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 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.