Sidelobe Reduction of Linear Nonuniformly Spaced Arrays

This paper describes a modified genetic algorithm (MGA) for optimizing the performance of linear sparse arrays with a fixed current distribution. Under the design constraints of the number of elements, the aperture and the minimum element spacing, the MGA has been utilized to optimize the element position to reduce the peak sidelobe level (PSLL) of the sparse arrays. Unlike standard GA using unalterable corresponding relationship between the gene variables and their coding, the MGA employs the coding resetting of gene variables to avoid infeasible solution throughout the GA evolutionary process. And the proposed approach has reduced the size of the searching area of the GA by means of indirect description of individuals. The process of MGA and the simulated results confirming the great efficiency of this algorithm are provided in this paper