Long baseline underwater source localization based on deep K-Means++ clustering in complex underwater environments
Long baseline localization relies on trilateration, with the least squares method being utilized to determine the unique position of underwater sources. However, it is highly sensitive to distance measurements from any of the reference beacons to the source. Furthermore, achieving continuous and sta...
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| Veröffentlicht in: | Digital signal processing Jg. 164; S. 105281 |
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| Abstract | Long baseline localization relies on trilateration, with the least squares method being utilized to determine the unique position of underwater sources. However, it is highly sensitive to distance measurements from any of the reference beacons to the source. Furthermore, achieving continuous and stable high-precision distance measurements presents a significant challenge in complex marine environments. To address this practical problem, this paper proposes a long baseline underwater source localization method based on deep K-Means++ clustering. A three-layer stack denoising autoencoder (SDA) was utilized to extract the features of the trilateration results. Subsequently, the K-Means++ algorithm was utilized to conduct multi-source location cluster analysis and fine-tuning. The experimental results demonstrate that, compared to the existing Kmeans-based long baseline localization method, the proposed approach demonstrates a modest enhancement in localization accuracy. Concurrently, it has led to an average increase of 7 percentage points in normalized mutual information (NMI), a rise of 8 percentage points in the adjusted Rand index (ARI), and an improvement of 2.5 percentage points in clustering accuracy (ACC). This not only ensures the stability and robustness of accuracy against ocean noise in long baseline mode but also enhances the operational efficiency and versatility of clustering methods applied within this field. |
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| AbstractList | Long baseline localization relies on trilateration, with the least squares method being utilized to determine the unique position of underwater sources. However, it is highly sensitive to distance measurements from any of the reference beacons to the source. Furthermore, achieving continuous and stable high-precision distance measurements presents a significant challenge in complex marine environments. To address this practical problem, this paper proposes a long baseline underwater source localization method based on deep K-Means++ clustering. A three-layer stack denoising autoencoder (SDA) was utilized to extract the features of the trilateration results. Subsequently, the K-Means++ algorithm was utilized to conduct multi-source location cluster analysis and fine-tuning. The experimental results demonstrate that, compared to the existing Kmeans-based long baseline localization method, the proposed approach demonstrates a modest enhancement in localization accuracy. Concurrently, it has led to an average increase of 7 percentage points in normalized mutual information (NMI), a rise of 8 percentage points in the adjusted Rand index (ARI), and an improvement of 2.5 percentage points in clustering accuracy (ACC). This not only ensures the stability and robustness of accuracy against ocean noise in long baseline mode but also enhances the operational efficiency and versatility of clustering methods applied within this field. |
| ArticleNumber | 105281 |
| Author | Dai, Yawen Cao, Yifei Yang, Lei |
| Author_xml | – sequence: 1 givenname: Yawen surname: Dai fullname: Dai, Yawen organization: School of Network Engineering, Wuhu Institute of Technology, Wuhu, 241000, People's Republic of China – sequence: 2 givenname: Lei orcidid: 0009-0009-1248-3891 surname: Yang fullname: Yang, Lei email: yl@ahpu.edu.cn organization: Reconfigurable and Intelligent Computing Laboratory, School of Computer and Information, Anhui Polytechnic University, Wuhu, 241000, People's Republic of China – sequence: 3 givenname: Yifei surname: Cao fullname: Cao, Yifei organization: School of Electrical Engineering, Anhui Polytechnic University, Wuhu, 241000, People's Republic of China |
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| Cites_doi | 10.1109/TCST.2013.2293958 10.1016/j.measurement.2022.111155 10.1121/10.0023619 10.1016/j.oceaneng.2024.117114 10.1109/TCST.2015.2429613 10.1016/j.apacoust.2016.04.009 10.1109/TVT.2023.3270931 10.3390/s22166085 10.1109/TRO.2007.895057 10.1109/TSP.2024.3378376 10.1121/10.0000972 10.1109/TPDS.2014.2306193 10.1016/j.patcog.2011.11.017 10.3390/jmse12050825 10.1109/JSEN.2020.2980966 10.1016/j.oceaneng.2024.116753 10.1109/JSEN.2022.3232614 10.1080/00207543.2025.2539312 10.1121/10.0010260 10.1016/j.apacoust.2014.06.002 10.1109/TSMC.2019.2895499 10.1109/JPROC.2018.2823638 10.1109/JOE.2017.2731061 10.5772/63246 10.1016/j.apacoust.2021.108503 |
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| Keywords | Long baseline Underwater source localization Deep K-Means++ clustering Stack denoising autoencoder |
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| Title | Long baseline underwater source localization based on deep K-Means++ clustering in complex underwater environments |
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