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 has also been developed.<>
Improving performance in pulse radar detection using neural networks
IEEE Transactions on Aerospace and Electronic Systems ; 31 , 3 ; 1193-1198
1995-07-01
399277 byte
Article (Journal)
Electronic Resource
English
Improving Performance in Pulse Radar Detection Using Neural Networks
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