A Novel Modification of PSO Algorithm for SML Estimation of DOAReportar como inadecuado


A Novel Modification of PSO Algorithm for SML Estimation of DOA


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1

College of Computer and Communication Engineering, China University of Petroleum, Qingdao 266580, China

2

Graduate School of Engineering, Kitami Institute of Technology, Kitami 090-8507, Japan





*

Author to whom correspondence should be addressed.



Academic Editor: Joel J. P. C. Rodrigues

Abstract This paper addresses the issue of reducing the computational complexity of Stochastic Maximum Likelihood SML estimation of Direction-of-Arrival DOA. The SML algorithm is well-known for its high accuracy of DOA estimation in sensor array signal processing. However, its computational complexity is very high because the estimation of SML criteria is a multi-dimensional non-linear optimization problem. As a result, it is hard to apply the SML algorithm to real systems. The Particle Swarm Optimization PSO algorithm is considered as a rather efficient method for multi-dimensional non-linear optimization problems in DOA estimation. However, the conventional PSO algorithm suffers two defects, namely, too many particles and too many iteration times. Therefore, the computational complexity of SML estimation using conventional PSO algorithm is still a little high. To overcome these two defects and to reduce computational complexity further, this paper proposes a novel modification of the conventional PSO algorithm for SML estimation and we call it Joint-PSO algorithm. The core idea of the modification lies in that it uses the solution of Estimation of Signal Parameters via Rotational Invariance Techniques ESPRIT and stochastic Cramer-Rao bound CRB to determine a novel initialization space. Since this initialization space is already close to the solution of SML, fewer particles and fewer iteration times are needed. As a result, the computational complexity can be greatly reduced. In simulation, we compare the proposed algorithm with the conventional PSO algorithm, the classic Altering Minimization AM algorithm and Genetic algorithm GA. Simulation results show that our proposed algorithm is one of the most efficient solving algorithms and it shows great potential for the application of SML in real systems. View Full-Text

Keywords: direction-of-arrival; stochastic maximum likelihood; Particle Swarm Optimization PSO algorithm; computational complexity direction-of-arrival; stochastic maximum likelihood; Particle Swarm Optimization PSO algorithm; computational complexity





Autor: Haihua Chen 1,* , Shibao Li 1, Jianhang Liu 1, Fen Liu 1 and Masakiyo Suzuki 2

Fuente: http://mdpi.com/



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