Sparse circular array pattern optimization based on MOPSO and convex optimization

Abstract

In order to reduce the peak side-lobe level of the sparse array pattern effectively and suppress the grating lobe at the same time, this paper presents a pattern synthesis algorithm using multi-objective Particle Swarm (MOPSO) combined with convex optimization algorithm. We take MOPSO as a global searcher and convex optimization as a local searcher to search for the optimal solution. In this search, the optimization variables are not only the weights of the elements, but also introduce the parameter of the positions, which can provide more freedom to control the performance of the sparse array. Simulation of a sparse circular array model of thirty elements reveals that compared with MOPSO algorithm alone, the proposed algorithm which use MOPSO and convex optimization to optimize the positions and the weights of the elements respectively, the grating lobe and the peak side-lobe level can be reduced to −15.38dB at the same time.