An Opposition-based Simulated Kalman Filter algorithm for adaptive beamforming

Abstract

In this paper, a population-based metaheuristic optimization algorithm named Opposition-based Simulated Kalman Filter (OBSKF) is proposed as an adaptive beamforming algorithm. This algorithm is based on Simulated Kalman Filter (SKF), which is a newly introduced optimization algorithm inspired by Kalman filtering. Opposition-based learning is employed in SKF to improve the performance of SKF in adaptive beamforming. Adaptive beamforming algorithm based on OBSKF is compared with SKF and the existing Adaptive Mutated Boolean Particle Swarm Optimization (AMBPSO). Experimental results show that adaptive beamforming algorithm based on OBSKF is better than SKF and AMBPSO.