Underwater Low-speed Small Targets Classification Using Highlights and Trajectory Features

YANG Pan; LAI Kai; LIU Xionghou; WANG Bin; YANG Yixin

doi:10.11993/j.issn.2096-3920.2026-0054

Volume 34 Issue 3

Jun 2026

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Article Navigation > Journal of Unmanned Undersea Systems > 2026 > 34(3): 524-533

YANG Pan, LAI Kai, LIU Xionghou, WANG Bin, YANG Yixin. Underwater Low-speed Small Targets Classification Using Highlights and Trajectory Features[J]. Journal of Unmanned Undersea Systems, 2026, 34(3): 524-533. doi: 10.11993/j.issn.2096-3920.2026-0054

Citation:

YANG Pan, LAI Kai, LIU Xionghou, WANG Bin, YANG Yixin. Underwater Low-speed Small Targets Classification Using Highlights and Trajectory Features[J]. Journal of Unmanned Undersea Systems, 2026, 34(3): 524-533. doi: 10.11993/j.issn.2096-3920.2026-0054

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Underwater Low-speed Small Targets Classification Using Highlights and Trajectory Features

doi: 10.11993/j.issn.2096-3920.2026-0054

YANG Pan^{1, 2, 3, 4
,},
LAI Kai^{1, 3, 4
,},
LIU Xionghou^{1, 2, 3, 4
,
,},
WANG Bin⁵,
YANG Yixin⁶

1.
School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710129, China
2.
National Institute of Excellence in Engineering, Northwestern Polytechnical University, Xi’an 710129, China
3.
Shaanxi Key Laboratory of Underwater Information Technology, Xi’an 710072, China
4.
Hanjiang National Laboratory, Wuhan 430061, China
5.
School of Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
6.
School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China

Received Date: 2026-03-17
Accepted Date: 2026-05-11
Rev Recd Date: 2026-04-29

Available Online: 2026-05-18

Abstract

Abstract

Traditional statistical learning methods rely only on manually designed trajectory features with single feature representation and limited classification performance in the classification task of underwater low-speed small targets. To address this issue, this paper proposed a joint classification method that integrated range-dimension highlight features and tracking trajectory features. The proposed method first extracted range-dimension highlight features based on physical scattering characteristics from active sonar echoes to supplement static attribute information of the target, while simultaneously extracting trajectory features to describe the dynamic motion behavior of the target. It realized the complementarity of static and dynamic features and solved the defect of insufficient information of a single feature. On this basis, a statistical learning method suitable for small-sample conditions was adopted to construct the classifier, and the stability of the method was verified through Monte Carlo experiments. The results of field historical data samples and scenario-based simulation joint verification show that compared with traditional classification methods, the proposed trajectory-highlight joint feature classification method improves the average precision from 79.7% to 85.4%, the average recall from 84.4% to 89.1%, and the average F₁-score from 81.6% to 87.0%, effectively addressing the issue of insufficient classification performance for underwater low-speed small targets caused by the one-sided feature information in traditional methods and improving the classification capability for underwater low-speed small targets.
- underwater low-speed small target,
- feature extraction,
- classification and recognition,
- statistical learning

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References(17)

References

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