By Topic

Investigation into the use of nonlinear predictor networks to improve the performance of maritime surveillance radar target detectors

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
$31 $31
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

3 Author(s)
Cowper, M.R. ; Dept. of Electron. & Electr. Eng., Edinburgh Univ., UK ; Mulgrew, B. ; Unsworth, C.P.

Previous research has claimed that sea clutter is a chaotic process with a nonlinear predictor function. Indeed, results have been reported which demonstrate that sea clutter is nonlinearly predictable, and that this predictability can be exploited, using nonlinear predictor networks, to improve the performance of maritime surveillance radars. The aim of the paper is to investigate if nonlinear predictor networks can be used to improve the performance of maritime surveillance radars, using sea clutter data sets provided by the Defence Evaluation and Research Agency (DERA). By presenting prediction results for radial basis function network predictors, Volterra series filter predictors, and linear predictors, it is shown that the clutter predictor functions are well approximated by a linear function, and that nonlinear predictor networks provide little or no improvement in performance. A novel and effective training methodology is used for the radial basis function network predictors

Published in:

Radar, Sonar and Navigation, IEE Proceedings -  (Volume:148 ,  Issue: 3 )