Quantized beamforming for RAR suppression using convex-ABC hybrid algorithm

Because most realistic synthetic aperture radar (SAR) antennas use quantized controllers, beamforming is an important technique. However, existing heuristic algorithms have some drawbacks to the beamforming problem. The heuristic algorithms spend a lot of computation time and we should get an appropriate mask pattern empirically. Therefore, we hybridized a deterministic algorithm called convex optimization with the artificial bee colony (ABC) algorithm which is a heuristic algorithm to overcome the drawbacks. Using the suggested convex-ABC hybrid algorithm, we conducted quantized beamforming for range ambiguity ratio (RAR) suppression. The validity and efficiency of the proposed algorithm were verified according to the synthesized beam patterns and fitness value graphs. The optimized beam patterns were synthesized to have a lower RAR value with a shorter computation time compared to previous discrete-ABC algorithm Also, the suggested beamforming scheme is applicable to a variety of beam patterns by modifying the convex optimization constraints.