摘要
在现有DOA估计的方法中,加权子空间拟合(WSF)具有很高分辨率.但是求解WSF算子的算法计算复杂度较高,无法满足实时性需求.为了降低计算复杂度,本文针对WSF算法提出了一种低复杂度的联合粒子群算法.首先利用旋转不变子空间法(ESPRIT)可以显式计算DOA结果,计算复杂度极低的特点,并联合利用克拉美-罗界来确定一个新的搜索空间,再随机撒入少量粒子进行粒子群算法,最后在满足一定的速度条件后跳出迭代.此外,本文也讨论了粒子群算法的惯性因子.试验结果表明,跟常规粒子群算法比较,在保持DOA估计精度不变的结果下,本文算法所需粒子数和迭代次数大幅度降低,计算复杂度也明显降低.
Among existing DOA estimation methods, the Weighted Subspace Fitting （WSF） algorithm is well-known for its high resolution of DOA estimation. However, its computational complexity is extremely high and cannot meet the real- time requirements. In this paper, we propose a Joint-PSO algorithm for WSF with less complexity. This algorithm has the following key steps： firstly we use the solution of Estimation of Signal Parameters via Rotational Invariance Techniques （ESPRIT） which can get the DOA estimation with extremely low complexity and stochastic Cramer-Rao bound （CRB） to determine a novel initialization space in the whole search space. Then, we randomly initiate a small number of particle in that small area. Finally, we let the particles ＂fly＂ to the solution with a suitable speed. Additionally, we also discuss and optimize the inertia factor of PSO algorithm. The simulation results find that for the same Root-Mean-Square-Error （RMSE）, the particles and iteration number of the proposed algorithm are much less than that of the original PSO algorithm. As a result, the computational complexity can be greatly reduced.
出处
《计算机系统应用》
2017年第8期162-167,共6页
Computer Systems & Applications
基金
山东省自然科学基金面向项目(ZR2014FM017)
青岛市科技创新计划(15-9-80-jch)
青岛市黄岛区科技创新计划(2014-1-45)
中央高校研究基金(15CX02047A
15CX05025A)
关键词
波达方向估计
加权子空间拟合算法
粒子群算法
计算复杂度
direction-of-arrival
weighted subspace fitting algorithm
particle swarm optimization
computational complexity
