Space-Time Frequency Feature Fusion Recognition Method for Underwater Targets Based on Meta-Learning

LIU Xiaochun; YANG Yunchuan; HU Youfeng; WANG Chenyu; LI Yongsheng

doi:10.11993/j.issn.2096-3920.2025-0067

Volume 34 Issue 1

Feb 2026

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Article Navigation > Journal of Unmanned Undersea Systems > 2026 > 34(1): 85-93

LIU Xiaochun, YANG Yunchuan, HU Youfeng, WANG Chenyu, LI Yongsheng. Space-Time Frequency Feature Fusion Recognition Method for Underwater Targets Based on Meta-Learning[J]. Journal of Unmanned Undersea Systems, 2026, 34(1): 85-93. doi: 10.11993/j.issn.2096-3920.2025-0067

Citation:

LIU Xiaochun, YANG Yunchuan, HU Youfeng, WANG Chenyu, LI Yongsheng. Space-Time Frequency Feature Fusion Recognition Method for Underwater Targets Based on Meta-Learning[J]. Journal of Unmanned Undersea Systems, 2026, 34(1): 85-93. doi: 10.11993/j.issn.2096-3920.2025-0067

Citation:

LIU Xiaochun, YANG Yunchuan, HU Youfeng, WANG Chenyu, LI Yongsheng. Space-Time Frequency Feature Fusion Recognition Method for Underwater Targets Based on Meta-Learning[J]. Journal of Unmanned Undersea Systems, 2026, 34(1): 85-93. doi: 10.11993/j.issn.2096-3920.2025-0067

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Space-Time Frequency Feature Fusion Recognition Method for Underwater Targets Based on Meta-Learning

doi: 10.11993/j.issn.2096-3920.2025-0067

The 705 Research Institute, China Shipbuilding Industry Corporation Limited, Xi’an 710077, China

Received Date: 2025-05-16
Accepted Date: 2025-06-13
Rev Recd Date: 2025-06-13

Available Online: 2025-09-12

Abstract

Abstract

To improve poor relative bearing adaptability and weak resistance to new types of interference in active sonar target recognition, this paper elaborated on the physical mechanism of active sonar target information perception from wave equation theory. Based on generalized multiple signal classification (MUSIC) spatial spectrum estimation, a novel method was proposed for acquiring the pseudo three-dimensional spatial feature of underwater targets by incorporating distance information, thereby effectively enhancing the adaptability of spatial features across different relative bearings. Additionally, research was conducted on methods to enhance pseudo Wigner-Ville distribution (PWVD) time frequency features and Doppler frequency shift distribution feature extraction of moving targets based on the two-dimensional correlation of time frequency. By leveraging the complementary advantages of both algorithms, the relative bearing adaptability for target recognition was further improved. To address the challenge of scarce and imbalanced underwater target sample distribution, the concept of meta-learning was integrated to construct a data-level fusion target recognition network that incorporated spatial, time-frequency, and Doppler domain features. The network was trained and tested using simulation and experimental data. The results demonstrate that the space-time frequency fusion features significantly improve the relative bearing adaptability and anti-interference capability for target recognition, providing a novel approach for the development of intelligent underwater target recognition technology.
- underwater target,
- active sonar; target recognition,
- space-time frequency feature fusion,
- multi-dimensional feature extraction,
- meta-learning

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References(26)

References

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