A Chaotic Sailfish Optimizer for Accurate DOA Estimation in Automotive Radar

Accurate Direction of Arrival (DOA) estimation has been a challenging problem in communication. In this paper, the DOA estimation in the application of automotive radar is dealt as an optimization problem. A Maximum likelihood estimator is used, and the optimization is carried out by a recently reported Chaotic Sailfish Optimizer (CSFO). The simulation is demonstrated by using two sources, uniform linear sensor array of ten sensors, operating under very high noise conditions at OdB and 5dB SNR. Comparative analysis is demonstrated with the similar model trained with three more optimizer namely: Sailfish optimizer (SFO), Whale Optimizer (WoA), and Particle Swarm Optimizer (PSO). Simulation results reveal the accurate DOA estimation achieved by CSFO with reduced computational time.