Target Detection Based on HSV Feature Fusion and Time-Frequency Analysis for HFSWR

被引：0

作者：

Li, Zongtai ^{[1
]}

Zhang, Xiaotong ^{[2
]}

Zhang, Ling ^{[1
]}

Niu, Jiong ^{[1
]}

Liu, Zhaokai ^{[3
]}

Wang, Cheng ^{[1
]}

Zhong, Jiangnan ^{[1
]}

机构：

[1] Ocean Univ China, Coll Engn, Qingdao, Peoples R China

[2] Qingdao Supply & Mkt Vocat Sch, Qingdao, Peoples R China

[3] Southern Univ Sci & Technologh, Dept Stat & Data Sci, Shenzhen, Peoples R China

来源：

OCEANS 2024 - SINGAPORE | 2024年

基金：

中国国家自然科学基金;

关键词：

High frequency surface wave radar (HFSWR); deep learning; target detection; feature fusion; Time-Frequency Analysis (TFA);

D O I：

10.1109/OCEANS51537.2024.10682364

中图分类号：

P75 [海洋工程];

学科分类号：

0814 ; 081505 ; 0824 ; 082401 ;

摘要：

High frequency surface wave radar (HFSWR) is capable of beyond-the-horizon surveillance of vessels in vast areas. However, the complex marine environment introduces various clutter and interference that are detrimental to the detection of HFSWR targets. In this paper, a target detection algorithm for sea clutter regions is proposed based on time-frequency analysis(TFA). The algorithm presents a multi-stage detection framework, initially identifying suspicious target positions in clutter through range-doppler (RD) spectrum analysis, followed by TFA of the echoes at these positions, feature fusion with manual features, and final classification using a deep neural network. Compared to classical target detection algorithms, this method exhibits superior detection performance in sea clutter regions.

引用

页数：5

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