一种基于改进PSO的随机最大似然算法

随机最大似然算法(Stochastic Maximum Likelihood,SML)具有优越的波达方位(Direction-of-Arrival,DOA)估计性能,但SML解析过程较高的计算复杂度限制了该算法在实际系统中的应用.针对SML计算复杂度高的问题,提出一种低复杂度的粒子群优化算法(Particle Swarm Optimization,PSO),解决了传统PSO算法中粒子数多和迭代次数多的双重缺点.首先,根据天线获得的信号,将旋转不变子空间法(Estimation of Signal Parameters via Rotational Invariance Techniques,ESPRIT)求得的闭式解作为DOA的预估计值,同时计算系统此时的信噪比以及SML在此信噪比下的克拉-美罗界(Cramer-Rao bound,CRB).然后,根据DOA预估计值和当前CRB值在SML最优解的近邻范围内确定较小的初始化空间,并在该空间初始化少量粒子.最后通过设计合适的惯性因子w,使粒子以合理的速度搜索最优解.实验结果表明,改进PSO算法所需的粒子个数和迭代次数大约是传统PSO算法的1/5,降低了SML的解析复杂度,计算时间是传统PSO算法的1/10,因此在收敛速度上也有显著的优势.

A Stochastic Maximum Likelihood Algorithm Based on Improved PSO

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.