Force-directed hybrid PSO–SNTO algorithm for acoustic source localization in sensor networks

As a smart combination of particle swarm optimization (PSO) and sequential number-theoretic optimization (SNTO), a new hybrid PSO–SNTO algorithm is proposed to handle the initialization dependence of basic PSO algorithm. We then apply the hybrid algorithm to the acoustic source localization problem...

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Bibliographic Details
Published in:Signal processing Vol. 89; no. 8; pp. 1671 - 1677
Main Authors: Yu, Zhijun, Wei, Jianming, Liu, Haitao
Format: Journal Article
Language:English
Published: Amsterdam Elsevier B.V 01.08.2009
Elsevier
Subjects:
Acoustic sources
Algorithms
Applied sciences
Convergence
Detection, estimation, filtering, equalization, prediction
Exact sciences and technology
Information, signal and communications theory
Localization
Networks
Number-theoretic optimization
Optimization
Particle swarm optimization
Position (location)
Sensor networks
Signal and communications theory
Signal, noise
Source localization
Telecommunications and information theory
Virtual force
Number-theoretic optimization
Virtual force
Sensor networks
Source localization
Particle swarm optimization
Performance evaluation
Global optimum
Acoustic signal detection
Sound source
Algorithm
Biomimetics
Algorithm performance
Simulation
Heuristic method
Convergence rate
Maximum likelihood
Sensor array
Target detection
Signal detection
Number theory
ISSN:0165-1684, 1872-7557
Online Access:Get full text
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Summary:As a smart combination of particle swarm optimization (PSO) and sequential number-theoretic optimization (SNTO), a new hybrid PSO–SNTO algorithm is proposed to handle the initialization dependence of basic PSO algorithm. We then apply the hybrid algorithm to the acoustic source localization problem in sensor networks, which is modeled as a maximum likelihood estimation problem. Furthermore, a heuristic method based on virtual force is used to direct the particles of PSO to the global optimum, which can efficiently speed up the algorithm convergence. Simulation results demonstrate that the hybrid algorithm can achieve robust convergence with sophisticated estimation performance, and the convergence rate can be largely enhanced with the assistance of the force-directed heuristics.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:0165-1684
1872-7557
DOI:10.1016/j.sigpro.2009.03.003