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.
Highlights
The localization of multiple signal sources by a passive sensor array is of great importance in a wide variety of fields, such as radar, communications, underwater acoustics and so on
Particle Swarm Optimization (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 Stochastic Maximum Likelihood (SML) estimation and we call it Joint-PSO algorithm
Since this paper focuses on the problem of reducing the computational complexity of SML estimation and our proposed algorithm is general, the proposed algorithm is applied to the SML criteria (10)
Summary
The localization of multiple signal sources by a passive sensor array is of great importance in a wide variety of fields, such as radar, communications, underwater acoustics and so on. These algorithms can deal with small number of snapshots They can handle coherent signals without any pre-processing technique and the accuracy of DOA estimation is much higher than that of MUSIC and ESPRIT. This technique is based on the principle that, through optimization, the sparsity of a signal can be exploited to recover it from far fewer samples than required by the Shannon-Nyquist sampling theorem The feature of this kind of algorithm is that it needs fewer samples, but the accuracy of DOA estimation is much lower than that of the second type. The only drawback of SML is that the computational complexity is too high (the estimation of SML criteria is a multi-dimensional non-linear optimization problem) to be applied to real systems.
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