A novel wideband direction-of-arrival estimation algorithm based on improved GRNN and PCA

被引：0

作者：

Zhang, Zhen-Kai ^{[1
,2
]}

Tian, Yu-Bo ^{[1
]}

Zhou, Jian-Jiang ^{[2
]}

机构：

[1] Department of Electronic Information, Jiangsu University of Science and Technology, Zhenjiang 212003, China

[2] Department of Electronic Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China

来源：

Guangdianzi Jiguang/Journal of Optoelectronics Laser | 2012年 / 23卷 / 04期

关键词：

Direction of arrival - Neural networks - Particle swarm optimization (PSO) - Focusing;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

A novel wideband direction-of-arrival estimation algorithm based on improved generalized regression neural network (IGRNN)and principal component analysis (PCA) is proposed in order to obtain high estimation precision. Firstly, the PCA method is used to lower the dimension of train samples in order to reduce the complexity of GRNN whose parameters are optimized by particle swarm optimization algorithm. At the same time, accurate and rough estimation models are established respectively according to different focusing angles. The accurate model is used for DOA estimation after rough estimation to avoide the influence of focusing angles. Simulation results show that our algorithm has high estimation precision and efficiency.

引用

页码：692 / 696