3D Off-Grid Localization for Adjacent Cavitation Noise Sources Using Bayesian Inference
The propeller tip vortex cavitation (TVC) localization problem involves the separation of noise sources in proximity. This work describes a sparse localization method for off-grid cavitations to estimates their precise locations while keeping reasonable computational efficiency. It adopts two differ...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/5/2628 |
| _version_ | 1797614265166725120 |
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| author | Minseuk Park Sufyan Ali Memon Geunhwan Kim Youngmin Choo |
| author_facet | Minseuk Park Sufyan Ali Memon Geunhwan Kim Youngmin Choo |
| author_sort | Minseuk Park |
| collection | DOAJ |
| description | The propeller tip vortex cavitation (TVC) localization problem involves the separation of noise sources in proximity. This work describes a sparse localization method for off-grid cavitations to estimates their precise locations while keeping reasonable computational efficiency. It adopts two different grid (pairwise off-grid) sets with a moderate grid interval and provides redundant representations for adjacent noise sources. To estimate the position of the off-grid cavitations, a block-sparse Bayesian learning-based method is adopted for the pairwise off-grid scheme (pairwise off-grid BSBL), which iteratively updates the grid points using Bayesian inference. Subsequently, simulation and experimental results demonstrate that the proposed method achieves the separation of adjacent off-grid cavitations with reduced computational cost, while the other scheme suffers from a heavy computational burden; for the separation of adjacent off-grid cavitations, the pairwise off-grid BSBL took significantly less time (29 s) compared with the time taken by the conventional off-grid BSBL (2923 s). |
| first_indexed | 2024-03-11T07:10:08Z |
| format | Article |
| id | doaj.art-175ef8fe762f4b7a999c58a7fcb6f8f0 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T07:10:08Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-175ef8fe762f4b7a999c58a7fcb6f8f02023-11-17T08:37:25ZengMDPI AGSensors1424-82202023-02-01235262810.3390/s230526283D Off-Grid Localization for Adjacent Cavitation Noise Sources Using Bayesian InferenceMinseuk Park0Sufyan Ali Memon1Geunhwan Kim2Youngmin Choo3Department of Defense Systems Engineering, Sejong University, Seoul 05006, Republic of KoreaDepartment of Defense Systems Engineering, Sejong University, Seoul 05006, Republic of KoreaDepartment of Ocean Systems Engineering, Sejong University, Seoul 05006, Republic of KoreaDepartment of Defense Systems Engineering, Sejong University, Seoul 05006, Republic of KoreaThe propeller tip vortex cavitation (TVC) localization problem involves the separation of noise sources in proximity. This work describes a sparse localization method for off-grid cavitations to estimates their precise locations while keeping reasonable computational efficiency. It adopts two different grid (pairwise off-grid) sets with a moderate grid interval and provides redundant representations for adjacent noise sources. To estimate the position of the off-grid cavitations, a block-sparse Bayesian learning-based method is adopted for the pairwise off-grid scheme (pairwise off-grid BSBL), which iteratively updates the grid points using Bayesian inference. Subsequently, simulation and experimental results demonstrate that the proposed method achieves the separation of adjacent off-grid cavitations with reduced computational cost, while the other scheme suffers from a heavy computational burden; for the separation of adjacent off-grid cavitations, the pairwise off-grid BSBL took significantly less time (29 s) compared with the time taken by the conventional off-grid BSBL (2923 s).https://www.mdpi.com/1424-8220/23/5/2628incipient cavitationadjacent noise sourcessparse localizationoff-grid |
| spellingShingle | Minseuk Park Sufyan Ali Memon Geunhwan Kim Youngmin Choo 3D Off-Grid Localization for Adjacent Cavitation Noise Sources Using Bayesian Inference Sensors incipient cavitation adjacent noise sources sparse localization off-grid |
| title | 3D Off-Grid Localization for Adjacent Cavitation Noise Sources Using Bayesian Inference |
| title_full | 3D Off-Grid Localization for Adjacent Cavitation Noise Sources Using Bayesian Inference |
| title_fullStr | 3D Off-Grid Localization for Adjacent Cavitation Noise Sources Using Bayesian Inference |
| title_full_unstemmed | 3D Off-Grid Localization for Adjacent Cavitation Noise Sources Using Bayesian Inference |
| title_short | 3D Off-Grid Localization for Adjacent Cavitation Noise Sources Using Bayesian Inference |
| title_sort | 3d off grid localization for adjacent cavitation noise sources using bayesian inference |
| topic | incipient cavitation adjacent noise sources sparse localization off-grid |
| url | https://www.mdpi.com/1424-8220/23/5/2628 |
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