A Crossover Adjustment Method Considering the Beam Incident Angle for a Multibeam Bathymetric Survey Based on USV Swarms
Multibeam echosounder systems (MBESs) are widely used in unmanned surface vehicle swarms (USVs) to perform various marine bathymetry surveys because of their excellent performance. To address the challenges of systematic error superposition and edge beam error propagation in multibeam bathymetry sur...
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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/2077-1312/13/7/1364 |
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| author | Qiang Yuan Weiming Xu Shaohua Jin Tong Sun |
| author_facet | Qiang Yuan Weiming Xu Shaohua Jin Tong Sun |
| author_sort | Qiang Yuan |
| collection | DOAJ |
| description | Multibeam echosounder systems (MBESs) are widely used in unmanned surface vehicle swarms (USVs) to perform various marine bathymetry surveys because of their excellent performance. To address the challenges of systematic error superposition and edge beam error propagation in multibeam bathymetry surveying, this study proposes a novel error adjustment method integrating crossover error density clustering and beam incident angle (BIA) compensation. Firstly, a bathymetry error detection model was developed based on adaptive Density-Based Spatial Clustering of Applications with Noise (DBSCAN). By optimizing the neighborhood radius and minimum sample threshold through analyzing sliding-window curvature, the method achieved the automatic identification of outliers, reducing crossover discrepancies from ±150 m to ±50 m in the deep sea at a depth of approximately 5000 m. Secondly, an asymmetric quadratic surface correction model was established by incorporating the BIA as a key parameter. A dynamic weight matrix ω = 1/(1 + 0.5<i>θ</i><sup>2</sup>) was introduced to suppress edge beam errors, combined with Tikhonov regularization to resolve ill-posed matrix issues. Experimental validation in the Western Pacific demonstrated that the RMSE of crossover points decreased by about 30.4% and the MAE was reduced by 57.3%. The proposed method effectively corrects residual systematic errors while maintaining topographic authenticity, providing a reference for improving the quality of multibeam bathymetric data obtained via USVs and enhancing measurement efficiency. |
| format | Article |
| id | doaj-art-e931afbc284f4ba7b578ded2ee9f58f8 |
| 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-e931afbc284f4ba7b578ded2ee9f58f82025-08-20T03:07:58ZengMDPI AGJournal of Marine Science and Engineering2077-13122025-07-01137136410.3390/jmse13071364A Crossover Adjustment Method Considering the Beam Incident Angle for a Multibeam Bathymetric Survey Based on USV SwarmsQiang Yuan0Weiming Xu1Shaohua Jin2Tong Sun3Department of Oceanography and Hydrography, Dalian Naval Academy, Dalian 116018, ChinaDepartment of Oceanography and Hydrography, Dalian Naval Academy, Dalian 116018, ChinaDepartment of Oceanography and Hydrography, Dalian Naval Academy, Dalian 116018, ChinaDepartment of Oceanography and Hydrography, Dalian Naval Academy, Dalian 116018, ChinaMultibeam echosounder systems (MBESs) are widely used in unmanned surface vehicle swarms (USVs) to perform various marine bathymetry surveys because of their excellent performance. To address the challenges of systematic error superposition and edge beam error propagation in multibeam bathymetry surveying, this study proposes a novel error adjustment method integrating crossover error density clustering and beam incident angle (BIA) compensation. Firstly, a bathymetry error detection model was developed based on adaptive Density-Based Spatial Clustering of Applications with Noise (DBSCAN). By optimizing the neighborhood radius and minimum sample threshold through analyzing sliding-window curvature, the method achieved the automatic identification of outliers, reducing crossover discrepancies from ±150 m to ±50 m in the deep sea at a depth of approximately 5000 m. Secondly, an asymmetric quadratic surface correction model was established by incorporating the BIA as a key parameter. A dynamic weight matrix ω = 1/(1 + 0.5<i>θ</i><sup>2</sup>) was introduced to suppress edge beam errors, combined with Tikhonov regularization to resolve ill-posed matrix issues. Experimental validation in the Western Pacific demonstrated that the RMSE of crossover points decreased by about 30.4% and the MAE was reduced by 57.3%. The proposed method effectively corrects residual systematic errors while maintaining topographic authenticity, providing a reference for improving the quality of multibeam bathymetric data obtained via USVs and enhancing measurement efficiency.https://www.mdpi.com/2077-1312/13/7/1364unmanned surface vehicle swarms (USVs)multibeam echosounder systemcrossover adjustmentDBSCANweighted least squares |
| spellingShingle | Qiang Yuan Weiming Xu Shaohua Jin Tong Sun A Crossover Adjustment Method Considering the Beam Incident Angle for a Multibeam Bathymetric Survey Based on USV Swarms Journal of Marine Science and Engineering unmanned surface vehicle swarms (USVs) multibeam echosounder system crossover adjustment DBSCAN weighted least squares |
| title | A Crossover Adjustment Method Considering the Beam Incident Angle for a Multibeam Bathymetric Survey Based on USV Swarms |
| title_full | A Crossover Adjustment Method Considering the Beam Incident Angle for a Multibeam Bathymetric Survey Based on USV Swarms |
| title_fullStr | A Crossover Adjustment Method Considering the Beam Incident Angle for a Multibeam Bathymetric Survey Based on USV Swarms |
| title_full_unstemmed | A Crossover Adjustment Method Considering the Beam Incident Angle for a Multibeam Bathymetric Survey Based on USV Swarms |
| title_short | A Crossover Adjustment Method Considering the Beam Incident Angle for a Multibeam Bathymetric Survey Based on USV Swarms |
| title_sort | crossover adjustment method considering the beam incident angle for a multibeam bathymetric survey based on usv swarms |
| topic | unmanned surface vehicle swarms (USVs) multibeam echosounder system crossover adjustment DBSCAN weighted least squares |
| url | https://www.mdpi.com/2077-1312/13/7/1364 |
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