CN110163502B - Multi-bullet cooperative multi-stage target distribution method - Google Patents

Multi-bullet cooperative multi-stage target distribution method Download PDF

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CN110163502B
CN110163502B CN201910423044.8A CN201910423044A CN110163502B CN 110163502 B CN110163502 B CN 110163502B CN 201910423044 A CN201910423044 A CN 201910423044A CN 110163502 B CN110163502 B CN 110163502B
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梁源
徐兵
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HUNAN ORDNANCE XINCHENG MACHINE Co.,Ltd.
Hunan Weidao Technology Co., Ltd
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Beijing Yiqing Technology Co ltd
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Abstract

The invention discloses a multi-missile cooperative multi-stage target distribution method which comprises the steps of firstly carrying out primary distribution on a plurality of missiles and a plurality of targets according to target prediction information provided by a target tracking system, launching the plurality of missiles according to a set primary distribution mode, and carrying out online redistribution on the plurality of missiles and the plurality of targets according to target information when the relative distance between the missiles and the targets is smaller than a preset threshold value. The invention divides the flow of the multi-missile cooperative target hitting into two stages, adopts the target distribution algorithm meeting the stage requirements to carry out target distribution according to different characteristics of different stages, and gives consideration to the real-time performance of the algorithm as much as possible while ensuring the distribution optimality.

Description

Multi-bullet cooperative multi-stage target distribution method
Technical Field
The invention relates to the field of target distribution, in particular to a multi-bullet cooperative multi-stage target distribution method.
Background
Among the numerous weapons, missiles, by their unique advantages, hold a high position among modern weaponry. The missile has the advantages of long attack distance, high attack precision, high speed, strong lethality and the like, so that the missile is used as a main weapon for destroying enemy targets in a long distance and is very different in war since being added into modern war. At present, the anti-attack capability of a defense system and a target is continuously improved, so that the original operation mode and operation concept are not more and more suitable for the modern war requirement, and meanwhile, the multi-missile cooperative operation becomes a new research hotspot. The multi-missile cooperative combat adjusts the past laggard combat concept and is more favorable for being integrated into the large environment of modern combined combat. The cooperative combat of the multiple missiles breaks through the situation that a single missile plays a single missile in the past, the multiple missiles form an attack network through communication and network connection among the multiple missiles, the great and good situation of information sharing, tactical coordination and function complementation is formed in the network, under the combined regulation and control of a command center, the multi-level and all-around comprehensive attack on a target is realized by utilizing group advantages, the attack network formed by the multiple missiles has great threat to a counterguidance system, the interception success rate of the counterguidance system is not very high originally, and therefore when the huge threat to the multiple missiles is met, the counterguidance system is fatigued to respond, and the effect of the counterguidance system is reduced to the minimum. At present, multi-missile cooperative combat is realized, and theoretical research and actual verification in the aspect are carried out in an effort in various countries. For example: the 'granite' anti-ship missile developed in Russia, the 'net fire' tactical missile system and the 'Sad' anti-guidance system in America are very typical cases of multi-missile cooperative combat.
When a plurality of missiles attack a plurality of targets in a cooperative manner, how to distribute the targets and what target distribution strategy is adopted is an important problem which needs to be solved in the cooperative combat of the plurality of missiles, and whether the target distribution is scientific or not directly determines the attack effect. Compared with a single missile attacking a single target, the most obvious difference of the multi-target attack is that comprehensive consideration needs to be carried out according to two aspects of the enemy and the my, the attack value of the target and the defense capability of the other party are considered, the resource constraint of the party is considered, and the attack capability and the characteristics of the missile are considered. In the case of air-air battles, the air battle situation between the two enemy and my is also considered. In general, the multi-target distribution problem is to give full play to the advantages of all parties after comprehensively considering factors of all aspects, fully utilize the hitting capability of the missile and strive to obtain the optimal combat effect.
Disclosure of Invention
The invention provides a multi-missile cooperative multi-stage target distribution algorithm to solve the problem of target distribution in a multi-missile-to-multi-target striking process.
In order to achieve the purpose, the technical scheme of the invention is as follows:
a multi-missile cooperative multi-stage target allocation method comprises the steps of firstly, carrying out primary allocation on a plurality of missiles and a plurality of targets according to target prediction information provided by a target tracking system, launching the plurality of missiles according to a set primary allocation mode, and carrying out online reallocation on the plurality of missiles and the plurality of targets according to target information at the moment when the relative distance between the missiles and the targets is smaller than a preset threshold value.
The scheme is further as follows: and when the relative distance between the missile and the target is smaller than the preset threshold value, the relative distance between one missile of the plurality of missiles and the target is smaller than the preset threshold value.
The scheme is further as follows: the line redistribution is to adopt an auction algorithm to realize the online redistribution of the target.
The scheme is further as follows: the process for realizing the online redistribution of the target by adopting the auction algorithm comprises the following steps:
the first step is as follows: establishing a redistribution killing probability matrix formula,
Figure BDA0002066611510000021
Figure BDA0002066611510000022
P_onlineijrepresenting the probability of successful attack of the ith missile on the jth target in the online redistribution stage,
wherein P isTgo_online(ij)And
Figure BDA0002066611510000023
the calculation formula of (a) is as follows:
Figure BDA0002066611510000031
Figure BDA0002066611510000032
Figure BDA0002066611510000033
Figure BDA0002066611510000034
wherein, Tgo_online(ij) assuming that the ith missile takes the jth target as the residual flight time value required for hitting the target,
Figure BDA0002066611510000035
the sight line rotation angular velocity value corresponding to the ith missile with the jth target as a hitting target is assumed;
wherein: m is the number of missiles, n is the number of targets, and m is larger than n; t isgo_online(ij) and
Figure BDA0002066611510000036
all the information is provided by a target tracking system, and the calculation is carried out by taking the striking information of the target at the current moment provided by the target tracking system as reference information;
the second step is that: establishing an unallocated set and an allocated set, putting the missiles into the unallocated set, and initializing the killing probability value of the missiles to each target to be 0;
the third step: judging whether the unallocated set is empty, and if the unallocated set is empty, outputting a final allocation result to complete reallocation; otherwise, executing the fourth step;
the fourth step: and calculating the killing probability value of each missile corresponding to different targets according to the redistribution killing probability matrix formula provided in the first step, and distributing according to an auction algorithm: and taking the killing probability value as the bid of the missile on the target, acquiring the missile with the highest bid on the target, putting the missile and the target into an allocated set, and returning to the third step.
The scheme is further as follows: the distribution algorithm of the primary distribution adopts a genetic algorithm, and the cross probability P in the genetic algorithmdAnd the mutation probability PmCarrying out self-adaptive selection, which respectively comprises the following steps:
Figure BDA0002066611510000041
and
Figure BDA0002066611510000042
and satisfies the following conditions:
Figure BDA0002066611510000043
Figure BDA0002066611510000044
the genetic algorithm process is as follows:
the first step is as follows: establishing an initial chromosome population;
the second step is that: judging whether the termination condition of the algorithm is met, and if so, outputting a search result; otherwise, executing the following steps;
the third step: performing DBSCAN clustering on all chromosomes, and performing cross probability P according to clustering resultdAnd the mutation probability PmAdaptive selection of (1);
the fourth step: performing copy operation according to the self-adaptive selection value;
the fifth step: according to the cross probability P selected in the third stepdPerforming a crossover operation on the chromosome;
and a sixth step: according to the mutation probability P selected in the third stepmPerforming mutation operations on the chromosome;
and a sixth step: returning to the second step;
wherein: the initial chromosome population established was:
the first step is as follows: determining the relationship of the chromosome to the missile and the target, namely: arranging each chromosome according to the missile number sequence to form a target unit by adopting a decimal representation mode and with the chromosome length equal to the total number m of missiles, wherein each gene represents the result of allocating a certain target to one missile, and the termination condition of the chromosomes in the initial population is as follows: each target is required to be allocated with at least 1 missile, and each missile is required to be allocated to the target;
the second step is that: and establishing a fitness function relation formula according to the relation, wherein a fitness function f is as follows:
Figure BDA0002066611510000045
wherein:
Figure BDA0002066611510000046
P_preijrepresenting the probability of successful attack of the ith missile on the jth target in the initial distribution stage before launching, the specific numerical calculation method is as follows:
Figure BDA0002066611510000051
wherein P isTgo_pre(ij)And
Figure BDA0002066611510000052
the calculation formula of (a) is as follows:
Figure BDA0002066611510000053
Figure BDA0002066611510000054
Figure BDA0002066611510000055
Figure BDA0002066611510000056
wherein, Tgo_pre(ij) is a predicted value of the residual flight time required by the ith missile by taking the jth target as a target,
Figure BDA0002066611510000057
the parameter T is a predicted value of the sight line rotation angular velocity corresponding to the hypothesis that the ith missile uses the jth target as a striking targetgo_pre(ij) and
Figure BDA0002066611510000058
all provided by a target tracking system, and the calculation is performed by taking target predicted striking information provided by the target tracking system as reference information, wherein: m is the number of missiles, n is the number of targets, and m is larger than n.
The scheme is further as follows: the cross probability P in the third stepdAnd the mutation probability PmThe self-adaptive selection mode is as follows:
if the chromosomes are judged to be of the same type through the DBSCAN algorithm, firstly, the fitness function results of all the chromosomes are calculated, and the mean value f of the fitness function results is calculatedmeanWhen the result is greater than f for fitness functionmeanChromosome of (2), cross probability P thereofdAnd the mutation probability PmThe selection is as follows:
Figure BDA0002066611510000059
and
Figure BDA00020666115100000510
further performing subsequent calculations; and for fitness function results less than fmeanChromosome of (2), cross probability P thereofdAnd the mutation probability PmThe selection is as follows:
Figure BDA0002066611510000061
and
Figure BDA0002066611510000062
further performing subsequent calculations;
if the chromosomes are judged not to be of the same class through the DBSCAN algorithm, the cross probability P of all the chromosomesdAnd the mutation probability PmThe selection is as follows:
Figure BDA0002066611510000063
and
Figure BDA0002066611510000064
and then perform subsequent calculations.
The invention has the beneficial effects that:
1. the invention divides the flow of the multi-missile cooperative target hitting into two stages, adopts the target distribution algorithm meeting the stage requirements to distribute the targets according to different characteristics of different stages, and gives consideration to the algorithm instantaneity as far as possible while ensuring the distribution optimality.
2. According to the invention, different cross probabilities and variation probabilities are selected for different chromosomes, so that the rapid optimization of the algorithm is realized on the basis of ensuring the population diversity, and the aim of taking account of both the rapidity of operation and the optimality of results is fulfilled.
The invention is described in detail below with reference to the figures and examples.
Drawings
FIG. 1 is a block diagram of a multi-shot cooperative multi-stage target assignment algorithm designed in this patent;
FIG. 2 is a timing diagram of a multi-shot cooperative multi-stage target allocation algorithm designed in this patent;
FIG. 3 is a schematic flow chart of the improved genetic algorithm designed in this patent.
Detailed Description
A multi-missile cooperative multi-stage target allocation method comprises the steps of firstly, carrying out primary allocation on a plurality of missiles and a plurality of targets according to target prediction information provided by a target tracking system, launching the plurality of missiles according to a set primary allocation mode, and carrying out online reallocation on the plurality of missiles and the plurality of targets according to target information at the moment when the relative distance between the missiles and the targets is smaller than a preset threshold value. Wherein: and when the relative distance between the missile and the target is smaller than the preset threshold value, the relative distance between one missile of the plurality of missiles and the target is smaller than the preset threshold value.
The algorithm characteristic of the allocation algorithm adopted for the initial allocation before the shooting is as follows: the global optimality of the distribution result is good (generally, the global optimal solution), but the arithmetic efficiency of the algorithm is poor.
The algorithm characteristics of the distribution algorithm adopted by the online redistribution are as follows: the distribution result may be only locally optimal or even locally suboptimal, and is only a "relatively satisfactory" distribution result, but the algorithm has high operation efficiency.
In the embodiment, the distribution algorithm adopted by the pre-shooting initial distribution is an improved genetic algorithm, the cross probability and the variation probability are automatically adjusted on line according to the chromosome clustering result, so that the optimal chromosomes are kept as far as possible, the poor chromosomes are eliminated as fast as possible, and the algorithm is quickly optimized on the basis of ensuring the population diversity.
The overall flow chart of the algorithm is shown in fig. 1. The algorithm timing diagram is shown in fig. 2.
For convenience of further description, the variables and the associated definitions used for target allocation in this embodiment are first described as follows:
assuming that the number of missiles is m, the number of targets is n, and m > n is generally true, the distribution matrix of the missiles hitting the targets is as follows:
Figure BDA0002066611510000071
wherein the content of the first and second substances,
Figure BDA0002066611510000072
meanwhile, the constraint conditions are met:
Figure BDA0002066611510000073
in the formula, the first constraint indicates that each target to be attacked is assigned to at least one missile; the second constraint means that each missile must attack one and only one target.
The attack profit refers to the value of the target obtained when the missiles execute the tasks, and the index guides the optimization and decision of target distribution to the direction of maximizing the fighting efficiency. Here, the probability of killing the missile attack of our party is taken as the attack income factor of the missile, so that the missile tends to attack a target which is high in value and can be effectively intercepted. The kill probability matrix is defined as follows:
Figure BDA0002066611510000081
wherein, PijRepresenting the probability of successful attack of the ith missile on the jth target, in the patent, the calculation methods of the killing probability matrix D in the initial distribution before launching and the online redistribution are not completely consistent, and the specific calculation method is described in detail in each step below.
In the examples: the line redistribution is to adopt an auction algorithm to realize the online redistribution of the target.
The basic idea of the auction algorithm is to treat task allocation as a trading process, and to implement assignment and migration of tasks through a market auction mechanism of bid-bid. When an agent in the intelligent unmanned system finds that the agent has insufficient capacity to process certain tasks or the cost of executing the tasks is too high in the process of executing the tasks, the tasks are auctioned externally, other agents bid according to the capacity and the state of the agents, and then the agent hosting the auction migrates the tasks to the agent capable of executing the tasks at lower cost. The task allocation method based on the auction algorithm is simple in principle, high in efficiency and good in real-time performance, and can achieve online target allocation.
The basic idea of the auction algorithm is outlined as follows: suppose buyer i expects the maximum bid on item j to be aijFor an article, must supportThe price paid is pjThen item j has a net profit to buyer i of aij-pjFor each buyer, the pursuit is for net profit maximization, i.e.
Figure BDA0002066611510000082
When each buyer is satisfied, the set of allocations and the set of prices are balanced. Such a balanced distribution provides the greatest overall profit for the whole, and the whole is optimal.
In practical application, in order to avoid the operation process falling into the loop, a chaos mechanism is introduced to break the loop, i.e. the bidding price of each article must be increased by at least 1 fixed positive number epsilon than the last bidding price, so that when the operation process is finished
Figure BDA0002066611510000083
In time, it can be considered that an allocation and a group of prices are in substantial balance, all have reached a substantially satisfactory state, and have reached an optimized state as a whole.
Wherein: the process for realizing the online redistribution of the target by adopting the auction algorithm comprises the following steps:
the first step is as follows: establishing a redistribution killing probability matrix formula,
Figure BDA0002066611510000091
Figure BDA0002066611510000092
P_onlineijrepresenting the probability of successful attack of the ith missile on the jth target in the online redistribution stage,
wherein P isTgo_online(ij)And
Figure BDA0002066611510000093
the calculation formula of (a) is as follows:
Figure BDA0002066611510000094
Figure BDA0002066611510000095
Figure BDA0002066611510000096
Figure BDA0002066611510000097
wherein, Tgo_online(ij) assuming that the ith missile takes the jth target as the residual flight time value required for hitting the target,
Figure BDA0002066611510000098
the sight line rotation angular velocity value corresponding to the ith missile with the jth target as a hitting target is assumed;
wherein: m is the number of missiles, n is the number of targets, and m is larger than n; t isgo_online(ij) and
Figure BDA0002066611510000099
all the information is provided by a target tracking system, and the calculation is carried out by taking the striking information of the target at the current moment provided by the target tracking system as reference information;
the second step is that: establishing an unallocated set and an allocated set, putting the missiles into the unallocated set, and initializing the killing probability value of the missiles to each target to be 0;
the third step: judging whether the unallocated set is empty, and if the unallocated set is empty, outputting a final allocation result to complete reallocation; otherwise, executing the fourth step;
the fourth step: and calculating the killing probability value of each missile corresponding to different targets according to the redistribution killing probability matrix formula provided in the first step, and distributing according to an auction algorithm: and taking the killing probability value as the bid of the missile on the target, acquiring the missile with the highest bid on the target, putting the missile and the target into an allocated set, and returning to the third step.
For the initial allocation, a killing probability matrix D _ pre used for the initial pre-firing allocation is first defined and explained, and a specific numerical calculation method thereof is as follows:
Figure BDA0002066611510000101
Figure BDA0002066611510000102
P_preijrepresenting the probability of successful attack of the ith missile on the jth target in the initial distribution stage before launching.
Wherein P isTgo_pre(ij)And
Figure BDA0002066611510000103
the calculation formula of (a) is as follows:
Figure BDA0002066611510000104
Figure BDA0002066611510000105
Figure BDA0002066611510000111
Figure BDA0002066611510000112
wherein, Tgo_pre(ij) is a predicted value of the residual flight time required by the ith missile by taking the jth target as a target,
Figure BDA0002066611510000113
in order to assume that the ith missile uses the jth target as the predicted sight rotation angular velocity value corresponding to the hitting target, the two parameters are both provided by the target tracking system, and it should be noted that, when the parameters are calculated, the target predicted hitting information provided by the target tracking system is used as reference information for calculation.
On the basis of the D _ pre, the distribution algorithm of the initial distribution adopts a genetic algorithm, and the cross probability P in the genetic algorithmdAnd the mutation probability PmCarrying out self-adaptive selection, which respectively comprises the following steps:
Figure BDA0002066611510000114
and
Figure BDA0002066611510000115
and satisfies the following conditions:
Figure BDA0002066611510000116
Figure BDA0002066611510000117
the flow chart of the improved genetic algorithm is shown in fig. 3, and the genetic algorithm process is as follows:
the first step is as follows: establishing an initial chromosome population;
the second step is that: judging whether the termination condition of the algorithm is met, and if so, outputting a search result; otherwise, executing the following steps;
the third step: performing DBSCAN Clustering (Noise-Based Density Clustering method) on all chromosomes, and performing cross probability P according to Clustering resultdAnd the mutation probability PmAdaptive selection of (1);
the fourth step: performing copy operation according to the self-adaptive selection value;
the fifth step: according to the cross probability P selected in the third stepdPerforming a crossover operation on the chromosome;
and a sixth step: according to the mutation probability P selected in the third stepmPerforming mutation operations on the chromosome;
and a sixth step: returning to the second step;
the above flow is a traditional genetic algorithm flow, and related algorithms are fully disclosed, and will not be described too much here. The patent aims at the cross probability P of the traditional genetic algorithmdAnd the mutation probability PmAdaptive adaptation of constants without fixed PdAnd PmAnd the rapid convergence can be realized on the basis of ensuring the diversity of the population by operation.
Wherein: the initial chromosome population established was:
the first step is as follows: determining the relationship of the chromosome to the missile and the target, namely: in a decimal representation mode, with the length of a chromosome equal to the total number m of missiles, each chromosome is arranged according to the number sequence of the missiles to form a target unit, each gene represents the result of a certain target assigned to one missile, for example, the total number m of the missiles is 10, the total number n of the targets is 6, and the form of one chromosome is as follows: [2542136514]
The 2 nd target is assigned to the 1 st missile, the 5 th target is assigned to the 2 nd missile, the 4 th target is assigned to the 3 rd missile, and so on, and the value range of the chromosome gene is [1, n ].
Wherein the termination conditions of chromosomes in the initial population are as follows: each target is required to be allocated with at least 1 missile, and each missile is required to be allocated to the target;
the second step is that: and establishing a fitness function relation formula according to the relation, wherein a fitness function f is as follows:
Figure BDA0002066611510000121
wherein:
Figure BDA0002066611510000122
P_preijrepresentsIn the initial distribution stage before launching, the probability that the ith missile successfully strikes the jth target is realized, and the specific numerical calculation method is as follows:
Figure BDA0002066611510000123
wherein P isTgo_pre(ij)And
Figure BDA0002066611510000124
the calculation formula of (a) is as follows:
Figure BDA0002066611510000131
Figure BDA0002066611510000132
Figure BDA0002066611510000133
Figure BDA0002066611510000134
wherein, Tgo_pre(ij) is a predicted value of the residual flight time required by the ith missile by taking the jth target as a target,
Figure BDA0002066611510000135
the parameter T is a predicted value of the sight line rotation angular velocity corresponding to the hypothesis that the ith missile uses the jth target as a striking targetgo_pre(ij) and
Figure BDA0002066611510000136
all provided by a target tracking system, and the calculation is performed by taking target predicted striking information provided by the target tracking system as reference information, wherein: m is the number of missiles, n is the number of targets, and m is larger than n.
Wherein: in the third step above the crossover probability PdAnd the mutation probability PmThe self-adaptive selection mode is as follows:
if the chromosomes are judged to be of the same type through the DBSCAN algorithm, firstly, the fitness function results of all the chromosomes are calculated, and the mean value f of the fitness function results is calculatedmeanWhen the result is greater than f for fitness functionmeanChromosome of (2), cross probability P thereofdAnd the mutation probability PmThe selection is as follows:
Figure BDA0002066611510000137
and
Figure BDA0002066611510000138
further performing subsequent calculations; and for fitness function results less than fmeanChromosome of (2), cross probability P thereofdAnd the mutation probability PmThe selection is as follows:
Figure BDA0002066611510000139
and
Figure BDA00020666115100001310
further performing subsequent calculations;
if the chromosomes are judged not to be of the same class through the DBSCAN algorithm, the cross probability P of all the chromosomesdAnd the mutation probability PmThe selection is as follows:
Figure BDA0002066611510000141
and
Figure BDA0002066611510000142
and then perform subsequent calculations.
Among them, for the termination condition of the genetic algorithm, it is required to be able to complete the target assignment within a limited time in consideration of the actual combat time. Therefore, the algorithm termination condition of the project adopts the combination of 'appointed genetic evolution algebra' and 'the quality of the best solution in successive generations is not improved any more'. And when the termination condition is met, outputting the chromosome with the maximum fitness value in the current population as a solution of the problem, wherein the fitness value is the total benefit value of the allocation scheme.

Claims (4)

1. A multi-missile cooperative multi-stage target allocation method is characterized in that a plurality of missiles and a plurality of targets are allocated for the first time according to target prediction information provided by a target tracking system, the missiles are launched according to a set initial allocation mode, when the relative distance between the missiles and the targets is smaller than a preset threshold value, the missiles and the targets are reallocated on line according to the target information at the moment, the online reallocation is realized by adopting an auction algorithm, and the process of realizing the online reallocation of the targets by adopting the auction algorithm is as follows:
the first step is as follows: establishing a redistribution killing probability matrix formula,
Figure FDA0002833286430000011
Figure FDA0002833286430000012
P_onlineijrepresenting the probability of successful attack of the ith missile on the jth target in the online redistribution stage,
wherein P isTgo_online(ij)And
Figure FDA0002833286430000013
the calculation formula of (a) is as follows:
Figure FDA0002833286430000014
Figure FDA0002833286430000015
Figure FDA0002833286430000016
Figure FDA0002833286430000017
wherein, Tgo_online(ij) assuming that the ith missile takes the jth target as the residual flight time value required for hitting the target,
Figure FDA0002833286430000021
the sight line rotation angular velocity value corresponding to the ith missile with the jth target as a hitting target is assumed;
wherein: m is the number of missiles, n is the number of targets, and m is larger than n; t isgo_online(ij) and
Figure FDA0002833286430000022
all the information is provided by a target tracking system, and the calculation is carried out by taking the striking information of the target at the current moment provided by the target tracking system as reference information;
the second step is that: establishing an unallocated set and an allocated set, putting the missiles into the unallocated set, and initializing the killing probability value of the missiles to each target to be 0;
the third step: judging whether the unallocated set is empty, and if the unallocated set is empty, outputting a final allocation result to complete reallocation; otherwise, executing the fourth step;
the fourth step: and calculating the killing probability value of each missile corresponding to different targets according to the redistribution killing probability matrix formula provided in the first step, and distributing according to an auction algorithm: and taking the killing probability value as the bid of the missile on the target, acquiring the missile with the highest bid on the target, putting the missile and the target into an allocated set, and returning to the third step.
2. The multi-missile cooperative multi-stage target distribution method according to claim 1, wherein when the relative distance between the missile and the target is smaller than a preset threshold value, the relative distance between one missile of the multiple missiles and the target is smaller than the preset threshold value.
3. The method of claim 1, wherein the initial allocation algorithm is a genetic algorithm with a cross probability PdAnd the mutation probability PmCarrying out self-adaptive selection, which respectively comprises the following steps:
Figure FDA0002833286430000023
and
Figure FDA0002833286430000024
and satisfies the following conditions:
Figure FDA0002833286430000025
the genetic algorithm process is as follows:
the first step is as follows: establishing an initial chromosome population;
the second step is that: judging whether the termination condition of the algorithm is met, and if so, outputting a search result; otherwise, executing the following steps;
the third step: performing DBSCAN clustering on all chromosomes, and performing cross probability P according to clustering resultdAnd the mutation probability PmAdaptive selection of (1);
the fourth step: performing copy operation according to the self-adaptive selection value;
the fifth step: according to the cross probability P selected in the third stepdPerforming a crossover operation on the chromosome;
and a sixth step: according to the mutation probability P selected in the third stepmPerforming mutation operations on the chromosome;
and a sixth step: returning to the second step;
wherein: the initial chromosome population established was:
the first step is as follows: determining the relationship of the chromosome to the missile and the target, namely: arranging each chromosome according to the missile number sequence to form a target unit by adopting a decimal representation mode and with the chromosome length equal to the total number m of missiles, wherein each gene represents the result of allocating a certain target to one missile, and the termination condition of the chromosomes in the initial population is as follows: each target is required to be allocated with at least 1 missile, and each missile is required to be allocated to the target;
the second step is that: and establishing a fitness function relation formula according to the relation, wherein a fitness function f is as follows:
Figure FDA0002833286430000031
wherein:
Figure FDA0002833286430000032
P_preijrepresenting the probability of successful attack of the ith missile on the jth target in the initial distribution stage before launching, the specific numerical calculation method is as follows:
Figure FDA0002833286430000033
wherein P isTgo_pre(ij)And
Figure FDA0002833286430000034
the calculation formula of (a) is as follows:
Figure FDA0002833286430000041
Figure FDA0002833286430000042
Figure FDA0002833286430000043
Figure FDA0002833286430000044
wherein, Tgo_pre(ij) is a predicted value of the residual flight time required by the ith missile by taking the jth target as a target,
Figure FDA0002833286430000045
the parameter T is a predicted value of the sight line rotation angular velocity corresponding to the hypothesis that the ith missile uses the jth target as a striking targetgo_pre(ij) and
Figure FDA0002833286430000046
all provided by a target tracking system, and the calculation is performed by taking target predicted striking information provided by the target tracking system as reference information, wherein: m is the number of missiles, n is the number of targets, and m is larger than n.
4. A multi-shot cooperative multi-phase target allocation method according to claim 3, wherein the cross probability P in the third stepdAnd the mutation probability PmThe self-adaptive selection mode is as follows:
if the chromosomes are judged to be of the same type through the DBSCAN algorithm, firstly, the fitness function results of all the chromosomes are calculated, and the mean value f of the fitness function results is calculatedmeanWhen the result is greater than f for fitness functionmeanChromosome of (2), cross probability P thereofdAnd the mutation probability PmThe selection is as follows:
Figure FDA0002833286430000047
and
Figure FDA0002833286430000048
further performing subsequent calculations; and for fitness function results less than fmeanDyeing ofBody of cross probability PdAnd the mutation probability PmThe selection is as follows:
Figure FDA0002833286430000049
and
Figure FDA00028332864300000410
further performing subsequent calculations;
if the chromosomes are judged not to be of the same class through the DBSCAN algorithm, the cross probability P of all the chromosomesdAnd the mutation probability PmThe selection is as follows:
Figure FDA0002833286430000051
and
Figure FDA0002833286430000052
and then perform subsequent calculations.
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