基于约束的单纯形-粒子群混合优化DEUCE测向算法

DEUCE Direction Finding Algorithm Based on Restrained Simplex Particle Swarm Optimization Method

文章介绍一种新的基于特征结构的DOA估计算法,并对算法中非线性多维搜索问题限制算法应用这个缺点提出了一种基于约束的单纯形-粒子群混合优化DEUCE测向算法,改进算法把罚函数方法、粒子群算法、单纯形算法有机结合并应用到DEUCE算法中去;仿真结果表明改进算法在保持低收敛门限和高估计精度的优点下,具有更快的运算速度。

In this paper, a novel eigenstructure-based method for direction estimation is presented. However, multi-dimensional optimizing restricts the broad application. Therefore, the DEUCE direction finding algorithm based on restrained simplex particle swarm optimization method is presented in order to solve this disadvantage. We unify the penalty function, particle swarm optimization algorithm, simplex algorithm to obtain the DOA estimation. Simulation shows that the proposed algorithm can provide accurate estimation efficiently, with lower resolution threshold.