Target allocation method, system, device and medium in UAV swarm confrontation

Abstract

The invention discloses a target allocation method, system, equipment and medium in a UAV cluster confrontation, comprising the following steps: calculating a combat advantage index parameter of our UAV relative to an enemy UAV, a threat situation index parameter and a target value index parameter of the enemy UAV relative to our UAV from a UAV cluster confrontation model, calculating a cluster comprehensive situation function according to the combat advantage index parameter, the threat situation index parameter and the target value index parameter, constructing a target allocation model according to the cluster comprehensive situation function, solving the target allocation model with the goal of maximizing the cluster comprehensive situation function, and obtaining the target allocation of our UAV to the enemy UAV; maximizing the situation function of our UAV cluster is achieved by an effective target allocation method, that is, obtaining the maximum battlefield advantage, and solving the unbalanced target allocation problem caused by the number of UAV clusters of both sides by an improved Hungarian algorithm.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle (UAV) cluster confrontation, and in particular to a target allocation method, system, equipment and medium in unmanned aerial vehicle (UAV) cluster confrontation. Background Art

[0002] Drone swarm tactics pose a serious threat to the protection of high-value targets such as land positions, sea platforms, and key infrastructure. Autonomous combat drones use a "swarm-to-swarm" approach to counter the "large number" and "low cost" of enemy drone swarms, which is one of the anti-swarm technology routes with great development potential.

[0003] At present, the main strategy for the problem of drone swarm confrontation is to decompose the many-to-many swarm air combat into one-to-one confrontation. However, the designed multi-drone air combat decision-making method is generally only applicable to combat between small-scale drone swarms and static target allocation. When the number of confronting individuals is large and the combat situation is constantly changing, the traditional air combat decision-making method will find it difficult to solve the dynamic confrontation problem between large-scale drone swarms. Summary of the invention

[0004] The present invention aims to at least solve the technical problems existing in the prior art. To this end, the present invention proposes a target allocation method in drone cluster confrontation, which realizes dynamic confrontation between drone clusters by real-time situation assessment of drone clusters in cluster confrontation.

[0005] In a first aspect, the present invention provides a method for target allocation in drone swarm confrontation, comprising the following steps:

[0006] Calculate the combat advantage index parameter of our drone relative to the enemy drone, the threat situation index parameter of the enemy drone relative to our drone, and the target value index parameter from the drone swarm confrontation model;

[0007] Calculate the cluster comprehensive situation function according to the combat advantage index parameter, the threat situation index parameter and the target value index parameter;

[0008] Construct a target allocation model based on the cluster comprehensive situation function;

[0009] The target allocation model is solved with the goal of maximizing the cluster comprehensive situation function to obtain the target allocation of the friendly UAV to the enemy UAV.

[0010] According to the embodiments of the present invention, there are at least the following technical effects:

[0011] This method evaluates the situation of drones by integrating the combat advantage index function, threat situation index function and target value index function of drone cluster confrontation, and maximizes our drone cluster situation function with an effective target allocation method, that is, obtains the maximum battlefield advantage.

[0012] According to some embodiments of the present invention, the step of calculating the combat advantage index parameter of our drone relative to the enemy drone, the threat situation index parameter of the enemy drone relative to our drone, and the target value index parameter from the drone cluster confrontation model includes:

[0013] The combat advantage index parameter of our drone over the enemy drone is calculated by the following formula:

[0014]

[0015]

[0016]

[0017]

[0018]

[0019] Among them, the α ij is the pursuit angle of the i-th friendly UAV to the j-th enemy UAV, and the LOS ij is the sight direction of the i-th friendly UAV to the j-th enemy UAV, and the v ri is the speed direction of the ith UAV of our party, and v bj is the speed direction of the jth enemy drone, the β ij is the escape angle of the i-th friendly UAV to the j-th enemy UAV, and the D a is the maximum range of our drone, and D t is the maximum detection distance of our drone, and D ij is the distance between the ith friendly UAV and the jth enemy UAV, and the E j is the energy value of the jth enemy drone, the E i is the energy value of the ith UAV of our party, is the combat advantage index parameter of the i-th friendly UAV against the j-th enemy UAV, ω1, ω2, ω3 are weight values, is the angle advantage value of the i-th friendly UAV over the k-th enemy UAV, is the attack distance advantage value of the i-th friendly drone to the j-th enemy drone, is the energy advantage of the ith friendly UAV over the jth enemy UAV;

[0020] The threat situation index parameter of the enemy UAV relative to the friendly UAV is calculated by the following formula:

[0021]

[0022]

[0023]

[0024]

[0025] Among them, the is the threat value of the weapon carried by the jth enemy UAV, P1 is the probability of the enemy UAV's weapon successfully hitting the target, P2 is the probability of the enemy UAV being damaged after hitting the target, and n A is the number of times the enemy drone can attack. is the distance threat value of the jth enemy UAV, and the D HVT To attack our drone, the straight-line distance from the high-value target is is the maneuverability threat value of the jth enemy UAV, the T MAX is the maximum thrust of the enemy UAV's engine, w is the weight of the enemy UAV, ξ is a uniform dimension coefficient, is the threat situation index parameter of the j-th enemy UAV to the i-th friendly UAV, and ω4, ω5, ω6 are weight values;

[0026] The target value index parameter of the enemy UAV relative to the friendly UAV is set to a value in [0, 0.5, 1].

[0027] According to some embodiments of the present invention, a comprehensive situation function is calculated according to the combat advantage index parameter, the threat situation index parameter and the target value index parameter by the following calculation formula:

[0028]

[0029] Among them, the c ij is the comprehensive situation function of the i-th friendly UAV to the j-th enemy UAV, is the target value index parameter of the jth enemy UAV to the ith friendly UAV, and k1, k2, and k3 are weight values.

[0030] According to some embodiments of the present invention, the values ​​of weight values ​​ω1, ω2, ω3, weight values ​​ω4, ω5, ω6 and weight values ​​k1, k2, k3 are calculated respectively by using the hierarchical analysis method and the consistency check formula.

[0031] According to some embodiments of the present invention, the target allocation model is solved with the maximization of the cluster comprehensive situation function as the goal to obtain the target allocation of the friendly UAV to the enemy UAV, including:

[0032] Step A101: If the number of our drones is equal to the number of enemy drones, establish a situation assessment matrix [C ij 1] m×n , and use a matrix whose elements are all 1 [C] m×n Subtract the situation assessment matrix [C ij 1] m×n Get the matrix C 11 , m is the number of our drones, and n is the number of enemy drones;

[0033] Step A102: In matrix C 11 Find the minimum element of each row, then subtract the minimum element from the corresponding row to get the matrix C 12 ;

[0034] Step A103: In matrix C 12 Find the minimum element in each column, then subtract the minimum element from the corresponding column to get the matrix C k ;

[0035] Step A104: If the minimum number of horizontal or vertical lines covering all zeros is equal to the matrix C k order, the matrix C k The row and column corresponding to the 0 element in is taken as the optimal allocation solution;

[0036] Step A105: If the minimum number of horizontal or vertical lines covering all zeros is less than the number of lines in matrix C k Order, find the smallest element among the elements not covered by the crossed lines, subtract the smallest element from the elements not covered by the crossed lines, the value of the element covered by one line remains unchanged; add the smallest element among the elements not covered by the crossed lines to the elements covered by two lines, and get the matrix C k+1 , let C k =C k+1 , continue to step A104;

[0037] Step A106: Execute target allocation of our UAV to the enemy UAV according to the optimal allocation plan.

[0038] According to some embodiments of the present invention, the target allocation model is solved with the maximization of the cluster comprehensive situation function as the goal to obtain the target allocation of the friendly UAV to the enemy UAV, including:

[0039] Step B201: If the number of our drones is less than the number of enemy drones, establish a situation assessment matrix [C ij 2] m×n , and add (nm) rows of virtual constants below the matrix to obtain the matrix [C ij 21], and use a matrix whose elements are all 1 [C] n×n Subtract the situation assessment matrix [C ij 21] Get the matrix C 21 , m is the number of our drones, n is the number of enemy drones, and the value of the virtual constant row element is less than 0;

[0040] Step B202: In matrix C 21 Find the minimum element of each row, then subtract the minimum element from the corresponding row to get the matrix C 22 ;

[0041] Step B203, in matrix C 22 Find the minimum element in each column, then subtract the minimum element from the corresponding column to get the matrix C x ;

[0042] Step B204: If the minimum number of horizontal or vertical lines covering all zeros is equal to the matrix C x order, the matrix C x The row and column corresponding to the 0 element in is taken as the optimal allocation solution;

[0043] Step B205: If the minimum number of horizontal or vertical lines covering all zeros is less than the number of lines in matrix C x Order, find the smallest element among the elements not covered by the crossed lines, subtract the smallest element from the elements not covered by the crossed lines, the value of the element covered by one line remains unchanged; add the smallest element among the elements not covered by the crossed lines to the elements covered by two lines, and get the matrix C x+1 , let C x =C x+1 , continue to step B204;

[0044] Step B206: Execute target allocation of our UAV to the enemy UAV according to the optimal allocation plan.

[0045] According to some embodiments of the present invention, the target allocation model is solved with the maximization of the cluster comprehensive situation function as the goal to obtain the target allocation of the friendly UAV to the enemy UAV, including:

[0046] Step C301: If the number of our drones is greater than the number of enemy drones, establish a situation assessment matrix [C ij 3] m×n, and add (mn) columns of virtual constants to the right of the matrix to obtain the matrix [C ij 31], and use a matrix whose elements are all 1 [C] m×m Subtract the situation assessment matrix [C ij 31] Get the matrix C 31 , m is the number of our drones, n is the number of enemy drones, and the value of the virtual constant column element is less than 0;

[0047] Step C302: In the matrix C 31 Find the minimum element of each row, then subtract the minimum element from the corresponding row to get the matrix C 32 ;

[0048] Step C303: In the matrix C 32 Find the minimum element in each column, then subtract the minimum element from the corresponding column to get the matrix C y ;

[0049] Step C304: If the minimum number of horizontal or vertical lines covering all zeros is equal to the matrix C y order, the matrix C y The row and column corresponding to the 0 element in is taken as the optimal allocation solution;

[0050] Step C305: If the minimum number of horizontal or vertical lines covering all zeros is less than the number of lines in matrix C y Order, find the smallest element among the elements not covered by the crossed lines, subtract the smallest element from the elements not covered by the crossed lines, the value of the element covered by one line remains unchanged; add the smallest element among the elements not covered by the crossed lines to the elements covered by two lines, and get the matrix C y+1 , let C y = y+1 , continue to step C304;

[0051] Step C306: Execute target allocation of our UAV to the enemy UAV according to the optimal allocation plan.

[0052] In a second aspect, the present invention provides a target allocation system for drone swarm confrontation, comprising the following modules:

[0053] A data calculation module, used to calculate the combat advantage index parameter of our drone relative to the enemy drone, the threat situation index parameter of the enemy drone relative to our drone, and the target value index parameter from the drone cluster confrontation model;

[0054] A function construction module, used for calculating a cluster comprehensive situation function according to the combat advantage index parameter, the threat situation index parameter and the target value index parameter;

[0055] A model building module is used to build a target allocation model based on the cluster comprehensive situation function;

[0056] The target allocation module is used to solve the target allocation model with the maximization of the cluster comprehensive situation function as the goal, and obtain the target allocation of the friendly UAV to the enemy UAV.

[0057] In the third aspect, the present invention provides a target allocation device in drone swarm confrontation, comprising at least one control processor and a memory for communicating with the at least one control processor; the memory stores instructions executable by the at least one control processor, and the instructions are executed by the at least one control processor so that the at least one control processor can execute the target allocation method in drone swarm confrontation as described above.

[0058] In a fourth aspect, the present invention provides a computer-readable storage medium, characterized in that: the computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to enable a computer to execute the target allocation method in drone cluster confrontation as described above.

[0059] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0061] Figure 1 A schematic diagram of a flow chart of a target allocation method in a UAV swarm confrontation provided by an embodiment of the present invention;

[0062] Figure 2 An air combat situation diagram of a target allocation method in a UAV swarm confrontation provided by another embodiment of the present invention;

[0063] Figure 3 A target allocation relationship matrix diagram of a target allocation method in a drone swarm confrontation provided by another embodiment of the present invention;

[0064] Figure 4 An initial battlefield situation diagram at time t0 of a target allocation method in a UAV swarm confrontation provided by another embodiment of the present invention;

[0065] Figure 5 A visualization diagram of a drone cluster situation assessment matrix at time t0 of a target allocation method in a drone cluster confrontation provided by another embodiment of the present invention;

[0066] Figure 6 A battlefield situation diagram of enemy reinforcement at time t1 of a target allocation method in a UAV swarm confrontation provided by another embodiment of the present invention;

[0067] Figure 7 A visualization diagram of a drone cluster situation assessment matrix at time t1 of a target allocation method in a drone cluster confrontation provided by another embodiment of the present invention;

[0068] Figure 8 A battlefield situation diagram at time t2 of a target allocation method in a UAV swarm confrontation provided by another embodiment of the present invention;

[0069] Fig. 9 A visualization of a drone cluster situation assessment matrix at time t2 of a target allocation method in a drone cluster confrontation provided by another embodiment of the present invention;

[0070] Fig.10 A visualization diagram of the second round of situation assessment matrix of a drone cluster at time t2 of a target allocation method in a drone cluster confrontation provided by another embodiment of the present invention; DETAILED DESCRIPTION

[0071] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0072] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0073] During cluster confrontation, the air combat situation is constantly changing. Each drone must make real-time decisions based on the changing situation to achieve cooperation with friendly aircraft and confrontation with enemy aircraft. This is a strategic choice for both combatants in the face of the battlefield situation, so the decision-making method has a decisive influence on the outcome of the battle. The classic Hungarian algorithm can only solve the one-to-one distribution problem under the condition of the same number of both sides. The actual battlefield is complex. In most cases, the number of drones on both sides is not the same. Even if they are the same at a certain moment, as the air battle progresses, the number of drone clusters on both sides will change due to combat damage, being shot down, or new reinforcement drones. Therefore, it is necessary to study unbalanced target allocation.

[0074] Reference Figure 1 The present invention provides a target allocation method in UAV swarm confrontation, and the method comprises the following steps:

[0075] Step S100, calculating the combat advantage index parameter of our drone relative to the enemy drone, the threat situation index parameter of the enemy drone relative to our drone, and the target value index parameter from the drone cluster confrontation model;

[0076] Step S200, calculating the cluster comprehensive situation function according to the combat advantage index parameter, the threat situation index parameter and the target value index parameter;

[0077] Step S300, constructing a target allocation model according to the cluster comprehensive situation function;

[0078] Step S400: Taking the maximization of the cluster comprehensive situation function as the goal, the target allocation model is solved to obtain the target allocation of our UAV to the enemy UAV.

[0079] Reference Figure 2 In some embodiments, the combat advantage index parameter of our drone relative to the enemy drone, the threat situation index parameter of the enemy drone relative to our drone, and the target value index parameter are calculated from the drone cluster confrontation model in step S100, including:

[0080] The combat advantage index parameters of our drone over the enemy drone are calculated by the following formula:

[0081]

[0082]

[0083]

[0084]

[0085]

[0086] Among them, α ij is the pursuit angle of the i-th friendly UAV to the j-th enemy UAV, LOS ij is the sight direction of the i-th friendly UAV to the j-th enemy UAV, v ri is the speed direction of the ith UAV of our team, v bj is the speed direction of the jth enemy drone, β ij is the escape angle of the i-th friendly UAV to the j-th enemy UAV, D a is the maximum range of our drone, D t is the maximum detection distance of our drone, D ij is the distance between the ith friendly UAV and the jth enemy UAV, E j is the energy value of the jth enemy drone, E i is the energy value of the i-th drone of our side, is the combat advantage index parameter of the i-th friendly UAV against the j-th enemy UAV, ω1, ω2, ω3 are weight values, is the angle advantage of the i-th friendly UAV over the j-th enemy UAV, is the attack distance advantage value of the i-th friendly drone to the j-th enemy drone, is the energy advantage of the ith friendly UAV over the jth enemy UAV;

[0087] The threat situation index parameters of the enemy UAV relative to our UAV are calculated by the following formula:

[0088]

[0089]

[0090]

[0091]

[0092] in, is the threat value of the weapon carried by the jth enemy UAV, P1 is the probability of the enemy UAV's weapon successfully hitting the target, P2 is the probability of the enemy UAV being damaged after hitting, and n A is the number of attacks that the enemy drone can perform. is the distance threat value of the jth enemy drone, D HVT To attack our drone, the straight-line distance from the high-value target is is the maneuverability threat value of the jth enemy UAV, T MAX is the maximum thrust of the enemy UAV’s engine, w is the weight of the enemy UAV, ξ is the uniform dimension coefficient, is the threat situation index parameter of the jth enemy UAV to the i-th friendly UAV, ω4, ω5, ω6 are weight values;

[0093] Set the target value index parameter of the enemy drone relative to our drone to one of the values ​​[0, 0.5, 1].

[0094] In step S200, a comprehensive situation function is calculated based on the combat advantage index parameter, the threat situation index parameter and the target value index parameter by the following calculation formula:

[0095]

[0096] Among them, c ij is the comprehensive situation function of the i-th friendly UAV against the j-th enemy UAV, is the target value index parameter of the j-th enemy UAV to the i-th friendly UAV, and k1, k2, and k3 are weight values.

[0097] In step S100 and step S200, the values ​​of weight values ​​ω1, ω2, ω3, weight values ​​ω4, ω5, ω6 and weight values ​​k1, k2, k3 are calculated respectively by using the hierarchical analysis method and the consistency check formula.

[0098] For example, suppose that at a certain time t on the battlefield, there are the same number n of our drones engaging in air combat with enemy drones, and a one-to-one "attack / target pair" match is performed for the two parties involved. Consider the influence ratio of the three advantage indicators relative to the comprehensive situation value, and make a pairwise comparison, such as the expert evaluation of such battlefield situation combat advantage indicators Threat Situation Index Slightly more important, threat situation indicators Target value index Slightly more important, combat advantage indicator Target value index Obviously important, refer to the following scaling method table:

[0099]

[0100] Table 1

[0101] According to the 1-9 scaling method table, we get the judgment matrix:

[0102]

[0103] Find the maximum eigenvalue λ of the judgment matrix A max =3.04, the corresponding normalized eigenvector is W = (0.64, 0.26, 0.10) T, the values ​​in the eigenvector are the weights of different factors in this problem. The comprehensive situation value of our UAV to the enemy UAV is obtained:

[0104]

[0105] Perform consistency check on the judgment matrix A. Calculate When looking up the table, n=3, the average random consistency index RI standard value is 0.52.

[0106] Comparing the two, we get the test coefficient CR=0.04, CR<0.10, and it is considered that the consistency of the judgment matrix is ​​acceptable.

[0107] In step S400, the target allocation model is solved with the goal of maximizing the cluster comprehensive situation function to obtain the target allocation of our UAV to the enemy UAV, including:

[0108] Step A101: If the number of our UAVs is equal to the number of enemy UAVs, establish a situation assessment matrix [C ij 1] m×n , and use a matrix whose elements are all 1 [C] m×n Subtract the situation assessment matrix [Cij1] m×n Get the matrix C 11 , m is the number of our drones, n is the number of enemy drones;

[0109] Step A102: In matrix C 11 Find the minimum element of each row, then subtract the minimum element from the corresponding row to get the matrix C 12 ;

[0110] Step A103: In matrix C 12 Find the minimum element in each column, then subtract the minimum element from the corresponding column to get the matrix C k ;

[0111] Step A104: If the minimum number of horizontal or vertical lines covering all zeros is equal to the matrix C k Order, the matrix C k The row and column corresponding to the 0 element in is taken as the optimal allocation solution;

[0112] Step A105: If the minimum number of horizontal or vertical lines covering all zeros is less than the number of lines in matrix C k Order, find the smallest element among the elements not covered by the crossed lines, subtract the smallest element from the elements not covered by the crossed lines, the value of the element covered by one line remains unchanged; add the smallest element among the elements not covered by the crossed lines to the elements covered by two lines, and get the matrix C k+1 , let C k =Ck+1 , continue to step A104;

[0113] Step A106: Execute target allocation of our UAV to enemy UAV according to the optimal allocation plan.

[0114] With the goal of maximizing the cluster comprehensive situation function, the target allocation model is solved to obtain the target allocation of our UAV to the enemy UAV, including:

[0115] Step B201: If the number of our drones is less than the number of enemy drones, establish a situation assessment matrix [C ij 2] m×n , and add (nm) rows of virtual constants below the matrix to obtain the matrix [C ij 21], and use a matrix whose elements are all 1 [C] n×n Subtract the situation assessment matrix [C ij 21] Get the matrix C 21 , m is the number of our drones, n is the number of enemy drones, and the value of the virtual constant row element is less than 0;

[0116] Step B202: In matrix C 21 Find the minimum element of each row, then subtract the minimum element from the corresponding row to get the matrix C 22 ;

[0117] Step B203, in matrix C 22 Find the minimum element in each column, then subtract the minimum element from the corresponding column to get the matrix C x ;

[0118] Step B204: If the minimum number of horizontal or vertical lines covering all zeros is equal to the matrix C x Order, the matrix C x The row and column corresponding to the 0 element in is taken as the optimal allocation solution;

[0119] Step B205: If the minimum number of horizontal or vertical lines covering all zeros is less than the number of lines in matrix C x Order, find the smallest element among the elements not covered by the crossed lines, subtract the smallest element from the elements not covered by the crossed lines, the value of the element covered by one line remains unchanged; add the smallest element among the elements not covered by the crossed lines to the elements covered by two lines, and get the matrix C x+1 , let C x =C x+1 , continue to step B204;

[0120] Step B206: Execute target allocation of our UAV to enemy UAV according to the optimal allocation plan.

[0121] With the goal of maximizing the cluster comprehensive situation function, the target allocation model is solved to obtain the target allocation of our UAV to the enemy UAV, including:

[0122] Step C301: If the number of our UAVs is greater than the number of enemy UAVs, establish a situation assessment matrix [C ij 3] m×n , and add (mn) columns of dummy constants to the right of the matrix to obtain the matrix [C ij 31], and use a matrix whose elements are all 1 [C] m×m Subtract the situation assessment matrix [C ij 31] Get the matrix C 31 , m is the number of our drones, n is the number of enemy drones, and the value of the virtual constant column element is less than 0;

[0123] Step C302: In the matrix C 31 Find the minimum element of each row, then subtract the minimum element from the corresponding row to get the matrix C 32 ;

[0124] Step C303: In the matrix C 32 Find the minimum element in each column, then subtract the minimum element from the corresponding column to get the matrix C y ;

[0125] Step C304: If the minimum number of horizontal or vertical lines covering all zeros is equal to the matrix C y Order, the matrix C y The row and column corresponding to the 0 element in is taken as the optimal allocation solution;

[0126] Step C305: If the minimum number of horizontal or vertical lines covering all zeros is less than the number of lines in matrix C y Order, find the smallest element among the elements not covered by the crossed lines, subtract the smallest element from the elements not covered by the crossed lines, the value of the element covered by one line remains unchanged; add the smallest element among the elements not covered by the crossed lines to the elements covered by two lines, and get the matrix C y+1 , let C y =C y+1 , continue to step C304;

[0127] Step C306: Execute the target allocation of our UAV to the enemy UAV according to the optimal allocation plan.

[0128] For example: Refer to Figure 3 , let the target allocation relationship matrix x = [x ij ], x ijrepresents the target allocation scheme of the i-th friendly UAV and the j-th enemy UAV, with a value of 0 or 1. The target allocation scheme of the i-th friendly UAV against the j-th enemy UAV can be expressed as x ij =1, if there is no distribution relationship between the two, then it is 0. The goal is to design an optimal mapping solution from our UAV to the enemy UAV to gain the maximum battlefield advantage.

[0129] The allocation strategy model with the goal of maximizing the total situation value of the cluster is:

[0130]

[0131] When the numbers of both parties are equal, a one-to-one correspondence is satisfied:

[0132]

[0133]

[0134] The above model is a balanced target allocation problem. When the number of drones on both sides is unequal, it is known that m friendly drones are fighting against n enemy drones (m≠n). It is required that each friendly drone must and can only choose one enemy drone as the confrontation target. The following cases are discussed:

[0135] Case 1: m<n, requiring each friendly drone to select a different enemy drone;

[0136] Case 2: m<n, requiring at most k friendly drones to choose the same enemy drone as the confrontation target, and allowing the existence of unassigned enemy drones;

[0137] Case 3: m>n, requiring that each enemy drone be assigned at least 1 and at most k friendly drones;

[0138] Case 4: m>n, requiring at most k friendly drones to choose the same enemy drone as the confrontation target, and allowing the existence of unassigned enemy drones.

[0139] This method evaluates the situation of drones by integrating the combat advantage index function, threat situation index function and target value index function of drone cluster confrontation, and maximizes our drone cluster situation function with an effective target allocation method, that is, obtains the maximum battlefield advantage.

[0140] In order to facilitate the understanding of those skilled in the art, a group of best embodiments are provided below:

[0141] The confrontation system S consists of m friendly drones (represented by r) and n enemy drones (represented by b), which can be expressed as:

[0142] S = {r1, r2, ..., ri , ..., r m , b1, b2, ..., b j , ...b n}

[0143] State 1, at time t0, the number of our drones is equal to the number of enemy drones, that is, m=n.

[0144] Reference Figure 4 At time t0, a cluster of 12 of our drones discovered a cluster of enemy drones, and both clusters were flying in formation.

[0145] Our drone cluster flies northward from the southwest of the battlefield, and the enemy drone cluster flies westward from the northeast of the battlefield. The position coordinates, speed, and heading angle of both sides are set as follows:

[0146]

[0147]

[0148] Table 2

[0149] Since both drone clusters were initially flying in formation, it can be seen that the flight speed of both sides was about 80m / s, the flight altitude was about 1000m, and the same cluster had the same heading. In the absence of more detailed information, it is assumed that the drones have the same performance, that is, their threat status and target value are the same. Therefore, the main basis for constructing the value matrix is ​​the distance advantage function in the advantage status index. Set the weapon attack distance to 200m and the drone detection distance to 5km, and calculate the drone cluster confrontation battlefield situation matrix, as follows: Figure 5 The situation assessment matrix at time t0 is visualized as shown.

[0150] Observe the situation assessment matrix. The larger the situation value, the greater the advantage, and the darker the color in the figure. Since the headings, flight speeds, and platform performance of both sides are similar, the battlefield situation value is mainly based on the relative distance between the drones. The R3 drone at the front of our cluster is closest to the B1 drone at the front of the enemy drone cluster, and its situation value is also the largest; our R11 drone is at the back of the cluster and is farthest from the enemy drone cluster. Compared with other our drones, its situation value relative to each enemy drone is also smaller. The conclusion is consistent with our observations.

[0151] Subtract the situation assessment matrix elements from 1 to get the cost matrix. Solve the cost matrix according to the Hungarian algorithm to get the target allocation matrix. The target allocation relationship of the drone at time t0 is as follows:

[0152]

[0153] Table 3

[0154] After target allocation, our UAVs correspond one-to-one with enemy UAVs, and the obtained battlefield situation value of the enemy cluster is the smallest, that is, the comprehensive situation value is the largest.

[0155] State 2: At time t1, the number of our drones is less than the number of enemy drones, that is, m<n.

[0156] At time t1, two new drones were detected behind the enemy drone cluster. Due to the change in the number of drones on both sides, the battlefield situation has changed. The status information of the newly added drones is as follows:

[0157]

[0158] Table 4

[0159] At this time, the number of our drones m = 12, which is less than the number of enemy drones n = 14. The battlefield situation is as follows Figure 6 As shown, Figure 7 The situation assessment matrix at time t1 is visualized as shown.

[0160] At this time, the target allocation scheme can be divided into two strategies according to tactical requirements:

[0161] The first strategy does not require launching attacks on all enemy drones. Each of our drones still only selects one enemy drone as its opponent, making the overall battlefield situation optimal.

[0162] The second strategy requires launching an attack on all enemy drones, and each friendly drone can select multiple enemy drones as opponents.

[0163] Taking the current state at time t1 as an example, each friendly drone is required to attack at least one enemy drone, and all enemy drones must be attacked. Since drone attacks are affected by weapon attack range and attack frequency, it is hoped that the distance and heading of the assigned targets are close. At this time, an intelligent clustering algorithm can be selected to divide the enemy drone cluster into multiple groups with similar distances and headings, and each friendly drone chooses to attack one group. When calculating the battlefield situation, the status of the same group of enemy drones is averaged.

[0164] State three, at time t2, the number of our drones is greater than the number of enemy drones, that is, m>n.

[0165] At t2, the drone clusters of both sides engage in an air battle. After the confrontation, our side has 10 drones left and the enemy has 6 drones left. The battlefield situation is as follows: Figure 8 As shown, at this time, the target allocation requires our multiple aircraft to coordinate to attack the enemy's single aircraft.

[0166] According to the edge-adding method, (mn) virtual columns are added to the right side of the cost matrix, the cost matrix is ​​converted into a simulation, and then the classic Hungarian algorithm is used to solve it. The modified cost matrix is ​​as follows Fig. 9 shown.

[0167] The distribution scheme obtained by calculation is:

[0168]

[0169] Table 5

[0170] The distribution plan for the first round is obtained: our R2 drone attacks the enemy's B6 drone; our R5 drone attacks the enemy's B5 drone; our R6 drone attacks the enemy's B2 drone; our R7 drone attacks the enemy's B3 drone; our R8 drone attacks the enemy's B1 drone; our R9 drone attacks the enemy's B4 drone.

[0171] The remaining friendly drones r1, r3, r4 and r10 can be assigned to the corresponding six enemy drones according to the improved Hungarian algorithm. The modified cost matrix is ​​as follows Fig.10 shown.

[0172] The final distribution plan is: our R2 drone attacks the enemy's B6 drone; our R1 and R5 drones coordinate to attack the enemy's B5 drone; our R4 and R6 drones coordinate to attack the enemy's B2 drone; our R3 and R7 drones coordinate to attack the enemy's B3 drone; our R8 drone attacks the enemy's B1 drone; our R9 and R10 drones coordinate to attack the enemy's B4 drone.

[0173] Based on the above embodiment, this embodiment also provides a target allocation system in drone cluster confrontation, including the following modules:

[0174] A data calculation module is used to calculate the combat advantage index parameters of our drones relative to the enemy drones, the threat situation index parameters of the enemy drones relative to our drones, and the target value index parameters from the drone cluster confrontation model;

[0175] A function building module, used for calculating a cluster comprehensive situation function according to a combat advantage index parameter, a threat situation index parameter and a target value index parameter;

[0176] A model building module is used to build a target allocation model based on the cluster comprehensive situation function;

[0177] The target allocation module is used to solve the target allocation model with the goal of maximizing the cluster comprehensive situation function, and obtain the target allocation of our UAV to the enemy UAV.

[0178] It should be noted that the system embodiment provided in this implementation and the above-mentioned method embodiment are based on the same inventive concept, so the relevant contents of the above-mentioned method embodiment are also applicable to the system embodiment and will not be repeated here.

[0179] In one embodiment of the present application, a target allocation device for drone cluster confrontation is provided; the device can be any type of smart terminal, such as a mobile phone, a tablet computer, a personal computer, etc. Specifically, the device includes: one or more control processors and a memory, and this example takes a control processor as an example. The control processor and the memory can be connected via a bus or other means, and this example takes the connection via a bus as an example.

[0180] As a non-transitory computer-readable storage medium, the memory can be used to store non-transitory software programs, non-transitory computer executable programs and modules, such as program instructions / modules corresponding to the electronic device in the embodiment of the present invention; the control processor implements the target allocation method in the drone cluster confrontation of the above method embodiment by running the non-transitory software programs, instructions and modules stored in the memory. The memory may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application required by at least one function; in addition, the memory may include a high-speed random access memory, and may also include a non-transitory memory, such as at least one disk storage device, a flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory may optionally include a memory remotely arranged relative to the control processor, and these remote memories may be connected to the electronic device via a network. Examples of the above network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network and a combination thereof. One or more modules are stored in the memory, and when executed by one or more control processors, the target allocation method in the drone cluster confrontation in the above method embodiment is executed.

[0181] An embodiment of the present invention also provides a computer-readable storage medium, which stores computer-executable instructions, and the computer-executable instructions are executed by one or more control processors. For example, the one or more control processors can execute the target allocation method in drone cluster confrontation in the above method embodiment.

[0182] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a general hardware platform. Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and the program can include the process of the embodiment of the above method when executed. Among them, the storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0183] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "illustrative embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0184] Although the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the claims and their equivalents.

Claims

1. A target allocation method in UAV swarm confrontation, characterized in that: The following steps are involved: The combat advantage index parameter of our drone relative to the enemy drone, the threat situation index parameter of the enemy drone relative to our drone, and the target value index parameter are calculated from the drone swarm confrontation model, specifically: The combat advantage index parameter of our drone over the enemy drone is calculated by the following formula: Among them, the α ij is the pursuit angle of the i-th friendly UAV to the j-th enemy UAV, and the LOS ij is the sight direction of the i-th friendly UAV to the j-th enemy UAV, and the v ri is the speed direction of the ith UAV of our party, and v bj is the speed direction of the jth enemy drone, the β ij is the escape angle of the i-th friendly UAV to the j-th enemy UAV, and the D a is the maximum range of our drone, and D t is the maximum detection distance of our drone, and D ij is the distance between the ith friendly UAV and the jth enemy UAV, and the E j is the energy value of the jth enemy drone, the E i is the energy value of the ith UAV of our party, is the combat advantage index parameter of the i-th friendly UAV against the j-th enemy UAV, ω1, ω2, ω3 are weight values, is the angle advantage value of the i-th friendly UAV to the j-th enemy UAV, is the attack distance advantage value of the i-th friendly drone to the j-th enemy drone, is the energy advantage of the ith friendly UAV over the jth enemy UAV; The threat situation index parameter of the enemy UAV relative to the friendly UAV is calculated by the following formula: Among them, the is the threat value of the weapon carried by the jth enemy UAV, P1 is the probability of the enemy UAV's weapon successfully hitting the target, P2 is the probability of the enemy UAV being damaged after hitting the target, and n A is the number of times the enemy drone can attack. is the distance threat value of the jth enemy UAV, and the D HVT To attack our drone, the straight-line distance from the high-value target is is the maneuverability threat value of the jth enemy UAV, the T MAX is the maximum thrust of the enemy UAV's engine, w is the weight of the enemy UAV, ξ is a uniform dimension coefficient, is the threat situation index parameter of the j-th enemy UAV to the i-th friendly UAV, and ω4, ω5, ω6 are weight values; The target value index parameter of the enemy UAV relative to the friendly UAV is set to a value in [0, 0.5, 1]; Calculate the cluster comprehensive situation function according to the combat advantage index parameter, the threat situation index parameter and the target value index parameter; Construct a target allocation model based on the cluster comprehensive situation function; The target allocation model is solved with the goal of maximizing the cluster comprehensive situation function to obtain the target allocation of the friendly UAV to the enemy UAV.

2. The target allocation method in drone swarm confrontation according to claim 1 is characterized in that: The comprehensive situation function is calculated according to the combat advantage index parameter, the threat situation index parameter and the target value index parameter by the following calculation formula: Among them, the c ij is the comprehensive situation function of the i-th friendly UAV to the j-th enemy UAV, is the target value index parameter of the jth enemy UAV to the ith friendly UAV, and k1, k2, k3 are weight values.

3. The target allocation method in drone swarm confrontation according to claim 2 is characterized in that: The values ​​of weights ω1, ω2, ω3, weights ω4, ω5, ω6 and weights k1, k2, k3 are calculated respectively by using the analytic hierarchy process and the consistency test formula.

4. The target allocation method in drone swarm confrontation according to claim 1 is characterized in that: The target allocation model is solved with the maximization of the cluster comprehensive situation function as the goal to obtain the target allocation of the friendly UAV to the enemy UAV, including: Step A101: If the number of our drones is equal to the number of enemy drones, establish a situation assessment matrix [C ij 1] m×n , and use a matrix whose elements are all 1 [C] m×n Subtract the situation assessment matrix [C ij 1] m×n Get the matrix C 11 , m is the number of our drones, and n is the number of enemy drones; Step A102: In matrix C 11 Find the minimum element of each row, then subtract the minimum element from the corresponding row to get the matrix C 12 ; Step A103: In matrix C 12 Find the minimum element in each column, then subtract the minimum element from the corresponding column to get the matrix C k ; Step A104: If the minimum number of horizontal or vertical lines covering all zeros is equal to the matrix C k order, the matrix C k The row and column corresponding to the 0 element in is taken as the optimal allocation solution; Step A105: If the minimum number of horizontal or vertical lines covering all zeros is less than the number of lines in matrix C k Order, find the smallest element among the elements not covered by the crossed lines, subtract the smallest element from the elements not covered by the crossed lines, the value of the element covered by one line remains unchanged; add the smallest element among the elements not covered by the crossed lines to the elements covered by two lines, and get the matrix C k+1 , let C k =C k+1 , continue to step A104; Step A106: Execute target allocation of our UAV to the enemy UAV according to the optimal allocation plan.

5. The target allocation method in drone swarm confrontation according to claim 1 is characterized in that: The target allocation model is solved with the maximization of the cluster comprehensive situation function as the goal to obtain the target allocation of the friendly UAV to the enemy UAV, including: Step B201: If the number of our drones is less than the number of enemy drones, establish a situation assessment matrix [C ij 2] m×n , and add (nm) rows of virtual constants below the matrix to obtain the matrix [C ij 21], and use a matrix whose elements are all 1 [C] n×n Subtract the situation assessment matrix [C ij 21] Get the matrix C 21 , m is the number of our drones, n is the number of enemy drones, and the value of the virtual constant row element is less than 0; Step B202: In matrix C 21 Find the minimum element of each row, then subtract the minimum element from the corresponding row to get the matrix C 22 ; Step B203, in matrix C 22 Find the minimum element in each column, then subtract the minimum element from the corresponding column to get the matrix C x ; Step B204: If the minimum number of horizontal or vertical lines covering all zeros is equal to the matrix C x order, the matrix C x The row and column corresponding to the 0 element in is taken as the optimal allocation solution; Step B205: If the minimum number of horizontal or vertical lines covering all zeros is less than the number of lines in matrix C x Order, find the smallest element among the elements not covered by the crossed lines, subtract the smallest element from the elements not covered by the crossed lines, the value of the element covered by one line remains unchanged; add the smallest element among the elements not covered by the crossed lines to the elements covered by two lines, and get the matrix C x+1 , let C x =C x+1 , continue to step B204; Step B206: Execute target allocation of our UAV to the enemy UAV according to the optimal allocation plan.

6. The target allocation method in drone swarm confrontation according to claim 1 is characterized in that: The target allocation model is solved with the maximization of the cluster comprehensive situation function as the goal to obtain the target allocation of the friendly UAV to the enemy UAV, including: Step C301: If the number of our drones is greater than the number of enemy drones, establish a situation assessment matrix [C ij 3] m×n , and add (mn) columns of virtual constants to the right of the matrix to obtain the matrix [C ij 31], and use a matrix whose elements are all 1 [C] m×m Subtract the situation assessment matrix [C ij 31] Get the matrix C 31 , m is the number of our drones, n is the number of enemy drones, and the value of the virtual constant column element is less than 0; Step C302: In the matrix C 31 Find the minimum element of each row, then subtract the minimum element from the corresponding row to get the matrix C 32 ; Step C303: In the matrix C 32 Find the minimum element in each column, then subtract the minimum element from the corresponding column to get the matrix C y ; Step C304: If the minimum number of horizontal or vertical lines covering all zeros is equal to the matrix C y order, the matrix C y The row and column corresponding to the 0 element in is taken as the optimal allocation solution; Step C305: If the minimum number of horizontal or vertical lines covering all zeros is less than the number of lines in matrix C y Order, find the smallest element among the elements not covered by the crossed lines, subtract the smallest element from the elements not covered by the crossed lines, the value of the element covered by one line remains unchanged; add the smallest element among the elements not covered by the crossed lines to the elements covered by two lines, and get the matrix C y+1 , let C y =C y+1 , continue to step C304; Step C306: Execute target allocation of our UAV to the enemy UAV according to the optimal allocation plan.

7. A target allocation system for drone swarm confrontation, characterized in that: include: The data calculation module is used to calculate the combat advantage index parameters of our drones relative to the enemy drones, the threat situation index parameters of the enemy drones relative to our drones, and the target value index parameters from the drone cluster confrontation model, specifically: The combat advantage index parameter of our drone over the enemy drone is calculated by the following formula: Among them, the α ij is the pursuit angle of the i-th friendly UAV to the j-th enemy UAV, and the LOS ij is the sight direction of the i-th friendly UAV to the j-th enemy UAV, and the v ri is the speed direction of the ith UAV of our party, and v bj is the speed direction of the jth enemy drone, the β ij is the escape angle of the i-th friendly UAV to the j-th enemy UAV, and the D a is the maximum range of our drone, and D t is the maximum detection distance of our drone, and D ij is the distance between the ith friendly UAV and the jth enemy UAV, and the E j is the energy value of the jth enemy drone, the E i is the energy value of the ith UAV of our party, is the combat advantage index parameter of the i-th friendly UAV against the j-th enemy UAV, ω1, ω2, ω3 are weight values, is the angle advantage value of the i-th friendly UAV to the j-th enemy UAV, is the attack distance advantage value of the i-th friendly drone to the j-th enemy drone, is the energy advantage of the ith friendly UAV over the jth enemy UAV; The threat situation index parameter of the enemy UAV relative to the friendly UAV is calculated by the following formula: Among them, the is the threat value of the weapon carried by the jth enemy UAV, P1 is the probability of the enemy UAV's weapon successfully hitting the target, P2 is the probability of the enemy UAV being damaged after hitting the target, and n A is the number of times the enemy drone can attack. is the distance threat value of the jth enemy drone, and the d HVT To attack our drone, the straight-line distance from the high-value target is is the maneuverability threat value of the jth enemy UAV, the T MAX is the maximum thrust of the enemy UAV's engine, w is the weight of the enemy UAV, ξ is a uniform dimension coefficient, is the threat situation index parameter of the j-th enemy UAV to the i-th friendly UAV, and ω4, ω5, ω6 are weight values; The target value index parameter of the enemy UAV relative to the friendly UAV is set to a value in [0, 0.5, 1]; A function construction module, used for calculating a cluster comprehensive situation function according to the combat advantage index parameter, the threat situation index parameter and the target value index parameter; A model building module is used to build a target allocation model based on the cluster comprehensive situation function; The target allocation module is used to solve the target allocation model with the maximization of the cluster comprehensive situation function as the goal, and obtain the target allocation of the friendly UAV to the enemy UAV.

8. A target allocation device for drone swarm confrontation, characterized in that: It includes at least one control processor and a memory for communicating with the at least one control processor; the memory stores instructions that can be executed by the at least one control processor, and the instructions are executed by the at least one control processor so that the at least one control processor can execute the target allocation method in drone cluster confrontation as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to enable a computer to execute the target allocation method in drone swarm confrontation as described in any one of claims 1 to 6.

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