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Sensors 2017, 17(10), 2286; https://doi.org/10.3390/s17102286

Cooperative Localization for Multi-AUVs Based on GM-PHD Filters and Information Entropy Theory

School of Marine Science and Technology, Northwestern Polytechnical University, 127 West Youyi Road, Xi’an 710072, China
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Received: 7 August 2017 / Revised: 2 October 2017 / Accepted: 3 October 2017 / Published: 8 October 2017
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Abstract

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. View Full-Text
Keywords: cooperative localization (CL); multiple autonomous underwater vehicles (multi-AUVs); information entropy; probability hypothesis density (PHD) filter cooperative localization (CL); multiple autonomous underwater vehicles (multi-AUVs); information entropy; probability hypothesis density (PHD) filter
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Zhang, L.; Wang, T.; Zhang, F.; Xu, D. Cooperative Localization for Multi-AUVs Based on GM-PHD Filters and Information Entropy Theory. Sensors 2017, 17, 2286.

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