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A Cooperative Dictionary Learning and Semi-supervised Learning Framework for Sea Clutter Suppression of HFSWR

  • Harbin Institute of Technology

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

High-frequency surface-wave radar (HFSWR) has been applied in searching targets and maritime surveillance systems. However, the sea clutter is usually strong and harmful for detecting the targets. In this paper, we explore the sea clutter suppression problem for HFSWR and propose a novel sea clutter suppression method named a cooperative dictionary learning and semi-supervised learning sea clutter suppression framework (CDLSL). The semi-supervised learning can obtain abundant needed sea clutter data for the subsequent dictionary learning. The dictionary learning has ability to capture the features of sea echo and provides a desired clutter estimation. We have applied the proposed framework in the actual HFSWR data. Significant improvements in sea clutter suppression performance are achieved by the proposed method with respect to the state-of-the-art method.

Original languageEnglish
Title of host publicationWireless and Satellite Systems - 12th EAI International Conference, WiSATS 2021, Proceedings
EditorsQing Guo, Weixiao Meng, Min Jia, Xue Wang
PublisherSpringer Science and Business Media Deutschland GmbH
Pages593-606
Number of pages14
ISBN (Print)9783030933975
DOIs
StatePublished - 2022
Event12th International Conference on Wireless and Satellite Services, WiSATS 2021 - Virtual, Online
Duration: 31 Jul 20212 Aug 2021

Publication series

NameLecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
Volume410 LNICST
ISSN (Print)1867-8211
ISSN (Electronic)1867-822X

Conference

Conference12th International Conference on Wireless and Satellite Services, WiSATS 2021
CityVirtual, Online
Period31/07/212/08/21

Keywords

  • Dictionary learning
  • High-frequency surface-wave radar
  • Sea clutter suppression
  • Semi-supervised learning

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