关键词: 波达方位估计, 粒子群优化算法, 随机最大似然算法, 计算复杂度

Abstract: The Stochastic Maximum Likelihood (SML) achieves exceptional performance of estimating Direction-of-Arrival (DOA).However,the high computational complexity of analytic method limits SML for further applications in practice.Considering the high computational complexity of SML,we propose a low complexity improved PSO algorithm,which outperforms the traditional PSO approach both in the number of particles and iterations.Based on the signals received by antenna,we firstly obtain the closed solution of Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT) to pre-estimate the DOA.In addition,we compute the current Signal Noise Ratio (SNR) of the system as well as the SNR based Cramer-Rao Bound (CRB) of the SML.According to the pre-estimated DOA and current CRB,we then determine a small specific initialized space which is closed to the optimal solution of SML.Besides,we set a few particles in the corresponding search space.Finally,we construct the appropriate inertia factor which lead to an appropriate search speed for particles.Experimental results demonstrate that the number of particles and iteration times required by the improved PSO algorithm is about one-fifth of the traditional PSO algorithm,which greatly reduces the computational complexity of SML,the computation time is one-tenth of the traditional PSO algorithm,thus,the proposed method achieves significant merit of convergence speed.

Key words: direction-of-arrival estimation, particle swarm optimization, stochastic maximum likelihood algorithm, computational complexity