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Please use this identifier to cite or link to this item: http://dspace.library.iitb.ac.in/jspui/handle/100/285

Title: Improving performance in pulse radar detection using neural networks
Authors: RAO, KD
SRIDHAR, G
Issue Date: 1995
Publisher: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation: IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS,31(3)1193-1198
Abstract: A new approach using a multilayered feed forward neural network for pulse compression is presented. The 13 element Barker code was used as the signal code. In training this network, the extended Kalman filtering (EKF)-based learning algorithm which has faster convergence speed than the conventional backpropagation (BP) algorithm was used. This approach has yielded output peak signal to sidelobe ratios which are much superior to those obtained with the BP algorithm. Further, for use of this neural network for real time processing, parallel implementation of the EKF-based learning algorithm is indispensable. Therefore, parallel implementation of the EKF-based learning algorithm on a network of three transputers also has been developed.
URI: http://dx.doi.org/10.1109/7.395219
http://dspace.library.iitb.ac.in/xmlui/handle/10054/14069
http://hdl.handle.net/100/285
ISSN: 0018-9251
Appears in Collections:Letter

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