Minimizing position uncertainty for under-ice autonomous underwater vehicles |
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| Authors: | Baozhi Chen Dario Pompili |
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| Affiliation: | 1. School of Electrical and Computer Engineering, National Technical University of Athens, Iroon Politechniou 9, 15773 Athens, Greece;2. Department of Electronics Engineering, Technological Education Institute of Piraeus, Petrou Ralli & Thivon 250, 12244 Aegaleo, Greece;1. Department of Computer Science, Sun Yat-sen University, Guangzhou, China;2. Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China;3. The Key Laboratory of Machine Intelligence and Sensor Networks, Ministry of Education, China;4. School of Computer, South China Normal University, Guangzhou, China;1. IMT Institute for Advanced Studies, Lucca, Italy;2. NVIDIA, Cambridge, UK;3. Aruba Webfarm S.r.l, Arezzo, Italy;4. University of Pisa, Pisa, Italy;1. Department of Electrical Engineering and Computer Science, Seoul National University, Seoul, Republic of Korea;2. Institute of Logistics Information Technology, Pusan National University, Pusan, Republic of Korea;1. School of Computer Science and Engineering, University of New South Wales, NSW 2052, Australia;2. School of Information Technologies, University of Sydney, NSW 2006, Australia |
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| Abstract: | Localization underwater has been known to be challenging due to the limited accessibility of the Global Positioning System (GPS) to obtain absolute positions. This becomes more severe in the under-ice environment since the ocean surface is covered with ice, making it more difficult to access GPS or to deploy localization infrastructure. In this paper, a novel solution that minimizes localization uncertainty and communication overhead of under-ice Autonomous Underwater Vehicles (AUVs) is proposed. Existing underwater localization solutions generally rely on reference nodes at ocean surface or on localization infrastructure to calculate positions, and they are not able to estimate the localization uncertainty, which may lead to the increase of localization error. In contrast, using the notion of external uncertainty (i.e., the position uncertainty as seen by others), our solution can characterize an AUV’s position with a probability model. This model is further used to estimate the uncertainty associated with our proposed localization techniques. Based on this uncertainty estimate, we further propose algorithms to minimize localization uncertainty and communication overhead. Our solution is emulated and compared against existing solutions, showing improved performance. |
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| Keywords: | Underwater acoustic sensor networks Autonomous underwater vehicle Localization Position uncertainty |
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