Pattern Synthesis for Different Sparse Planar Arrays by a Hybrid Unconstrained-Heuristic Approach

A hybrid approach that combines the strategy of partial density tapering (PDT) and the multiobjective particle swarm optimization (MOPSO), called PDT-MOPSO, is proposed to address the synthesis of sparse planar arrays (SPAs) of different shapes. By performing the PDT, not only the strong constraint of element placement in SPAs is eased to nonconstrained issue, but also the element locations are forced to follow the distribution of density tapering somewhat to facilitate the sidelobe suppression. Then, by taking the sidelobe level (SLL) and directivity as the optimization objectives, the traditional MOPSO is used to optimize the element locations so that the SPAs with reduced number of elements, minimized SLL and nondegraded directivity are achieved. Some numerical instances confirmed the effectiveness of the proposed method.