Research Article
A Cooperative Dictionary Learning and Semi-supervised Learning Framework for Sea Clutter Suppression of HFSWR
@INPROCEEDINGS{10.1007/978-3-030-93398-2_53, author={Xiaowei Ji and Qiang Yang and Xiaochuan Wu and Xin Zhang}, title={A Cooperative Dictionary Learning and Semi-supervised Learning Framework for Sea Clutter Suppression of HFSWR}, proceedings={Wireless and Satellite Systems. 12th EAI International Conference, WiSATS 2021, Virtual Event, China, July 31 -- August 2, 2021, Proceedings}, proceedings_a={WISATS}, year={2022}, month={1}, keywords={Dictionary learning High-frequency surface-wave radar Sea clutter suppression Semi-supervised learning}, doi={10.1007/978-3-030-93398-2_53} }- Xiaowei Ji
Qiang Yang
Xiaochuan Wu
Xin Zhang
Year: 2022
A Cooperative Dictionary Learning and Semi-supervised Learning Framework for Sea Clutter Suppression of HFSWR
WISATS
Springer
DOI: 10.1007/978-3-030-93398-2_53
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.
