Non-uniform optical phased array based on dual-adaption genetic algorithm improved by chaos sequence

Abstract

To address the issue of diminishing peak side lobe levels (PSLL) in non-uniform optical phased array (OPA) as steering angle increases, we introduce a dual-adaption genetic algorithm (DAGA) based on chaos sequence to optimize the antenna array layout. By leveraging chaos mapping traversal and randomness, this approach enhances the diversity of the initial antenna layout, thereby improving the algorithm's capacity to escape local optima. Concurrently, we also reduce background noise during the array steering process to enhance PSLL at large steering angle. In this study, we optimize non-uniform antenna arrays with 64-, 128-, and 256-channels. The results demonstrate that at a steering angle of 80°, the PSLL is improved by 2.18, 2.81, and 3.59 dB, respectively, compared to the conventional genetic algorithm (GA). Notably, for a 256-channel array, the PSLL can be optimized to an impressive -18.46 dB using this method. To our knowledge, this represents the lowest PSLL achieved for an equivalent number of channels. Our research offers valuable insights for the practical implementation of OPAs as transmitters in chip-scale frequency modulated continuous wave (FMCW) light detection and ranging (LiDAR) systems.