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.