Infrasound Source Localization of Distributed Stations Using Sparse Bayesian Learning and Bayesian Information Fusion
The precise localization of the infrasound source is important for infrasound event monitoring. The localization of infrasound sources is influenced by the atmospheric propagation environment and infrasound measurement equipment in the large-scale global distribution of infrasound arrays. A distribu...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-07-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/13/3181 |
| _version_ | 1797442079997034496 |
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| author | Ran Wang Xiaoquan Yi Liang Yu Chenyu Zhang Tongdong Wang Xiaopeng Zhang |
| author_facet | Ran Wang Xiaoquan Yi Liang Yu Chenyu Zhang Tongdong Wang Xiaopeng Zhang |
| author_sort | Ran Wang |
| collection | DOAJ |
| description | The precise localization of the infrasound source is important for infrasound event monitoring. The localization of infrasound sources is influenced by the atmospheric propagation environment and infrasound measurement equipment in the large-scale global distribution of infrasound arrays. A distributed infrasound source localization method based on sparse Bayesian learning (SBL) and Bayesian information fusion is proposed to reduce the localization error. First, the arrival azimuth of the infrasound source is obtained based on the SBL algorithm. Then, the infrasound source localization result is obtained by the Bayesian information fusion algorithm. The localization error of the infrasound source can be reduced by this infrasound source method, which incorporates the uncertainty of the infrasound propagation environment and infrasound measurement equipment into the infrasound source localization results. The effectiveness of the proposed algorithm was validated using rocket motor explosion data from the Utah Test and Training Range (UTTR). The experimental results show that the arrival azimuth estimation error can be within 2° and the localization distance error is 3.5 km. |
| first_indexed | 2024-03-09T12:36:37Z |
| format | Article |
| id | doaj.art-1ced59be718043f5a1aaf33597dc75bb |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T12:36:37Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-1ced59be718043f5a1aaf33597dc75bb2023-11-30T22:23:38ZengMDPI AGRemote Sensing2072-42922022-07-011413318110.3390/rs14133181Infrasound Source Localization of Distributed Stations Using Sparse Bayesian Learning and Bayesian Information FusionRan Wang0Xiaoquan Yi1Liang Yu2Chenyu Zhang3Tongdong Wang4Xiaopeng Zhang5College of Logistics Engineering, Shanghai Maritime University, Shanghai 201306, ChinaCollege of Logistics Engineering, Shanghai Maritime University, Shanghai 201306, ChinaInstitute of Vibration, Shock and Noise, State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai 200240, ChinaCollege of Logistics Engineering, Shanghai Maritime University, Shanghai 201306, ChinaNorthwest Institute of Nuclear Technology, Xi’an 710024, ChinaNorthwest Institute of Nuclear Technology, Xi’an 710024, ChinaThe precise localization of the infrasound source is important for infrasound event monitoring. The localization of infrasound sources is influenced by the atmospheric propagation environment and infrasound measurement equipment in the large-scale global distribution of infrasound arrays. A distributed infrasound source localization method based on sparse Bayesian learning (SBL) and Bayesian information fusion is proposed to reduce the localization error. First, the arrival azimuth of the infrasound source is obtained based on the SBL algorithm. Then, the infrasound source localization result is obtained by the Bayesian information fusion algorithm. The localization error of the infrasound source can be reduced by this infrasound source method, which incorporates the uncertainty of the infrasound propagation environment and infrasound measurement equipment into the infrasound source localization results. The effectiveness of the proposed algorithm was validated using rocket motor explosion data from the Utah Test and Training Range (UTTR). The experimental results show that the arrival azimuth estimation error can be within 2° and the localization distance error is 3.5 km.https://www.mdpi.com/2072-4292/14/13/3181infrasound source localizationsparse Bayesian learningBayesian information fusion |
| spellingShingle | Ran Wang Xiaoquan Yi Liang Yu Chenyu Zhang Tongdong Wang Xiaopeng Zhang Infrasound Source Localization of Distributed Stations Using Sparse Bayesian Learning and Bayesian Information Fusion Remote Sensing infrasound source localization sparse Bayesian learning Bayesian information fusion |
| title | Infrasound Source Localization of Distributed Stations Using Sparse Bayesian Learning and Bayesian Information Fusion |
| title_full | Infrasound Source Localization of Distributed Stations Using Sparse Bayesian Learning and Bayesian Information Fusion |
| title_fullStr | Infrasound Source Localization of Distributed Stations Using Sparse Bayesian Learning and Bayesian Information Fusion |
| title_full_unstemmed | Infrasound Source Localization of Distributed Stations Using Sparse Bayesian Learning and Bayesian Information Fusion |
| title_short | Infrasound Source Localization of Distributed Stations Using Sparse Bayesian Learning and Bayesian Information Fusion |
| title_sort | infrasound source localization of distributed stations using sparse bayesian learning and bayesian information fusion |
| topic | infrasound source localization sparse Bayesian learning Bayesian information fusion |
| url | https://www.mdpi.com/2072-4292/14/13/3181 |
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