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IEEE Transactions on Antennas and Propagation
Volume 48 Number 5, May 2000

Table of Contents for this issue

Complete paper in PDF format

A Neural Network-Based Smart Antenna for Multiple Source Tracking

Ahmed H. El Zooghby, Student Member, IEEE Christos G. Christodoulou, Senior Member, IEEE and Michael Georgiopoulos

Page 768.

Abstract:

This paper considers the problem of multiple-source tracking with neural network-based smart antennas for wireless terrestrial and satellite mobile communications. The neural multiple-source tracking (N-MUST) algorithm is based on an architecture of a family of radial basis function neural networks (RBFNN) to perform both detection and direction of arrival (DOA) estimation. The field of view of the antenna array is divided into spatial angular sectors, which are in turn assigned to a different pair of RBFNN's. When a network detects one or more sources in the first stage, the corresponding second stage network(s) are activated to perform the DOA estimation. Simulation results are performed to investigate the performance of the algorithm for various angular separations, with sources of random relative signal-to-noise ratio and when the system suffers from a doppler spread.

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