By Topic

Application of neural networks to radar signal detection in K-distributed clutter

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 $33
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)
K. Cheikh ; Dept. d'Electronique, Univ. de Constantine ; F. Soltani

Radar signal detection is a complex task that is generally based on conventional statistical methods. In real applications, these methods require a lot of computing to estimate the clutter parameters and that they are optimal only for one type of clutter distribution. Recently, artificial neural networks (ANNs) have been used as a means of signal detection. Following on from this work, the authors consider the problem of radar signal detection using ANNs in a K-distributed environment. Two training algorithms are tested, namely, the back-propagation algorithm, and genetic algorithms for a multi-layer perceptron (MLP) architecture and also for the radial basis function architecture. The simulation results show that the MLP architecture outperforms the classical cell-averaging constant false alarm rate and order statistics constant false alarm rate detectors

Published in:

IEE Proceedings - Radar, Sonar and Navigation  (Volume:153 ,  Issue: 5 )