Adaptive Beamforming using Complex-valued Radial Basis Function Neural Networks

被引：0

作者：

Savitha, R. ^{[1
]}

Vigneswaran, S. ^{[2
]}

Suresh, S. ^{[3
]}

Sundararajan, N. ^{[1
]}

机构：

[1] Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore, Singapore

[2] Continental AG, Singapore, Singapore

[3] Indian Inst Technol, Sch Elect Engn, New Delhi, India

来源：

TENCON 2009 - 2009 IEEE REGION 10 CONFERENCE, VOLS 1-4 | 2009年

关键词：

STRUCTURED GRADIENT ALGORITHM; ANTENNA-ARRAYS; CHANNEL EQUALIZATION; LEARNING ALGORITHM;

D O I：

暂无

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Beamforming is an array signal processing problem of forming a beam pattern of an array of sensors. In doing so, beams are directed to the desired direction (beam-pointing) and the nulls are directed to interference direction (null-steering). In this paper, the performance of beamforming using the Fully Complex-valued RBF network (FC-RBF) with the fully complex-valued activation function is compared with the performance of the existing complex-valued RBF neural networks. It was observed that the FC-RBF network performed better than the other complex-valued RBF networks in suppressing the nulls and steering beams, as desired. The learning speed of the FC-RBF network was also faster than the Complex-valued Radial Basis Function network. Comparison of these performances with the optimum Matrix method showed that the beam-pattern of the FC-RBF beamformer was closer to the beampattern of the matrix method.

引用

页码：1133 / +

页数：2

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