CGHA for principal component extraction in the complex domain

作     者：Zhang, YW Ma, YL 

作者机构：NORTHWESTERN POLYTECH UNIV COLL MARINE ENGN XIAN 710072 PEOPLES R CHINA 

出 版 物：《IEEE TRANSACTIONS ON NEURAL NETWORKS》 (IEEE Trans Neural Networks)

年 卷 期：1997年第8卷第5期

页      面：1031-1036页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 0808[工学-电气工程] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：complex domain convergence direction-of-arrival estimation generalized Hebbian algorithm neural network principal component single layer 

摘      要：Principal component extraction is an efficient statistical tool which is applied to data compression, feature extraction, signal processing, etc. Representative algorithms in the literature can only handle real data. However, in many scenarios such as sensor array signal processing, complex data are encountered. In this paper, the complex domain generalized Hebbian algorithm (CGHA) is presented for complex principal component extraction. It extends the real domain generalized Hebbian algorithm (GHA) proposed by Sanger. Convergence of CGHA is analyzed. Like GHA, CGHA can be implemented by a single-layer linear neural network with simple computation. An example is given where CGHA is utilized in direction-of-arrival (DOA) estimation of multiple narrowband plane waves received by a sensor array.