A parallel chaotic sailfish optimization algorithm for estimation of DOA in wireless sensor array

Abstract

Direction of Arrival (DOA) estimation by Maximum Likelihood (ML) method has been popular among researchers due to its effective performance. However, its practical applications are limited by its computational complexity in non-linear multidimensional solution search which requires optimization. In order to make accurate non-linear ML-DOA estimation, simultaneously reducing the computational complexity involved in the process evolutionary algorithms are employed in this manuscript. In this manuscript, a recently reported nature-inspired optimization algorithm termed as ‘sailfish optimizer’ has been applied for non-linear multidimensional DOA estimation. The sailfish optimization is based on the swarming behavior of a group of sailfish to catch the prey which are sardines. The algorithm has been designed with both random and chaotic sequence-based population initialization. Simulation studies reveal that the chaotic sequence-based algorithm has lower computational time and accurate DOA estimation compared to its random counterpart. A parallel implementation of the chaotic version of the algorithm has been carried out to reduce the computational aspect of the algorithm. Simulation studies reveal the parallel version has almost 50% lower run time than that of the sequential one for 2048 snapshots implementations. By varying the number of sources, sensors, and snapshots experimentation has been carried out which reveals accurate DOA estimation (more than 95% angle accuracy in most of the cases) with reduced computational time (due to parallel implementation) achieved with the proposed algorithm. The comparative analysis of the proposed algorithm has been carried out with that achieved by benchmark MUSIC (Multiple Signal Classification) algorithm and nature inspired algorithms like : Advanced Particle Swarm Optimization, Real Coded Genetic Algorithm, Differential Evolution, CLONAL Selection, and Artificial Bee Colony algorithm. Kruskal–Wallis non-parametric test is performed under various noise conditions to validate the superior performance of the proposed approach over the comparative algorithms.