Cooperative Localization for Multi-AUVs Based on GM-PHD Filters and Information Entropy Theory
Cooperative localization (CL) is considered a promising method for underwater localization with respect to multiple autonomous underwater vehicles (multi-AUVs). In this paper, we proposed a CL algorithm based on information entropy theory and the probability hypothesis density (PHD) filter, aiming t...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2017-10-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/17/10/2286 |
| _version_ | 1828151462147915776 |
|---|---|
| author | Lichuan Zhang Tonghao Wang Feihu Zhang Demin Xu |
| author_facet | Lichuan Zhang Tonghao Wang Feihu Zhang Demin Xu |
| author_sort | Lichuan Zhang |
| collection | DOAJ |
| description | Cooperative localization (CL) is considered a promising method for underwater localization with respect to multiple autonomous underwater vehicles (multi-AUVs). In this paper, we proposed a CL algorithm based on information entropy theory and the probability hypothesis density (PHD) filter, aiming to enhance the global localization accuracy of the follower. In the proposed framework, the follower carries lower cost navigation systems, whereas the leaders carry better ones. Meanwhile, the leaders acquire the followers’ observations, including both measurements and clutter. Then, the PHD filters are utilized on the leaders and the results are communicated to the followers. The followers then perform weighted summation based on all received messages and obtain a final positioning result. Based on the information entropy theory and the PHD filter, the follower is able to acquire a precise knowledge of its position. |
| first_indexed | 2024-04-11T21:57:11Z |
| format | Article |
| id | doaj.art-ec7e5410d9604e2c8590b2692048afef |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T21:57:11Z |
| publishDate | 2017-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-ec7e5410d9604e2c8590b2692048afef2022-12-22T04:01:04ZengMDPI AGSensors1424-82202017-10-011710228610.3390/s17102286s17102286Cooperative Localization for Multi-AUVs Based on GM-PHD Filters and Information Entropy TheoryLichuan Zhang0Tonghao Wang1Feihu Zhang2Demin Xu3School of Marine Science and Technology, Northwestern Polytechnical University, 127 West Youyi Road, Xi’an 710072, ChinaSchool of Marine Science and Technology, Northwestern Polytechnical University, 127 West Youyi Road, Xi’an 710072, ChinaSchool of Marine Science and Technology, Northwestern Polytechnical University, 127 West Youyi Road, Xi’an 710072, ChinaSchool of Marine Science and Technology, Northwestern Polytechnical University, 127 West Youyi Road, Xi’an 710072, ChinaCooperative localization (CL) is considered a promising method for underwater localization with respect to multiple autonomous underwater vehicles (multi-AUVs). In this paper, we proposed a CL algorithm based on information entropy theory and the probability hypothesis density (PHD) filter, aiming to enhance the global localization accuracy of the follower. In the proposed framework, the follower carries lower cost navigation systems, whereas the leaders carry better ones. Meanwhile, the leaders acquire the followers’ observations, including both measurements and clutter. Then, the PHD filters are utilized on the leaders and the results are communicated to the followers. The followers then perform weighted summation based on all received messages and obtain a final positioning result. Based on the information entropy theory and the PHD filter, the follower is able to acquire a precise knowledge of its position.https://www.mdpi.com/1424-8220/17/10/2286cooperative localization (CL)multiple autonomous underwater vehicles (multi-AUVs)information entropyprobability hypothesis density (PHD) filter |
| spellingShingle | Lichuan Zhang Tonghao Wang Feihu Zhang Demin Xu Cooperative Localization for Multi-AUVs Based on GM-PHD Filters and Information Entropy Theory Sensors cooperative localization (CL) multiple autonomous underwater vehicles (multi-AUVs) information entropy probability hypothesis density (PHD) filter |
| title | Cooperative Localization for Multi-AUVs Based on GM-PHD Filters and Information Entropy Theory |
| title_full | Cooperative Localization for Multi-AUVs Based on GM-PHD Filters and Information Entropy Theory |
| title_fullStr | Cooperative Localization for Multi-AUVs Based on GM-PHD Filters and Information Entropy Theory |
| title_full_unstemmed | Cooperative Localization for Multi-AUVs Based on GM-PHD Filters and Information Entropy Theory |
| title_short | Cooperative Localization for Multi-AUVs Based on GM-PHD Filters and Information Entropy Theory |
| title_sort | cooperative localization for multi auvs based on gm phd filters and information entropy theory |
| topic | cooperative localization (CL) multiple autonomous underwater vehicles (multi-AUVs) information entropy probability hypothesis density (PHD) filter |
| url | https://www.mdpi.com/1424-8220/17/10/2286 |
| work_keys_str_mv | AT lichuanzhang cooperativelocalizationformultiauvsbasedongmphdfiltersandinformationentropytheory AT tonghaowang cooperativelocalizationformultiauvsbasedongmphdfiltersandinformationentropytheory AT feihuzhang cooperativelocalizationformultiauvsbasedongmphdfiltersandinformationentropytheory AT deminxu cooperativelocalizationformultiauvsbasedongmphdfiltersandinformationentropytheory |