Performance of Neural Network Trained with Genetic Algorithm for Direction of Arrival Estimation

Author

Pour, Hamed Movahedi ; Atlasbaf, Zahra ; Hakkak, Mohammad

Author_Institution

Tarbiat Modares Univ., Tehran

fYear

2006

fDate

17-20 Sept. 2006

Firstpage

197

Lastpage

202

Abstract

Direction of Arrival (DOA) estimation has turned out to be extremely vital by reason of recent developments in Spatial Division Multiple Access (SDMA) systems. Superresolution algorithms such as the Multiple Signal Classification (MUSIC) and neural networks have been approached to carry out DOA estimation. In this paper, a Multi-Layer Perceptron (MLP) network using Genetic Algorithm (GA) method for training is proposed. The performance of the proposed network is compared with Radial Basis Function Neural Network (RBFNN) which has been considered as an effective solution to the DOA problem. It is demonstrated that by exploiting the genetic algorithm based MLP, error attributes of the estimation improve, despite the reduction of neural network size.

Keywords

direction-of-arrival estimation; genetic algorithms; multilayer perceptrons; radial basis function networks; signal classification; direction of arrival estimation; genetic algorithm; multilayer perceptron network; multiple signal classification; radial basis function neural network; spatial division multiple access systems; Antenna arrays; Direction of arrival estimation; Directive antennas; Genetic algorithms; Interference; Multiaccess communication; Multiple signal classification; Neural networks; Radial basis function networks; Spatial resolution; Direction of Arrival; Genetic Algorithm; Radial Basis Function Neural Network;

fLanguage

English

Publisher

ieee

Conference_Titel

Mobile Computing and Wireless Communication International Conference, 2006. MCWC 2006. Proceedings of the First

Conference_Location

Amman

Print_ISBN

978-9957-486-00-6

Type

conf

DOI

10.1109/MCWC.2006.4375221

Filename

4375221