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Please use this identifier to cite or link to this item: http://dspace.library.iitb.ac.in/jspui/handle/10054/8955

Title: Radial basis function neural network for pulse radar detection
Authors: KHAIRNAR, DG
MERCHANT, SN
DESAI, UB
Keywords: compression
Issue Date: 2007
Publisher: INST ENGINEERING TECHNOLOGY-IET
Citation: IET RADAR SONAR AND NAVIGATION, 1(1), 8-17
Abstract: A new approach using a radial basis function network (RBFN) for pulse compression is proposed. In the study, networks using 13-element Barker code, 35-clement Barker code and 21-bit optimal sequences have been implemented. In training these networks, the RIBFN-based learning algorithm was used. Simulation results show that RBFN approach has significant improvement in error convergence speed (very low training error), superior signal-to-sidelobe ratios, good noise rejection performance, improved misalignment performance, good range resolution ability and improved Doppler shift performance compared to other neural network approaches such as back-propagation, extended Kalman filter and autocorrelation function based learning algorithms. The proposed neural network approach provides a robust mean for pulse radar tracking.
URI: http://dx.doi.org/10.1049/iet-rsn:20050023
http://dspace.library.iitb.ac.in/xmlui/handle/10054/8955
http://hdl.handle.net/10054/8955
ISSN: 1751-8784
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