Dynamic Stochastic Model Optimization for Underwater Acoustic Navigation via Singular Value Decomposition
The geometric distribution of seabed beacons significantly impacts the positioning accuracy of underwater acoustic navigation systems. To address this challenge, we propose a depth-constrained adaptive stochastic model optimization method based on singular value decomposition (SVD). The method quant...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/13/7/1329 |
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| author | Jialu Li Junting Wang Tianhe Xu Jianxu Shu Yangfan Liu Yueyuan Ma Yangyin Xu |
| author_facet | Jialu Li Junting Wang Tianhe Xu Jianxu Shu Yangfan Liu Yueyuan Ma Yangyin Xu |
| author_sort | Jialu Li |
| collection | DOAJ |
| description | The geometric distribution of seabed beacons significantly impacts the positioning accuracy of underwater acoustic navigation systems. To address this challenge, we propose a depth-constrained adaptive stochastic model optimization method based on singular value decomposition (SVD). The method quantifies the contribution weights of each beacon to the dominant navigation direction by performing SVD on the acoustic observation matrix. The acoustic ranging covariance matrix can be dynamically adjusted based on these weights to suppress error propagation. At the same time, the prior depth with centimeter-level accuracy provided by the pressure sensor is used to establish strong constraints in the vertical direction. The experimental results demonstrate that the depth-constrained adaptive stochastic model optimization method reduces three-dimensional RMS errors by 66.65% (300 m depth) and 77.25% (2000 m depth) compared to conventional equal-weight models. Notably, the depth constraint alone achieves 95% vertical error suppression, while combined SVD optimization further enhances horizontal accuracy by 34.2–53.5%. These findings validate that coupling depth constraints with stochastic optimization effectively improves navigation accuracy in complex underwater environments. |
| format | Article |
| id | doaj-art-ba1ae5e7cc1843eea143f4f165f8b038 |
| institution | DOAJ |
| issn | 2077-1312 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-ba1ae5e7cc1843eea143f4f165f8b0382025-08-20T02:45:49ZengMDPI AGJournal of Marine Science and Engineering2077-13122025-07-01137132910.3390/jmse13071329Dynamic Stochastic Model Optimization for Underwater Acoustic Navigation via Singular Value DecompositionJialu Li0Junting Wang1Tianhe Xu2Jianxu Shu3Yangfan Liu4Yueyuan Ma5Yangyin Xu6School of Space Science and Technology, Shandong University, Weihai 264209, ChinaSchool of Space Science and Technology, Shandong University, Weihai 264209, ChinaSchool of Space Science and Technology, Shandong University, Weihai 264209, ChinaSchool of Space Science and Technology, Shandong University, Weihai 264209, ChinaSchool of Space Science and Technology, Shandong University, Weihai 264209, ChinaBeijing Institute of Tracking and Telecommunication Technology, Beijing 100081, ChinaXi’an Research Institute of Surveying and Mapping, Xi’an 710054, ChinaThe geometric distribution of seabed beacons significantly impacts the positioning accuracy of underwater acoustic navigation systems. To address this challenge, we propose a depth-constrained adaptive stochastic model optimization method based on singular value decomposition (SVD). The method quantifies the contribution weights of each beacon to the dominant navigation direction by performing SVD on the acoustic observation matrix. The acoustic ranging covariance matrix can be dynamically adjusted based on these weights to suppress error propagation. At the same time, the prior depth with centimeter-level accuracy provided by the pressure sensor is used to establish strong constraints in the vertical direction. The experimental results demonstrate that the depth-constrained adaptive stochastic model optimization method reduces three-dimensional RMS errors by 66.65% (300 m depth) and 77.25% (2000 m depth) compared to conventional equal-weight models. Notably, the depth constraint alone achieves 95% vertical error suppression, while combined SVD optimization further enhances horizontal accuracy by 34.2–53.5%. These findings validate that coupling depth constraints with stochastic optimization effectively improves navigation accuracy in complex underwater environments.https://www.mdpi.com/2077-1312/13/7/1329underwater acoustic navigationsingular value decomposition (SVD)stochastic model optimizationadaptive covariance estimationdepth constraintscombined acoustic/pressure sensor navigation |
| spellingShingle | Jialu Li Junting Wang Tianhe Xu Jianxu Shu Yangfan Liu Yueyuan Ma Yangyin Xu Dynamic Stochastic Model Optimization for Underwater Acoustic Navigation via Singular Value Decomposition Journal of Marine Science and Engineering underwater acoustic navigation singular value decomposition (SVD) stochastic model optimization adaptive covariance estimation depth constraints combined acoustic/pressure sensor navigation |
| title | Dynamic Stochastic Model Optimization for Underwater Acoustic Navigation via Singular Value Decomposition |
| title_full | Dynamic Stochastic Model Optimization for Underwater Acoustic Navigation via Singular Value Decomposition |
| title_fullStr | Dynamic Stochastic Model Optimization for Underwater Acoustic Navigation via Singular Value Decomposition |
| title_full_unstemmed | Dynamic Stochastic Model Optimization for Underwater Acoustic Navigation via Singular Value Decomposition |
| title_short | Dynamic Stochastic Model Optimization for Underwater Acoustic Navigation via Singular Value Decomposition |
| title_sort | dynamic stochastic model optimization for underwater acoustic navigation via singular value decomposition |
| topic | underwater acoustic navigation singular value decomposition (SVD) stochastic model optimization adaptive covariance estimation depth constraints combined acoustic/pressure sensor navigation |
| url | https://www.mdpi.com/2077-1312/13/7/1329 |
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