An Improved Squirrel Search Algorithm for Maximum Likelihood DOA Estimation and Application for MEMS Vector Hydrophone Array

Peng Wang,Xuefang He,Yujun Kong,Mingxing Zhang,Xiuhui Tan

doi:10.1109/access.2019.2936823

Abstract

Maximum likelihood (ML) method for direction of arrival (DOA) estimation achieves an excellent performance in array signal processing, but the complexity and computational load of searching the multidimensional nonlinear function prevented it from practical application. Based on squirrel search algorithm (SSA), an improved SSA (ISSA) for ML DOA estimation is proposed in this paper, which can reduces the computational complexity. The idea of spatial variation and diffuse inspired by the invasive weed optimization(IWO) algorithm is applied to ISSA. The simulation experiments compared ISSA with SSA, IWO, seeker optimization algorithm(SOA), sine cosine algorithm (SCA), genetic algorithm (GA), particle swarm optimization (PSO) and differential evolution (DE) method for ML DOA estimator show that the proposed algorithm has faster convergence speed, fewer iterations and lower root mean square error(RMSE) under different number of signal sources, different signal to noise ratio(SNR) and different population size. Therefor the proposed algorithm does not only ensure the estimation accuracy, but also greatly reduce the computation complexity of multidimensional nonlinear optimization for the ML method. Finally, the test experiment using Micro Electronic Mechanical Systems(MEMS) vector hydrophone array in Fenhe lake show the engineering practicability of proposed ML DOA estimator with ISSA.The results obtained will be valuable in the application of engineering.

Highlights

Acoustic vector sensor(AVS) is a new type of underwater acoustic equipment, which is one of the research focuses of the underwater sound circle in the past two decades
The convergence curves of different improved SSA (ISSA), squirrel search algorithm (SSA), sine cosine algorithm (SCA), invasive weed optimization (IWO), genetic algorithm (GA), particle swarm optimization (PSO), differential evolution (DE) methods for maximum likelihood (ML) estimator are shown in Fig.7 when the number of signal sources is 2, 3, 4, respectively, in which the population sizes of these algorithms are all 30, and the maximum number of iterations is 200, the other parameters of the algorithms are taken in Tab
direction of arrival (DOA) estimation is a basic problem in the underwater acoustic signal processing

Summary

INTRODUCTION

Acoustic vector sensor(AVS) is a new type of underwater acoustic equipment, which is one of the research focuses of the underwater sound circle in the past two decades. In [25], a method combining artificial bee colony (ABC) algorithm with ML DOA estimation is presented, simulation results show the proposed method is more efficient in computation and statistical performance. The present work mathematically models this behaviour to realize the process of optimization These features may be helpful to improve convergence and reduce the number of iterations of SSA algorithm to determine the ML DOA estimate. A new ML DOA estimator based on the improved SSA(ISSA) is proposed, which has more advantages than other evolutionary methods in the aspect of lower SNR, computational complexity and convergence speed. Grid search is one of the most accurate methods in finding optimal solution of likelihood function, whose computational complexity depends on the grid size and search range, and on the number of signals.

FITNESS EVALUATION

SPATIAL VARIATION AND DIFFUSION

GENERATE NEW LOCATION

SEASONAL MONITORING

SIMULATION EXPERIMENTS AND DISCUSSION

CONCLUSION