Localization in underwater sensor networks: survey and challenges
WUWNet '06 Proceedings of the 1st ACM international workshop on Underwater networks
Slocum Gliders: Robust and ready: Research Articles
Journal of Field Robotics - Special Issue on Underwater Robotics
Localization with Dive'N'Rise (DNR) beacons for underwater acoustic sensor networks
Proceedings of the second workshop on Underwater networks
AUV-Aided Localization for Underwater Sensor Networks
WASA '07 Proceedings of the International Conference on Wireless Algorithms,Systems and Applications
Motion-aware self-localization for underwater networks
Proceedings of the third ACM international workshop on Underwater Networks
Collaborative tracking in mobile underwater networks
Proceedings of the Fourth ACM International Workshop on UnderWater Networks
Task allocation for networked autonomous underwater vehicles in critical missions
IEEE Journal on Selected Areas in Communications
Cooperative AUV Navigation using a Single Maneuvering Surface Craft
International Journal of Robotics Research
Team formation and steering algorithms for underwater gliders using acoustic communications
Computer Communications
IEEE Journal on Selected Areas in Communications
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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.