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Proceedings Paper

Smart adaptive array antennas for wireless communcations
Author(s): Christos G. Christodoulou; Michael Georgiopoulos
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Paper Abstract

This paper discusses an experimental neural network based smart antenna capable of performing direction finding and the necessary beamforming. The Radial Basis Function Neural Network (RBFNN) algorithm is used for both tasks and for multiple signals. The algorithm operates in two stages. The field of view of the antenna array is divided into spatial sectors, then each network is trained in the first stage to detect signals emanating from sources in that sector. According to the outputs of the first stage, one or more networks of the second stage can be activated so as to estimate the exact location of the sources. No a priori knowledge is required about the number of sources, and the networks can be designed to arbitrary angular resolution. Some experimental results are shown and compared with other algorithms, such as, the Fourier Transform and the MUSIC algorithm. The comparisons show the superior performance of the RBFNN and its ability to overcome many limitations of the conventional and other superresolution techniques, specifically by reducing the computational complexity and the ability to deal with a large number of sources.

Paper Details

Date Published: 28 August 2001
PDF: 9 pages
Proc. SPIE 4395, Digital Wireless Communication III, (28 August 2001); doi: 10.1117/12.438297
Show Author Affiliations
Christos G. Christodoulou, Univ. of New Mexico (United States)
Michael Georgiopoulos, Univ. of Central Florida (United States)

Published in SPIE Proceedings Vol. 4395:
Digital Wireless Communication III
Raghuveer M. Rao; Soheil A. Dianat; Michael D. Zoltowski, Editor(s)

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