基于粒子群优化算法的相干信源波达方向估计

利用粒子群优化算法和在解决优化问题的优势和广义极大似然测向的优点,提出了一种估计相干信源波达方向的新方法.对于所提出的测向算法,人射的信源可以是独立信源,也可以是多相干信源的混合,对阵列的几何结构也没有任何约束,而且它分辨的信源数还可以大于阵元数.为了有效地对所提出的测向代价函数进行拟合,把高斯异策略引进粒子群算法中,提出了一种可快速多维搜索的随机变异粒子群算法.仿真结果表明:与基于遗传算法的相干信源波达方向估计方法相比,基于粒子群优化算法的波达方向估计在收敛速度和估计精度上都有优势,有很好的可行性和有效性.

Estimating direction-of-arrival of coherent source based on particle swarm optimization algorithm

Using a particle swarm optimization algorithm and generalized maximum likelihood algorithm,a novel method to estimate direction-of-arrival(DOA) of a coherent source is proposed.For the proposed algorithm,incident sources may be a mixture of multi-clusters of coherent sources,the array's geometry is unrestricted,and more importantly,the number of sources resolved can be larger than the number of sensors.In order to realize precise fitting of the cost function,the mechanism of Gaussian mutation was considered in an original particle swarm optimization algorithm,and a modified particle swarm optimization algorithm for DOA was proposed.Simulation results show that DOA estimation of coherent sources based on a particle swarm optimization algorithm performs better than a genetic algorithm in aspects of convergence and estimation precision,and its efficacy and feasibility are proved by computer simulation.