Large-scale sparse array synthesis method based on multi-agent genetic algorithm

Abstract

The invention discloses a multi-agent genetic algorithm suitable for large-scale sparse array synthesis, which solves the problem that the traditional optimization algorithm easily falls into local optimum when performing sparse array synthesis. The implementation steps are as follows: construct the antenna sparse array grid model; construct the initial sparse array layout plan, and use it as an agent to form a multi-agent grid environment; Domain expansion two improvement strategies; Neighborhood competition, Neighborhood orthogonal crossover and mutation operations are performed on the multi-agent grid in turn; Self-learning operation is performed on the optimal agent of the offspring; The energy value of the optimal agent is updated; When the maximum If the number of iterations is required, the optimal array arrangement scheme for the sparse array of antennas is output. By virtue of the fact that there is no global control in the evolution process of the multi-agent system, and each agent is an independent individual, the present invention realizes the purpose of using a small-scale population for fast and efficient synthesis of a large sparse phased array.

Description

technical field [0001] The invention belongs to the technical field of antennas, and relates to a large-scale antenna array sparse synthesis construction method, specifically a large-scale sparse array synthesis method based on a multi-agent genetic algorithm. It can be used in the fields of radar, wireless communication and electronic countermeasures to minimize cost and feed network complexity. Background technique [0002] In modern electronic warfare, in order to effectively counter targets and improve radar anti-jamming capabilities, low or ultra-low sidelobe arrays are required for radar antennas. At present, extremely low sidelobe antennas have become an important part of high-performance electronic systems. In some array application examples, simple feed network, light weight, and narrow beam are required, such as radar, remote sensing, satellite communication, and biomedical imaging. Considering the basic principle of the antenna array, the periodicity of the arra...

AI Technical Summary

Problems solved by technology

However, in some engineering applications, one of the common problems of genetic algorithm is that it is easy to converge prematurely, and it is easy to fall into the local optimal solution and cannot find the expected desired solution.

Patent No. CN104102791 A proposes a sparse construction method of antenna array based on quantum firefly search mechanism. Compared with traditional particle swarm algorithm and genetic algorithm, this method can find a higher quality solution, but this method is only get applied

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