摘要
针对信号源方向时变情况,分析了样本协方差矩阵的更新,在此基础上提出了一种基于粒子群算法的跟踪方法。该方法直接利用性能优越的最大似然估计器,避免了子空间跟踪类方法需要不断重复的协方差矩阵分解;同时通过锁定目标、大幅度缩小搜索范围和运用群智能搜索,有效降低了算法的计算量。仿真结果表明,与子空间跟踪类算法相比,该方法具备解相干的能力和较好的跟踪精度,并且能够保证算法的实时性。
The update of covariance matrix of samples is studied, and a new method to estimate direction-of-arrival （DOA） for moving sources is proposed. Making use of the maximum likelihood algorithm, this method avoids the decomposition of the covariance matrix which arises repetitiously in subspace tracking types methods. And this method effectively reduces the computational load by locking the targets, reducing the range of search for targets and appling swarm intelligence technologies. Simulation results show that the DOA estimation based on the improved particle swarm algorithm has the ability to track coherent sources and is better for precision and real-time characteristics.
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2009年第9期2046-2049,共4页
Systems Engineering and Electronics
关键词
动态目标
波达方向估计
最大似然函数
粒子群算法
moving source
direction-of-arrival estimation
ML estimation
particle swarm algorithm
