基于SCAGOA优化BP神经网络和极大似然算法的DOA估计研究

Research on DOA Estimation Based on SCAGOA Optimized BP Neural Network and Maximum Likelihood Algorithm

利用BP神经网络和极大似然(ML)算法对阵列信号波达方向(DOA)进行估计,结合了混沌映射和群智能优化算法的优势,设计了一种正余混沌双弦蝗虫优化算法(SCAGOA),不仅解决了因神经网络的权值和阈值选取不当导致陷入局部最优的问题,而且解决了ML算法中多维搜索导致运算负荷大、效率低的问题.通过仿真实验对双信号源的输出效果和估计误差进行讨论,比较了不同优化算法对信噪比的泛化能力.结果表明,通过SCAGOA优化后的BP神经网络和ML算法在DOA估计方面比其他优化算法具有更好的估计精度.

The direction of arrival(DOA)of array signal is estimated by using BP neural network and maximum likelihood estimation(ML).We combined the advantages of chaos mapping and swarm intelligence optimization algorithm to design a sines cosine chaotic double string grasshopper optimization algorithm(SCAGOA).On the one hand,it can solve the problem of falling into local optimum due to improper selection of weights and thresholds of neural network;on the other hand,it can solve the problem of computational load and low efficiency caused by multidimensional search in maximum likelihood.The output effect and estimation error of two signal sources were discussed through simulation experiments.The generalization ability of different optimization algorithms to SNR were compared.The results show that the optimized BP neural network and maximum likelihood method have better estimation accuracy than other optimization algorithms in DOA estimation.