Sparse Array Optimization Based on Modified Particle Swarm Optimization and Orthogonal Matching Pursuit

This paper addresses the low degree of freedom in optimization, primarily attributed to the conventional antenna array optimization methods that solely focus on the optimization of element positions, without considering the influence of element excitations. To address this issue, a sparse array opti...

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Bibliographic Details
Published in:Applied Computational Electromagnetics Society journal Vol. 40; no. 6; p. 508
Main Authors: Li, Daren, Guo, Qiang
Format: Journal Article
Language:English
Published: Pisa River Publishers 30.06.2025
Subjects:
Algorithms
Antenna arrays
Antennas
Arrays
Excitation
Genetic algorithms
Matched pursuit
Matching
Optimization algorithms
Optimization models
Particle swarm optimization
Radiation
Radio communications
Sidelobes
ISSN:1054-4887, 1943-5711
Online Access:Get full text
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Summary:This paper addresses the low degree of freedom in optimization, primarily attributed to the conventional antenna array optimization methods that solely focus on the optimization of element positions, without considering the influence of element excitations. To address this issue, a sparse array optimization method is proposed based on modified Particle Swarm Optimization (PSO) algorithm and Orthogonal Matching Pursuit (OMP). This method simultaneously optimizes both the element positions and excitations to achieve the desired pattern. Initially, the compressive sensing principle is employed to establish a compressive sensing optimization model for the antenna array. Subsequently, OMP is utilized to simultaneously optimize the element positions and excitations within the antenna array. An improved PSO algorithm is then applied to iteratively update the obtained parameters, thereby further enhancing the peak sidelobe level. Experimental results demonstrate that the proposed algorithm can achieve satisfactory optimization performance.
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ISSN:1054-4887
1943-5711
DOI:10.13052/2024.ACES.J.400603