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24 June 1999 Multiple mobile user tracking with neural network-based adaptive array antennas
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Abstract
The problem of multiple source tracking with neural network- based adaptive array antennas for wireless terrestrial and satellite mobile communications is considered to this paper. The Neural Multiple Source Tracking algorithm which is based on an architecture of a family of radial basis function neural networks is introduced. In the first stage a number of RBFNNs are trained to perform the detection phase, while in the second state another set of networks is trained for the direction of arrival estimation phase. The field of view of the antenna array is divided into separate 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 state networks are activated to perform the direction of arrival estimation step. No prior knowledge of the number of present sources is required. Simulation results are performed to investigate the validity of the algorithm for various angular separations, with sources of random relative SNR and when the system suffers from frequency errors. The aforementioned approach results in substantial reduction of the computational complexity associated with the network training.
© (1999) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Ahmed H. El Zooghby, Christos G. Christodoulou, and Michael Georgiopoulos "Multiple mobile user tracking with neural network-based adaptive array antennas", Proc. SPIE 3708, Digital Wireless Communication, (24 June 1999); https://doi.org/10.1117/12.351221
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