Ocean clutter suppression using one-class SVM

Yajuan Tang, Xiapu Luo, Zijie Yang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

11 Citations (Scopus)

Abstract

This paper presents a new algorithm based on pattern classification technique to suppress ocean clutter of high frequency ground wave radar (HFGWR)1. We first decompose radar returns as weighted sum of several chirp-like signals and then extract chirp rates and initial frequencies of these chirps to construct the feature space. The support region of ocean clutter in this feature space is calculated by one-class support vector machine (OCSVM). New data that falls inside this support region will be labelled as ocean clutter and thus will be discarded. We validate our method by conducting experiments on real radar data. The result shows that the selected features can discriminate between ocean clutter and target echo effectively and our classifier can suppress ocean clutter successfully by defining a fine support region of ocean clutter in that feature space.
Original languageEnglish
Title of host publicationMachine Learning for Signal Processing XIV - Proceedings of 2004 IEEE Signal Processing Society Workshop
Pages559-568
Number of pages10
Publication statusPublished - 1 Dec 2004
Externally publishedYes
EventMachine Learning for Signal Processing XIV - Proceedings of the 2004 IEEE Signal Processing Society Workshop - Sao Luis, Brazil
Duration: 29 Sep 20041 Oct 2004

Conference

ConferenceMachine Learning for Signal Processing XIV - Proceedings of the 2004 IEEE Signal Processing Society Workshop
CountryBrazil
CitySao Luis
Period29/09/041/10/04

ASJC Scopus subject areas

  • Engineering(all)

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