A machine learning approach to radar sea clutter suppression

D. Callaghan, J. Burger, Amit K. Mishra

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)

Abstract

The radar detection of small maritime targets requires special attention to the suppression of sea clutter returns that, in certain circumstances, may be difficult to discriminate from target returns. In this paper, the novel use of machine learning techniques is explored to address this familiar problem. A comparison of the results obtained using two machine learning techniques for the suppression of sea clutter is presented. Data for this experiment was gathered using an experimental S-band radar called NetRAD. This radar system was observing a coastal scene and the data collected was classified as either target or clutter using k-Nearest-Neighbour (kNN) and Support Vector Machine (SVM) algorithms. The results are presented as averaged probability of detection and probability of false alarm.

Original languageEnglish
Title of host publication2017 IEEE Radar Conference, RadarConf 2017
PublisherIEEE Press
Pages1222-1227
Number of pages6
ISBN (Electronic)9781467388238
DOIs
Publication statusPublished - 08 May 2017
Externally publishedYes
Event2017 IEEE Radar Conference, RadarConf 2017 - Seattle, United States of America
Duration: 08 May 201712 May 2017

Publication series

Name2017 IEEE Radar Conference, Radar Conf 2017

Conference

Conference2017 IEEE Radar Conference, RadarConf 2017
Country/TerritoryUnited States of America
CitySeattle
Period08 May 201712 May 2017

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