Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Maximum likelihood source localisation in wireless sensor network using particle swarm optimisation

Maximum likelihood source localisation in wireless sensor network using particle swarm optimisation Direction–Of–Arrival (DOA) estimation in wireless sensor network is an important problem. Decentralised approach using antenna arrays at each node or sensor arrays at different positions are used to localise the sources. In this paper we proposed a centralised method where every node will participate in bearing estimation to achieve best resolution with minimum computation. The DOA is obtained by optimising ML function formed by a random array with all the nodes globally. A Particle Swarm Optimisation (PSO) based solution is proposed here to compute DOA. Simulation results confirm the advantages of PSO over most analysed multiple signal classification (MUSIC) algorithms. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Signal and Imaging Systems Engineering Inderscience Publishers

Maximum likelihood source localisation in wireless sensor network using particle swarm optimisation

Download PDF
8 pages

Loading next page...
 
/lp/inderscience-publishers/maximum-likelihood-source-localisation-in-wireless-sensor-network-1yzhzcKxCV

References

References for this paper are not available at this time. We will be adding them shortly, thank you for your patience.

Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd. All rights reserved
ISSN
1748-0698
eISSN
1748-0701
DOI
10.1504/IJSISE.2013.053414
Publisher site
See Article on Publisher Site

Abstract

Direction–Of–Arrival (DOA) estimation in wireless sensor network is an important problem. Decentralised approach using antenna arrays at each node or sensor arrays at different positions are used to localise the sources. In this paper we proposed a centralised method where every node will participate in bearing estimation to achieve best resolution with minimum computation. The DOA is obtained by optimising ML function formed by a random array with all the nodes globally. A Particle Swarm Optimisation (PSO) based solution is proposed here to compute DOA. Simulation results confirm the advantages of PSO over most analysed multiple signal classification (MUSIC) algorithms.

Journal

International Journal of Signal and Imaging Systems Engineering – Inderscience Publishers

Published: Jan 1, 2013

There are no references for this article.