Title
Multiple sources neural network direction finding with arbitrary separations
Abstract
Interference rejection is very important and often represents an inexpensive way to increase the system capacity of cellular and mobile communication systems. This paper presents a modification to the radial basis function-based direction finding algorithm where the DOA problem is approached as a mapping which can be modeled by training the network with input output pairs with multiple angular separations. The network is then able to track a fixed number of sources with arbitrary angular separations using a linear array. A novel training technique is suggested and the performance of the RBFNN algorithm is compared to ideal data.
Publication Date
1-1-1998
Publication Title
1998 IEEE-APS Conference on Antennas and Propagation for Wireless Communications, APWC 1998
Volume
1998-November
Number of Pages
57-60
Document Type
Article; Proceedings Paper
Personal Identifier
scopus
DOI Link
https://doi.org/10.1109/APWC.1998.730646
Copyright Status
Unknown
Socpus ID
56349158303 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/56349158303
STARS Citation
El Zooghby, A. H.; Christodoulou, C. G.; and Georgiopoulos, M., "Multiple sources neural network direction finding with arbitrary separations" (1998). Scopus Export 1990s. 3208.
https://stars.library.ucf.edu/scopus1990/3208