Scholarlink

<p>High-frequency surface wave radar (HFSWR) exploits high-frequency electromagnetic waves to achieve over-the-horizon maritime target detection and plays a vital role in marine resource monitoring, ocean environment sensing, and maritime security assurance. However, owing to the complex electromagnetic environment over the ocean, its target detection performance is often severely affected by various types of interferences. To address the challenging problem of target detection in sea clutter facing HFSWR, this study proposes a cascaded target detection algorithm. First, considering the complex regional morphology and significant shape variations of sea clutter in the range–doppler (RD) spectrum of the HFSWR, a lightweight multiscale feature fusion semantic segmentation method, termed multiscale lightweight SegNet (MSL-SegNet), is developed. This method effectively reduces the model parameters while maintaining high segmentation accuracy. Subsequently, for the candidate target point set obtained after cell averaging constant false alarm rate (CA-CFAR) detection and extreme learning machine (ELM) filtering, an adaptive parameter and anisotropic (APA)-DBSCAN was proposed. Experimental results based on shipborne HFSWR field data demonstrate that the proposed method achieves effective detection performance.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Multiscale feature and adaptive density-based detector for targets concealed in sea clutter of shipborne HFSWR

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月19日

查看全文
  • Zhongcheng Liu,
  • Haibo Yu,
  • Ling Zhang,
  • Hongdu Wang,
  • Jiong Niu,
  • Q. M. Jonathan Wu

摘要

High-frequency surface wave radar (HFSWR) exploits high-frequency electromagnetic waves to achieve over-the-horizon maritime target detection and plays a vital role in marine resource monitoring, ocean environment sensing, and maritime security assurance. However, owing to the complex electromagnetic environment over the ocean, its target detection performance is often severely affected by various types of interferences. To address the challenging problem of target detection in sea clutter facing HFSWR, this study proposes a cascaded target detection algorithm. First, considering the complex regional morphology and significant shape variations of sea clutter in the range–doppler (RD) spectrum of the HFSWR, a lightweight multiscale feature fusion semantic segmentation method, termed multiscale lightweight SegNet (MSL-SegNet), is developed. This method effectively reduces the model parameters while maintaining high segmentation accuracy. Subsequently, for the candidate target point set obtained after cell averaging constant false alarm rate (CA-CFAR) detection and extreme learning machine (ELM) filtering, an adaptive parameter and anisotropic (APA)-DBSCAN was proposed. Experimental results based on shipborne HFSWR field data demonstrate that the proposed method achieves effective detection performance.