attacked by two missiles, and the remaining rail forts are attacked by one munition for every two rail forts.

[0067] S12. When the total probability of damage to the target rail fort is maximized by the guided munition, the landing point of the guided munition is taken as the theoretical attack position of the guided munition.

[0068] In a preferred embodiment, when a guided munition targets two rail forts, the maximum total probability of damage to the guided munition is expressed as:

[0069]

[0070] in, Let v represent the probability of damage to rail forts u1 and u2 by guided munitions; when two guided munitions target three rail forts, the maximum total probability of damage by the guided munitions is expressed as:

[0071]

[0072] in, This represents the probability of guided munition v1 damaging the rail fortress u1. This represents the probability of guided munition v1 damaging the rail fortress u2. This indicates the probability of guided munition v1 damaging the rail fortress u3; This represents the probability of guided munition v2 damaging the rail fortress u1. This represents the probability of guided munition v2 damaging the rail fortress u2. This represents the probability of guided munition v2 damaging the rail fortress u3.

[0073] The inventors discovered that the attack sequence of munitions determined directly based on the theoretical attack point position of the munitions does not ultimately achieve the best attack effect. This is because guided munitions may deviate from their landing point during the attack.

[0074] In a preferred embodiment of the present invention, a munition attack point position dispersion error model is set to transform the theoretical munition attack point position into the ideal munition attack point position, and then the munition attack sequence is obtained based on the ideal munition attack point position.

[0075] The ammunition attack point location dispersion error model is used to simulate the degree to which ballistic ammunition deviates from the target upon impact.

[0076] Furthermore, the scattering error model of the ammunition attack point location follows a Gaussian distribution, expressed as:

[0077] xi ~(0,k_sigma / error_aim) (4)

[0078] Where k_sigma is the deviation probability coefficient, error_aim is the landing point deviation, and x i Let represent the error amount of the i-th time, which follows a Gaussian distribution, and the subscript i indicates the i-th munition attack; according to the present invention, the deviation probability coefficient and the impact point deviation in the munition attack point location dispersion error model can be obtained by statistically analyzing the munition point dispersion results after the munition attack.

[0079] The ideal attack point position X of the ammunition e Obtained through the following steps:

[0080] S21. Based on the ammunition attack point location dispersion error model, obtain the i-th deviation landing point X. i ;

[0081] X i =X t +x i

[0082] Among them, X t This refers to the theoretical attack point location for the ammunition;

[0083] S22, Based on the multiple deviation points X i Obtain the ideal attack point location X for the ammunition. e :

[0084]

[0085] or

[0086] Where N represents the total number of deviation points, and the specific value is set based on experience. and To pass through the deviation landing point X i The rail forts u1, u2, and u3 are obtained through formula (1).

[0087] According to a preferred embodiment of the present invention, the attack order of ammunition on different attack points is obtained by using the longest distance between adjacent ideal attack points as a constraint.

[0088] More preferably, the attack order is obtained through the following steps, such as Figure 3 As shown:

[0089] S31. Construct a connected graph with all attack points in the attack area as vertices, and use the distance between vertices as edge weights to obtain all edge weights in the connected graph.

[0090] S32. Taking the middle vertex i of the connected graph as the starting point, search for the attack point j with the maximum edge weight formed with the starting point, take the attack point j as the new starting point and reset the maximum edge weight to 0.

[0091] S33. Repeat S32 to search for the next attack point until all edge weights are 0. Sort the searched attack points in order of priority. This order is the attack order.

[0092] The connected graph G is an abstract network composed of vertices, in which each vertex is connected to the other by an edge. It can be represented as G = {v(i,j), w(i,j)}, where v(i,j) is the set of vertices and w(i,j) is the edge weight, representing the distance between vertex i and vertex j.

[0093] In a preferred embodiment, in S1, the attack point at the center of the connected graph is taken as the vertex. The inventors discovered that if the attack point at the center is not taken as the vertex, two adjacent ammunition landing points may be close together in subsequent attacks, potentially causing mutual interference.

[0094] In S2, by searching for the maximum edge weight, the distance between adjacent attack points is maximized, thereby reducing the impact of the previous attack on the next attack.

[0095] Furthermore, by resetting the maximum edge weight to 0, attacked vertices are no longer included in the subsequent attack point sorting, thus avoiding repeated attacks.

[0096] Example

[0097] Example 1

[0098] For the allocation of attack points for guided munitions in a dry beach scenario, three rows of rail forts are set up on the dry beach, with the spacing parameters between adjacent rail forts being p=4 and q=8, for a total of 22 rail forts; the number of guided munitions is 12.

[0099] The damage model for guided munitions needs to be changed synchronously.

[0100]

[0101]

[0102] The values ​​of parameters a and b are 1m and 3.5m, respectively.

[0103] Theoretical attack points for ammunition are obtained through the following methods:

[0104] S11. Determine the target trajectory fortress for different guided munitions;

[0105] S12. When the total probability of damage to the target rail fort is maximized by the guided munition, the landing point of the guided munition is taken as the theoretical attack position of the guided munition.

[0106] The scattering error model of the ammunition attack point location follows a Gaussian distribution, expressed as:

[0107] x ~ (0, k_sigma / crror_aim)

[0108] Specifically, for this type of guided munition, the dispersion of the impact points after the attack was statistically analyzed. Based on the actual impact point location, the deviation probability coefficient k_sigma was 3.0 and the impact point deviation erroraim was 2.0.

[0109] Ideal attack point location for ammunition X e Obtained through the following steps:

[0110] S21. Based on the ammunition attack point location dispersion error model, obtain the i-th deviation landing point X. i ;

[0111] X i =X t +x i

[0112] Among them, X t This refers to the theoretical attack point location for the ammunition;

[0113] S22, Based on the multiple deviation points X i Obtain the ideal attack point location X for the ammunition. e :

[0114]

[0115] or

[0116] Where N is 500, and To pass through the deviation landing point X i The rail forts u1, u2, and u3 are obtained through formula (1).

[0117] The attack sequence is obtained based on the ideal attack point location of the ammunition through the following steps:

[0118] S31. Construct a connected graph with all attack points in the attack area as vertices, and use the distance between vertices as edge weights to obtain all edge weights in the connected graph.

[0119] S32. Taking the middle vertex i of the connected graph as the starting point, search for the attack point j with the maximum edge weight formed with the starting point, take the attack point j as the new starting point and reset the maximum edge weight to 0.

[0120] S33. Repeat S32 to search for the next attack point until all edge weights are 0. Sort the searched attack points in order of priority. This order is the attack order.

[0121] The final attack order is as follows Figure 4 As shown.

[0122] The attack sequence was simulated to observe the damage effect.

[0123] Because the actual landing point may deviate from the ideal landing point, 500 simulations were performed. The final simulation results are as follows. Figure 5 As shown.

[0124] The results show that the probability of damage to different rail fortifications is greater than 90%, which means that rail fortifications in dry beach scenarios can be effectively cleared.

[0125] In the description of this invention, it should be noted that the terms "upper," "lower," "inner," "outer," "front," and "rear," etc., indicate the orientation or positional relationship based on the orientation or positional relationship in the working state of this invention, and are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this invention. Furthermore, the terms "first," "second," "third," and "fourth" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0126] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal communication between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0127] The present invention has been described above with reference to preferred embodiments; however, these embodiments are merely exemplary and illustrative. Various substitutions and modifications can be made to the present invention based on these embodiments, all of which fall within the scope of protection of the present invention.

Claims

1. A method for allocating attack points of guided munitions in dry beach scenarios, characterized in that, Based on the constraints and damage model of guided munitions, the theoretical attack point of the munitions is obtained; based on the theoretical attack point of the munitions, the attack order of the munitions is obtained through graph search method so that the different guided munitions have the least impact on each other when attacking the fortification. The constraints on the guided munitions include the spacing between adjacent rail forts and the ratio of rail forts to munitions within the dry beach area; The damage model of the guided munition is used to describe the degree of damage to the rail fort by a single guided munition. The damage model of the guided munition is as follows: in, Indicates guided munitions Duiguitiao Village The probability of damage, Indicates guided munitions Yuguitiaozhai The distance between them Indicates rail fortification Location Indicates guided munitions The landing point, This represents the radius of the first-level hit area. Indicates the radius of the secondary damage area; The theoretical attack point of the munition is obtained through the following method: S11, determining the target trajectory fortress of different guided munitions; S12. When the total probability of damage to the target rail fort is maximized by the guided munition, the landing point of the guided munition is taken as the theoretical attack position of the guided munition. When a guided munition targets two rail forts, its maximum total probability of destruction is expressed as: in, , Indicates guided munitions Duiguitiao Village and The probability of damage; when two guided munitions correspond to three rail forts as targets, the maximum total probability of damage of the guided munitions is expressed as: in, Indicates guided munitions Duiguitiao Village The probability of damage, Indicates guided munitions Duiguitiao Village The probability of damage, Indicates guided munitions Duiguitiao Village The probability of damage; Indicates guided munitions Duiguitiao Village The probability of damage, Indicates guided munitions Duiguitiao Village The probability of damage, Indicates guided munitions Duiguitiao Village The probability of damage; By setting up a dispersion error model for the ammunition's attack point location, the theoretical attack point location of the ammunition is transformed into the ideal attack point location of the ammunition. The ammunition attack point location dispersion error model is used to simulate the degree to which ballistic ammunition deviates from the target upon impact.

2. The method for allocating guided munition attack points in dry beach scenarios according to claim 1, characterized in that, The scattering error model for the ammunition attack point location is expressed as follows: in, k_sigma The deviation probability coefficient error_aim For landing point deviation, Indicates the first The error magnitude follows a Gaussian distribution, and the subscript... Indicates the first Secondary munition attack; The ideal attack point location of the ammunition It can be obtained through the following steps: S21. Based on the ammunition attack point location dispersion error model, obtain the first... Secondary deviation landing point ; in, This refers to the theoretical attack point location for the ammunition; S22, Based on multiple deviation points Obtain the ideal attack point location for ammunition. : or in, N This indicates the total number of points where the deviation falls.

3. The method for allocating guided munition attack points in dry beach scenarios according to claim 1, characterized in that, The order of ammunition attacks is obtained by using the longest distance between adjacent ideal attack points as a constraint.

4. The method for allocating guided munition attack points in dry beach scenarios according to claim 3, characterized in that, The attack sequence is obtained through the following steps: S31. Construct a connected graph with all attack points in the attack area as vertices, and use the distance between vertices as edge weights to obtain all edge weights in the connected graph. S32, using the middle vertices of a connected graph i Starting from this point, search for the attack point with the maximum edge weight formed by the starting point. j , attack point j This will serve as a new starting point, and the maximum edge weight will be reset to 0. S33. Repeat S32 to search for the next attack point until all edge weights are 0. Sort the searched attack points in order of priority. This order is the attack order.

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