By Topic

A neural network approach to pulse radar detection

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
H. K. Kwan ; Dept. of Electr. Eng., Windsor Univ., Ont., Canada ; C. K. Lee

A multilayer feedforward neural network is applied to pulse compression. The 13-element Barker code and the maximum-length sequences (m-sequences) with lengths 15, 31, and 63 b were used as the signal codes, and four networks were implemented, respectively. In each of these networks, the number of input units was the same as the signal length while the number of hidden units was three and the number of output units was one. In training each of these networks, backpropagation learning was used and the number of training epochs was 500. Using this approach, a more than 40 dB output peak signal-to-sidelobe ratio can be achieved. These fault-tolerant neural networks can provide a robust means for pulse radar detection

Published in:

IEEE Transactions on Aerospace and Electronic Systems  (Volume:29 ,  Issue: 1 )