阅读说明：本技术 一种多弹协同系统的模糊关联融合方法 (Fuzzy association fusion method of multi-missile cooperative system ) 是由 陈凯 周钧 梁文超 赵子祥 闫斌斌 于 2021-05-27 设计创作，主要内容包括：本发明公开了一种多弹协同系统的模糊关联融合方法,其包括进行滤波和时空同步得到多弹探测系统内各个导弹的局部估计；根据各个从弹的局部估计构建与任意两个导弹对应的模糊因子集；采用高斯隶属度函数对每个模糊因子集进行计算,得到对应的相似度值；基于相似度值通过模糊综合函数获取任意两个导弹的局部估计的综合模糊相似度；将所有综合模糊相似度构建为综合模糊相似度矩阵；对综合模糊相似度矩阵进行关联判决,得到该多弹探测系统的融合估计信息；利用融合估计信息纠正从弹的探测信息错误,并将融合估计信息作为从弹制导信息的输入。本发明使多弹协同系统中的导弹不易受到欺骗干扰,能够对目标探测出现的偏差进行修正。(The invention discloses a fuzzy association fusion method of a multi-missile cooperative system, which comprises the steps of carrying out filtering and space-time synchronization to obtain local estimation of each missile in a multi-missile detection system; constructing a fuzzy factor set corresponding to any two missiles according to the local estimation of each slave missile; calculating each fuzzy factor set by adopting a Gaussian membership function to obtain a corresponding similarity value; acquiring the locally estimated comprehensive fuzzy similarity of any two missiles through a fuzzy comprehensive function based on the similarity value; constructing all the comprehensive fuzzy similarity into a comprehensive fuzzy similarity matrix; performing association judgment on the comprehensive fuzzy similarity matrix to obtain fusion estimation information of the multi-missile detection system; and correcting the detection information error of the slave bomb by using the fusion estimation information, and taking the fusion estimation information as the input of the slave bomb guidance information. The invention ensures that the missiles in the multi-missile cooperative system are not easy to be subjected to deception interference and can correct the deviation of target detection.)

1. A fuzzy association fusion method of a multi-missile cooperative system is characterized by comprising the following steps:

s1, carrying out filtering and space-time synchronization to obtain local estimation of each missile in the multi-missile detection system;

s2, receiving local estimation of each slave missile through the missile, and constructing a fuzzy factor set corresponding to any two missiles according to the local estimation of each slave missile; the guided missile is a guided missile, and the slave missile is a guided missile belonging to the guided missile;

s3, calculating each fuzzy factor set by adopting a Gaussian membership function to obtain a corresponding similarity value;

s4, acquiring the locally estimated comprehensive fuzzy similarity of any two missiles through a fuzzy comprehensive function based on the similarity value;

s5, constructing all the comprehensive fuzzy similarity into a comprehensive fuzzy similarity matrix;

s6, performing relevance judgment on the comprehensive fuzzy similarity matrix, eliminating local estimation of unassociated missiles, and fusing the local estimation of the associated missiles to obtain fused estimation information of the multi-missile detection system;

s7, correcting the error of the detection information of the slave bomb by using the fusion estimation information, and using the fusion estimation information as the input of the slave bomb guidance information.

2. The fuzzy association fusion method of the multi-shot cooperative system according to claim 1, wherein the specific method of step S1 is as follows:

according to the formula:

obtaining local estimates of missile j at time kWherein:

is a predicted value of the one-step state of the missile j at the moment k, phi_jIs the state transition matrix for missile j at time k,is a local estimation value of the missile j at the k-1 moment;

P_j(k | k-1) is a one-step covariance estimate of missile j at time k, P_j(k-1| k-1) is the covariance estimate of missile j at time k-1,is the transpose of the state transition matrix, Q, of the missile j at time k_jExciting a noise covariance matrix for the process;

K_j(k) as Kalman gain, H_j(K) Is an observation matrix of missile j at time k, H_j(K)^TThe matrix is a transpose matrix of an observation matrix of the missile j at the moment k, and R is a measured acoustic covariance matrix;

z (k) is the observation information of the missile j at the moment k;

P_jand (k | k) is the covariance estimation value of the missile j at the moment k, and I is an identity matrix.

3. The fuzzy association fusion method of the multi-shot cooperative system according to claim 1, wherein the specific method of step S2 is as follows:

respectively taking state estimation values of missile i and missile j in the previous two periods of current time k and k as input quantitiesAnd input quantityAnd according to the formula:

obtaining a fuzzy factor set mu corresponding to the missile i and the missile j_ij(l | l), wherein:

C_ij(l | l) is an intermediate parameter, P_i(l | l) is the covariance estimate of missile i at the current time k and two periods prior to k, P_j(l | l) is the covariance estimation value of the missile j of the current time k and the previous two periods of k; when l is equal to k-2,is a local estimate of missile i at time k-2.

4. The fuzzy association fusion method of the multi-shot cooperative system according to claim 3, wherein the specific method of step S3 is as follows:

according to the formula:

d_ij(l|l)＝exp{-μ_ij(l|l)^Tμ_ij(l|l)}

obtaining the corresponding similarity value d of missile i and missile j_ij(l | l); wherein mu_ij(l|l)^TFor the set of fuzzy factors mu corresponding to missile i and missile j_ijThe transposed matrix of (l | l).

5. The fuzzy association fusion method of the multi-shot cooperative system according to claim 4, wherein the specific method of step S4 is as follows:

according to the formula:

obtaining the comprehensive fuzzy similarity s_ij。

6. The fuzzy association fusion method of the multi-shot cooperative system according to claim 5, wherein the specific method of step S5 is as follows:

according to the formula:

obtaining a comprehensive fuzzy similarity matrix S_ijWherein i is the number of columns, j is the number of rows, and n is the number of missiles in the multi-missile detection system.

7. The fuzzy association fusion method of the multi-shot cooperative system according to claim 6, wherein the specific method of step S6 is:

s6-1, maximum value element S for each column from the second column of the integrated fuzzy similarity matrix_i＝maxS_ijCarrying out missile correlation judgment, if s_ijIf the value is more than epsilon, judging that the missile pair (i, j) is associated at the moment, and performing step S6-2; otherwise, judging the missile is not associated, and rejecting the local estimation of the unassociated missile; wherein epsilon is a preset decision threshold;

s6-2, according to the formula:

fusing the local estimation of the related missile to obtain the fusion estimation information of the multi-missile detection systemWhere P (l | l) is the covariance estimate of missile i and missile j for the current time k and the first two cycles of k.

Technical Field

The invention relates to the field of multi-missile cooperative attack, in particular to a fuzzy association fusion method of a multi-missile cooperative system.

Background

The future air combat is based on advanced information technology, the cross-domain, cross-army, distributed and networked cooperative combat capability is established, the multi-platform cooperative combat is taken as a main combat mode, the targets are detected by using sensors distributed on multiple platforms in a battlefield space, and the state information of the targets is provided for the missiles, so that the networked cooperative combat is realized. The detection information of a plurality of missiles is comprehensively used, the target guidance information (position and speed) can be more efficiently estimated, and when the multi-missile system is interfered and has errors, the system can still complete the preset task by using the redundant information of the system.

The missile in the multi-missile cooperative system is influenced by deception interference, the situation that the detection deviation of the target cannot be corrected occurs, and the detection precision of the whole system is influenced, so that the tracking information of a plurality of missiles needs to be correlated.

Disclosure of Invention

Aiming at the defects in the prior art, the fuzzy association fusion method of the multi-missile cooperative system provided by the invention solves the problem that the deviation of the target detection cannot be corrected.

In order to achieve the purpose of the invention, the invention adopts the technical scheme that:

the fuzzy association fusion method for the multi-missile cooperative system comprises the following steps:

s1, carrying out filtering and space-time synchronization to obtain local estimation of each missile in the multi-missile detection system;

s2, receiving local estimation of each slave missile through the missile, and constructing a fuzzy factor set corresponding to any two missiles according to the local estimation of each slave missile; the guided missile is a guided missile, and the slave missile is a guided missile belonging to the guided missile;

s3, calculating each fuzzy factor set by adopting a Gaussian membership function to obtain a corresponding similarity value;

s4, acquiring the locally estimated comprehensive fuzzy similarity of any two missiles through a fuzzy comprehensive function based on the similarity value;

s5, constructing all the comprehensive fuzzy similarity into a comprehensive fuzzy similarity matrix;

s6, performing relevance judgment on the comprehensive fuzzy similarity matrix, eliminating local estimation of unassociated missiles, and fusing the local estimation of the associated missiles to obtain fused estimation information of the multi-missile detection system;

s7, correcting the error of the detection information of the slave bomb by using the fusion estimation information, and using the fusion estimation information as the input of the slave bomb guidance information.

Further, the specific method of step S1 is:

according to the formula:

obtaining local estimates of missile j at time kWherein:

is a predicted value of the one-step state of the missile j at the moment k, phi_jIs the state transition matrix for missile j at time k,is a local estimation value of the missile j at the k-1 moment;

P_j(k | k-1) is a one-step covariance estimate of missile j at time k, P_j(k-1| k-1) is the covariance estimate of missile j at time k-1,is the transpose of the state transition matrix, Q, of the missile j at time k_jExciting a noise covariance matrix for the process;

K_j(k) as Kalman gain, H_j(K) Is an observation matrix of missile j at time k, H_j(K)^TIs kA transpose matrix of an observation matrix of the missile j at the moment, wherein R is a measured acoustic covariance matrix;

z (k) is the observation information of the missile j at the moment k;

P_jand (k | k) is the covariance estimation value of the missile j at the moment k, and I is an identity matrix.

Further, the specific method of step S2 is:

respectively taking state estimation values of missile i and missile j in the previous two periods of current time k and k as input quantitiesAnd input quantityAnd according to the formula:

obtaining a fuzzy factor set mu corresponding to the missile i and the missile j_ij(l | l), wherein:

C_ij(l | l) is an intermediate parameter, P_i(l | l) is the covariance estimate of missile i at the current time k and two periods prior to k, P_j(l | l) is the covariance estimation value of the missile j of the current time k and the previous two periods of k; when l is equal to k-2,is a local estimate of missile i at time k-2.

Further, the specific method of step S3 is:

according to the formula:

d_ij(l|l)＝exp{-μ_ij(l|l)^Tμ_ij(l|l)}

obtaining the corresponding similarity value d of missile i and missile j_ij(l | l); wherein mu_ij(l|l)^TFor the set of fuzzy factors mu corresponding to missile i and missile j_ijThe transposed matrix of (l | l).

Further, the specific method of step S4 is:

according to the formula:

obtaining the comprehensive fuzzy similarity s_ij。

Further, the specific method of step S5 is:

according to the formula:

obtaining a comprehensive fuzzy similarity matrix S_ijWherein i is the number of columns, j is the number of rows, and n is the number of missiles in the multi-missile detection system.

Further, the specific method of step S6 is:

s6-1, maximum value element S for each column from the second column of the integrated fuzzy similarity matrix_i＝max S_ijCarrying out missile correlation judgment, if s_ijIf the value is more than epsilon, judging that the missile pair (i, j) is associated at the moment, and performing step S6-2; otherwise, judging the missile is not associated, and rejecting the local estimation of the unassociated missile; wherein epsilon is a preset decision threshold;

s6-2, according to the formula:

fusing the local estimation of the related missile to obtain the fusion estimation information of the multi-missile detection systemWhere P (l | l) is the covariance estimate of missile i and missile j for the current time k and the first two cycles of k.

The invention has the beneficial effects that: the missile in the multi-missile cooperative system is not easy to be subjected to deception interference, and the deviation of target detection can be corrected.

Drawings

FIG. 1 is a general flow chart of the present invention;

FIG. 2 is a three-dimensional diagram of a multi-shot tracking trajectory of the present invention;

FIG. 3 is a two-dimensional plot of a multi-shot tracking trajectory of the present invention;

FIG. 4 is a simulation diagram of the position error without data association information fusion according to the present invention;

FIG. 5 is a multi-shot Gaussian similarity curve of the present invention;

FIG. 6 is a diagram of a fuzzy synthesis function track associated position error simulation of the present invention;

FIG. 7 is a simulation diagram of track-associated speed error of the fuzzy synthesis function of the present invention.

Detailed Description

The following description of the embodiments of the present invention is provided to facilitate the understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and it will be apparent to those skilled in the art that various changes may be made without departing from the spirit and scope of the invention as defined and defined in the appended claims, and all matters produced by the invention using the inventive concept are protected.

As shown in fig. 1, the fuzzy association fusion method of the multi-missile cooperative system includes the following steps:

s1, carrying out filtering and space-time synchronization to obtain local estimation of each missile in the multi-missile detection system;

s2, receiving local estimation of each slave missile through the missile, and constructing a fuzzy factor set corresponding to any two missiles according to the local estimation of each slave missile; the guided missile is a guided missile, and the slave missile is a guided missile belonging to the guided missile;

s3, calculating each fuzzy factor set by adopting a Gaussian membership function to obtain a corresponding similarity value;

s4, acquiring the locally estimated comprehensive fuzzy similarity of any two missiles through a fuzzy comprehensive function based on the similarity value;

s5, constructing all the comprehensive fuzzy similarity into a comprehensive fuzzy similarity matrix;

s6, performing relevance judgment on the comprehensive fuzzy similarity matrix, eliminating local estimation of unassociated missiles, and fusing the local estimation of the associated missiles to obtain fused estimation information of the multi-missile detection system;

s7, correcting the error of the detection information of the slave bomb by using the fusion estimation information, and using the fusion estimation information as the input of the slave bomb guidance information.

The specific method of step S1 is:

according to the formula:

obtaining local estimates of missile j at time kWherein:

is a predicted value of the one-step state of the missile j at the moment k, phi_jIs the state transition matrix for missile j at time k,is a local estimation value of the missile j at the k-1 moment;

P_j(k | k-1) is a one-step covariance estimate of missile j at time k, P_j(k-1| k-1) is the covariance estimate of missile j at time k-1,is the transpose of the state transition matrix, Q, of the missile j at time k_jExciting a noise covariance matrix for the process;

K_j(k) as Kalman gain, H_j(K) Is an observation matrix of missile j at time k, H_j(K)^TTranspose matrix of observation matrix for missile j at time kR is a measured acoustic covariance matrix;

z (k) is the observation information of the missile j at the moment k;

P_jand (k | k) is the covariance estimation value of the missile j at the moment k, and I is an identity matrix.

The specific method of step S2 is:

respectively taking state estimation values of missile i and missile j in the previous two periods of current time k and k as input quantitiesAnd input quantityAnd according to the formula:

obtaining a fuzzy factor set mu corresponding to the missile i and the missile j_ij(l | l), wherein:

C_ij(l | l) is an intermediate parameter, P_i(l | l) is the covariance estimate of missile i at the current time k and two periods prior to k, P_j(l | l) is the covariance estimation value of the missile j of the current time k and the previous two periods of k; when l is equal to k-2,is a local estimate of missile i at time k-2.

The specific method of step S3 is:

according to the formula:

d_ij(l|l)＝exp{-μ_ij(l|l)^Tμ_ij(l|l)}

obtaining the corresponding similarity value d of missile i and missile j_ij(l | l); wherein mu_ij(l|l)^TFor the set of fuzzy factors mu corresponding to missile i and missile j_ijThe transposed matrix of (l | l).

The specific method of step S4 is:

according to the formula:

obtaining the comprehensive fuzzy similarity s_ij。

The specific method of step S5 is:

according to the formula:

obtaining a comprehensive fuzzy similarity matrix S_ijWherein i is the number of columns, j is the number of rows, and n is the number of missiles in the multi-missile detection system.

The specific method of step S6 is:

s6-1, maximum value element S for each column from the second column of the integrated fuzzy similarity matrix_i＝maxS_ijCarrying out missile correlation judgment, if s_ijIf the value is more than epsilon, judging that the missile pair (i, j) is associated at the moment, and performing step S6-2; otherwise, judging the missile is not associated, and rejecting the local estimation of the unassociated missile; wherein epsilon is a preset decision threshold;

s6-2, according to the formula:

fusing the local estimation of the related missile to obtain the fusion estimation information of the multi-missile detection systemWhere P (l | l) is the covariance estimate of missile i and missile j for the current time k and the first two cycles of k.

In an embodiment of the invention, a multi-missile cooperative system takes the situation that three missiles are networked to attack a high-speed maneuvering target as an example for simulation, wherein the missiles are all the same-mode radar missiles, and the specific situations are as follows:

assuming that the target and the missiles move in the same direction, S maneuver is performed within 0-10S, and 6g overload turning maneuver is performed after 10S, three missiles independently attack the target according to respective guidance laws, simulation is finished when any missile hits the target, and the initial positions of the three missiles are shown in table 1:

TABLE 1 simulation initial conditions

Assuming that the target 2 throws a bait bullet at the beginning of the 10 th turning maneuver, the bait bullet makes free-fall movement with the instantaneous speed of the target 10s as the initial speed, and the missile 3 in the multi-missile cooperative system is interfered by the bait bullet to generate error tracking.

The trajectory of the projectile motion of the multi-projectile coordination system is shown in fig. 2 and 3.

And (3) simulation results:

as shown in FIG. 4, the mistracking of missile 3 affects the normal tracking of the whole system, and the position estimation of the target diverges after 10 s.

As shown in fig. 5, before 5s, due to the difference of the initial states of the missiles, the difference of the filter estimation information is large, the rise of the gaussian similarity is slow, the missiles in 5-10s track and converge on the target, the gaussian similarity between the missiles is greater than 0.5, after 10s, the real target information is lost due to the missile 3, d₁₃,d₂₃The similarity decreases suddenly until the degree decreases to 0, d₁₂The high numerical value is still kept, the estimation information of the missile 3 is rejected by information fusion, high-precision position estimation is kept, the terminal leading missile of the trajectory approaches to the target, the estimation information begins to diverge, and the Gaussian similarity d₁₂And (4) descending.

As shown in fig. 6 and 7, the position error in three directions is less than 10m, the speed error is less than 10m/s, and the tracking effect is good.

The invention has the beneficial effects that: the missile in the multi-missile cooperative system is not easy to be subjected to deception interference, and the deviation of target detection can be corrected.