Method for distributing targets cooperatively attacked by unmanned aerial vehicle group

A technology of target allocation and cluster, applied in the directions of instruments, adaptive control, control/regulation systems, etc., can solve problems such as increasing algorithm time consumption, timely response to unfavorable real-time tasks, and inability to achieve single-machine multi-objective solution, and achieve simple implementation. convenient effects

Inactive Publication Date: 2015-09-30
KUNMING UNIV OF SCI & TECH
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  • Summary
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Genetic algorithm and ant colony algorithm are also widely used in the problem of UAV target assignment, but it is easy to produce a solution that does not meet the constraint conditions in the process of solving, although the heuristic information in the constraint can be used to check

Method used

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  • Method for distributing targets cooperatively attacked by unmanned aerial vehicle group
  • Method for distributing targets cooperatively attacked by unmanned aerial vehicle group
  • Method for distributing targets cooperatively attacked by unmanned aerial vehicle group

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0020] Embodiment 1: as figure 1 Shown, a kind of unmanned aerial vehicle swarm cooperative strike target allocation method, the concrete steps of described method are as follows:

[0021] Step1. Initialize the particle swarm, convert the relationship between the UAV swarm and the target into a particle matrix form as a particle, and perform random initialization according to the constraints;

[0022] Step2. Formulate the fitness function f(x) according to the evaluation criteria, and calculate the fitness value of each particle;

[0023] Step3. Save the fitness value of each particle, and select the initial individual best fitness value and the global best fitness value from all particle fitness values ​​to save;

[0024] Step4. Judging whether the optimal target allocation scheme is reached, namely MAXf(x), MAXf(x) is the maximum value of the difference between the attack income and the battle damage. If the maximum value of the fitness function f(x) is not calculated, MAXf...

Embodiment 2

[0030] Embodiment 2: as figure 1 Shown, a kind of unmanned aerial vehicle swarm cooperative strike target allocation method, the concrete steps of described method are as follows:

[0031] Step1. Initialize the particle swarm, convert the relationship between the UAV swarm and the target into a particle matrix form as a particle, and perform random initialization according to the constraints;

[0032] Step2. Formulate the fitness function f(x) according to the evaluation criteria, and calculate the fitness value of each particle;

[0033] Step3. Save the fitness value of each particle, and select the initial individual best fitness value and the global best fitness value from all particle fitness values ​​to save;

[0034] Step4. Judging whether the optimal target allocation scheme is reached, namely MAXf(x), MAXf(x) is the maximum value of the difference between the attack income and the battle damage. If the maximum value of the fitness function f(x) is not calculated, MAXf...

specific example

[0054] Assume x i ( n ) = 1 0 0 0 0 1 0 1 0 0 1 0 , p m ( n ) = 1 0 0 1 0 0 ...

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Abstract

The invention relates to a method for distributing targets cooperatively attacked by an unmanned aerial vehicle group and belongs to the field of realtime embedding. The method includes: initializing a particle swarm, formulating a fitness function according to evaluation standards, storing fitness values of all particles, judging whether an optimal target distribution scheme is reached or not, and if not, using a random function, a cognitive function and a social function to update the particles, calculating fitness values of current particles, and updating a particle with individual optimal fitness and a particle with global optimal fitness; comparing particle individual fitness values after being updated and global fitness values after being updated with an initial individual optimal fitness value and a global optimal fitness value, evolving to a next generation after all particles are updated, and rejudging whether the optimal target distribution scheme is reached or not. A particle matrix between the unmanned aerial vehicle group and the targets is established, the problem of target distribution of the unmanned aerial vehicle group can be solved quickly and efficiently through constant self learning, single-vehicle to multiple-target attack can be realized, and the method is simple, convenient and quick.

Description

technical field [0001] The invention relates to a method for allocating targets for unmanned aerial vehicle group cooperative attack, and belongs to the field of real-time embedded technology. Background technique [0002] With the continuous advancement of new military reforms, real-time embedded UAVs have developed in the direction of high intelligence, high precision, and collaboration. As one of the key technologies of collaboration, cooperative attack refers to the mutual cooperation between two or more UAVs through the battlefield information network and guidance network to complete weapon strikes and attack ultra-long-range air targets, medium / long-range A new type of attack that strikes distance targets and ground targets. [0003] At present, the fire control and target allocation methods of unmanned aerial vehicles require high real-time performance, and the attack on the target can be completed in the shortest possible time. Genetic algorithm and ant colony algo...

Claims

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Application Information

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IPC IPC(8): G05B13/04
Inventor 张晶肖智斌薛冷容会汤守国范洪博崔毅李润鑫石少玲于胜军史舒鹏
Owner KUNMING UNIV OF SCI & TECH
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