Title: Application of comprehensive learning particle swarm optimisation algorithm for maximum likelihood DOA estimation in wireless sensor networks

Authors: Trilochan Panigrahi; Srinivas Roula; Harikrishna Gantayat

Addresses: Department of ECE, National Institute of Technology, Goa, 430401, India ' Department of ECE, National Institute of Science and Technology, Brahmapur, 430401, India ' Department of ECE, National Institute of Science and Technology, Brahmapur, 430401, India

Abstract: Direction of arrival (DOA) estimation is one of the challenging problem in wireless sensor networks. Several methods based on maximum likelihood (ML) criteria have been established in literature. But, the multimodal nature of ML cost function is one of the inherent limitations in ML-DOA estimation technique. Generally, to obtain the likelihood solutions, the DOAs must be estimated by optimising a complicated function over a high-dimensional problem space. Recently particle swarm optimisation (PSO) algorithm is used in MLDOA estimation. To overcome the drawback of premature convergence in PSO, a learning strategy is introduced, and this approach called comprehensive learning particle swarm optimisation (CLPSO), which is applied to this problem and a comparison of results is made between these two. Simulation results confirms that the ML-CLPSO estimator is significantly giving better performance at low signal-to-noise ratio compared to conventional methods like multiple signal classification (MUSIC) and ML-PSO in various scenarios at less computational costs.

Keywords: array signal processing; wireless sensor networks; WSNs; maximum likelihood DOA estimation; comprehensive learning PSO; particle swarm optimisation; CLPSO; MUSIC algorithm; direction of arrival; simulation; SNR; signal-to-noise ratio.

DOI: 10.1504/IJSI.2016.081145

International Journal of Swarm Intelligence, 2016 Vol.2 No.2/3/4, pp.208 - 228

Received: 09 May 2015
Accepted: 01 Feb 2016

Published online: 24 Dec 2016 *

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