A Novel Modification of PSO Algorithm for SML Estimation of DOA

• Published in: Sensors (Basel, Switzerland) Vol. 16; no. 12; p. 2188
• Main Authors: Chen, Haihua, Li, Shibao, Liu, Jianhang, Liu, Fen, Suzuki, Masakiyo
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 19.12.2016; MDPI
• Subjects: Algorithms; Complexity; Computation; computational complexity; Computer simulation; Cramer-Rao bounds; Direction of arrival; Genetic algorithms; Maximum likelihood estimation; Optimization; Parameter estimation; Particle Swarm Optimization (PSO) algorithm; Sensor arrays; Signal processing; stochastic maximum likelihood; Swarm intelligence; direction-of-arrival; Particle Swarm Optimization (PSO) algorithm; stochastic maximum likelihood; computational complexity
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s16122188

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.