Single-Beam Sonar Small Target Recognition Algorithm Based on Underwater Unmanned Platform

XU Linpeng; MA Jingwen; QU Guorui; DU Weidong; ZHOU Tian; YU Xiaoyang

doi:10.11993/j.issn.2096-3920.2026-0061

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XU Linpeng, MA Jingwen, QU Guorui, DU Weidong, ZHOU Tian, YU Xiaoyang. Single-Beam Sonar Small Target Recognition Algorithm Based on Underwater Unmanned Platform[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2026-0061

Citation:

XU Linpeng, MA Jingwen, QU Guorui, DU Weidong, ZHOU Tian, YU Xiaoyang. Single-Beam Sonar Small Target Recognition Algorithm Based on Underwater Unmanned Platform[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2026-0061

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Single-Beam Sonar Small Target Recognition Algorithm Based on Underwater Unmanned Platform

doi: 10.11993/j.issn.2096-3920.2026-0061

XU Linpeng^{1, 2, 3
,},
MA Jingwen^{1, 2, 3
,},
QU Guorui^{1, 2, 3
,},
DU Weidong^{1, 2, 3
,},
ZHOU Tian^{1, 2, 3
,},
YU Xiaoyang^{1, 2, 3
,
,}

1.
National Key Laboratory of Underwater Acoustic Technology, Harbin Engineering University, Harbin 150001, China
2.
Key Laboratory of Marine Information Acquisition and Security (Harbin Engineering University), Ministry of Industry and Information Technology, Harbin 150001, China
3.
College of Underwater Acoustic Engineering, Harbin Engineering University, Harbin 150001, China

Received Date: 2026-03-27
Accepted Date: 2026-05-11
Rev Recd Date: 2026-05-02

Available Online: 2026-05-19

Abstract

Abstract

Aiming at the difficulty of small target recognition caused by the limited payload capacity of underwater unmanned platforms and the scarcity of sonar data samples, this paper proposes a single-beam sonar signal target recognition algorithm adapted to the few-shot condition of underwater unmanned platforms. Based on the single-beam echo signal of active sonar targets, the algorithm realizes high-accuracy target recognition under the few-shot condition by extracting multi-dimensional time-frequency features of the signal, optimizing feature selection via correlation analysis and PCA dimensionality reduction, and integrating the random forest classifier. Test results on water tank experimental data show that, compared with various methods combining multi-beam sonar images with deep learning, the proposed algorithm achieves performance indicators of 99.42% precision, 99.39% recall, 99.39% F1-score, and 99.39% accuracy with a smaller training set. The proposed method has the advantages of low computational cost, fast running speed and strong interpretability, making it more suitable for deployment on underwater unmanned platforms with limited computing and storage resources. It provides an efficient and feasible scheme for small target recognition by underwater unmanned platforms under resource-constrained conditions.
- sonar signal processing,
- small target recognition,
- feature selection,
- machine learning,
- few-shot

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References(15)

References

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