A Meta-Learning-Based Recognition Method for Multidimensional Feature Extraction and Fusion of Underwater Targets
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| Publicado en: | Applied Sciences vol. 15, no. 10 (2025), p. 5744 |
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| Autor principal: | |
| Otros Autores: | , , , , , |
| Publicado: |
MDPI AG
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| Acceso en línea: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 003 | UK-CbPIL | ||
| 022 | |a 2076-3417 | ||
| 024 | 7 | |a 10.3390/app15105744 |2 doi | |
| 035 | |a 3211859808 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231338 |2 nlm | ||
| 100 | 1 | |a Liu, Xiaochun |u Xi’an Precision Machinery Research Institute, Xi’an 710077, China; xiaochunliu@mail.nwpu.edu.cn (X.L.); seasonsleo@163.com (L.L.); | |
| 245 | 1 | |a A Meta-Learning-Based Recognition Method for Multidimensional Feature Extraction and Fusion of Underwater Targets | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a To tackle the challenges of relative attitude adaptability and limited sample availability in underwater moving target recognition for active sonar, this study focuses on key aspects such as feature extraction, network model design, and information fusion. A pseudo-three-dimensional spatial feature extraction method is proposed by integrating generalized MUSIC with range–dimension information. The pseudo-WVD time–frequency feature is enhanced through the incorporation of prior knowledge. Additionally, the Doppler frequency shift distribution feature for underwater moving targets is derived and extracted. A multidimensional feature information fusion network model based on meta-learning is developed. Meta-knowledge is extracted separately from spatial, time–frequency, and Doppler feature spectra, to improve the generalization capability of single-feature task networks during small-sample training. Multidimensional feature information fusion is achieved via a feature fusion classifier. Finally, a sample library is constructed using simulation-enhanced data and experimental data for network training and testing. The results demonstrate that, in the few-sample scenario, the proposed method leverages the complementary nature of multidimensional features, effectively addressing the challenge of limited adaptability to relative horizontal orientation angles in target recognition, and achieving a recognition accuracy of up to 97.1%. | |
| 651 | 4 | |a United States--US | |
| 653 | |a Simulation | ||
| 653 | |a Methods | ||
| 653 | |a Eigenvalues | ||
| 653 | |a Algorithms | ||
| 653 | |a Adaptability | ||
| 653 | |a Attitudes | ||
| 653 | |a Target recognition | ||
| 653 | |a Parameter estimation | ||
| 700 | 1 | |a Yang, Yunchuan |u Xi’an Precision Machinery Research Institute, Xi’an 710077, China; xiaochunliu@mail.nwpu.edu.cn (X.L.); seasonsleo@163.com (L.L.); | |
| 700 | 1 | |a Hu Youfeng |u Xi’an Precision Machinery Research Institute, Xi’an 710077, China; xiaochunliu@mail.nwpu.edu.cn (X.L.); seasonsleo@163.com (L.L.); | |
| 700 | 1 | |a Yang, Xiangfeng |u Xi’an Precision Machinery Research Institute, Xi’an 710077, China; xiaochunliu@mail.nwpu.edu.cn (X.L.); seasonsleo@163.com (L.L.); | |
| 700 | 1 | |a Liu, Liwen |u Xi’an Precision Machinery Research Institute, Xi’an 710077, China; xiaochunliu@mail.nwpu.edu.cn (X.L.); seasonsleo@163.com (L.L.); | |
| 700 | 1 | |a Shi, Lei |u Xi’an Precision Machinery Research Institute, Xi’an 710077, China; xiaochunliu@mail.nwpu.edu.cn (X.L.); seasonsleo@163.com (L.L.); | |
| 700 | 1 | |a Liu, Jianguo |u School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China; liujianguo@nwpu.edu.cn | |
| 773 | 0 | |t Applied Sciences |g vol. 15, no. 10 (2025), p. 5744 | |
| 786 | 0 | |d ProQuest |t Publicly Available Content Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3211859808/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3211859808/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3211859808/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |