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Particle swarm optimization method for air combat decision

A particle swarm optimization and decision-making technology, applied in special data processing applications, instruments, biological neural network models, etc., can solve problems such as poor accuracy and slow convergence speed, and achieve the effect of good search ability

Inactive Publication Date: 2010-12-08
BEIHANG UNIV
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Problems solved by technology

[0004] The purpose of the present invention is to solve the shortcomings of the particle swarm optimization method for cooperative target allocation of cooperative multi-target attack air combat decision-making, such as local convergence, slow convergence speed and poor precision in the later stage of evolution, and propose a new type of particle swarm optimization method for air combat decision-making. The basic idea of ​​the particle swarm method, but the adjustment of the particle position is not based on the speed adjustment formula, but the BP neural network is used to make the particles continuously move to the global optimal solution

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  • Particle swarm optimization method for air combat decision

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Embodiment Construction

[0021] The present invention will be further described in detail with reference to the accompanying drawings and embodiments.

[0022] A particle swarm optimization method for air combat decision-making, the process is as follows figure 1 shown, including the following steps:

[0023] Step 1: Obtain the current situation of the battlefield from the command center;

[0024] Obtain the current situation of the battlefield from the command center, including: the number of our aircraft, the number of enemy aircraft, the code name of the aircraft, the number of weapons carried by each aircraft, the position and attitude of all aircraft on the current battlefield, the current flight speed of the aircraft, the aircraft The maximum tracking distance of the radar and the effective range of the weapon.

[0025] Step 2: According to the current battlefield situation, the threat factor between aircraft is obtained through the empirical formula.

[0026] The empirical formula for the th...

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Abstract

The invention discloses a particle swarm optimization method for an air combat decision, comprising the following steps of: firstly, acquiring the current situation of a battlefield from a command control center; secondly, acquiring a threat factor among aircrafts according to the current situation of the battlefield; thirdly, setting the particle swarm scale and the maximum iterations of the particle swarm; fourthly, initializing all particles of the particle swarm; fifthly, acquiring the threat degree of an enemy party on a first party after weapon attacks of the first part according to an empirical formula; sixthly, constructing a BP (Back Propagation) neural network; seventhly, updating the historical optimal position of the particle swarm and the individual historical optimal position of the particles; eighthly, continuously searching an air combat decision scheme until the maximum iterations of the particle swarm are achieved; and ninthly, determining the historical optimal position coordinate of the particle swarm as the obtained air combat decision. By processing the input and the output of the BP neural network, the decision method can move in a set solution space and has favorable search capability on the optimal solution.

Description

technical field [0001] The invention relates to a particle swarm optimization method for air combat decision-making, and belongs to the technical field of computer simulation and method optimization. Background technique [0002] Coordinated multi-target attack air combat decision-making has become one of the key technologies for modern fighters to realize beyond visual range air combat fire control system, and its research is of great significance. Multiple scattered targets. When the number of air targets increases sharply, we also need to dispatch a large number of aircraft to intercept and attack them at the same time, thus forming a group of aircraft coordinated air combat. The key to air combat decision-making for coordinated multi-target attack is to allocate targets for friendly aircraft according to our resources, and air combat situation assessment and threat analysis are the basis of target allocation. The core content of target attack air combat decision-making...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F19/00G06N3/02
Inventor 李妮邓英灿龚光红马耀飞
Owner BEIHANG UNIV
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