Paper
28 August 2001 Smart adaptive array antennas for wireless communcations
Christos G. Christodoulou, Michael Georgiopoulos
Author Affiliations +
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.
© (2001) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Christos G. Christodoulou and Michael Georgiopoulos "Smart adaptive array antennas for wireless communcations", Proc. SPIE 4395, Digital Wireless Communication III, (28 August 2001); https://doi.org/10.1117/12.438297
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CITATIONS
Cited by 4 scholarly publications.
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KEYWORDS
Antennas

Neural networks

Signal detection

Detection and tracking algorithms

Evolutionary algorithms

Signal processing

Spatial resolution

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