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Proceedings Paper

Neural network approach for direction of arrival estimation
Author(s): Ahmed H. El Zooghby; Christos G. Christodoulou; Michael Georgiopoulos
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Paper Abstract

The problem of Direction of Arrival (DOA) estimation of users in mobile communication systems using linear antenna arrays is addressed. Superresolution algorithms, such as Multiple Signal Classification (MUSIC), are used to locate desired as well as cochannel mobile users. However these algorithms require extensive computation and are difficult to implement in real-time. In this paper, the DOA problem is approached as a mapping problem which can be modeled using a suitable artificial neural network trained with input output pairs. A study of a three-layer Radial Basis Function Neural Network (RBFNN) which can learn multiple source direction finding with a six-element array is conducted. RBFNNs were used due to their ability for data interpolation in higher dimensions. The network weights are modified using the normalized cumulative delta rule. The performance of this network is compared to that of the MUSIC algorithm for both uncorrelated and corrected signals. It was found that networks implementing these functions were indeed successful in performing the required task and their performance approached that of the MUSIC algorithm. It is also shown that the RBFNN substantially reduced the CPU time for the DOA estimation computations.

Paper Details

Date Published: 4 April 1997
PDF: 10 pages
Proc. SPIE 3077, Applications and Science of Artificial Neural Networks III, (4 April 1997); doi: 10.1117/12.271519
Show Author Affiliations
Ahmed H. El Zooghby, Univ. of Central Florida (United States)
Christos G. Christodoulou, Univ. of Central Florida (United States)
Michael Georgiopoulos, Univ. of Central Florida (United States)

Published in SPIE Proceedings Vol. 3077:
Applications and Science of Artificial Neural Networks III
Steven K. Rogers, Editor(s)

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