Efficient GPS Scheduling in Wildlife Tags using an Extended Kalman Filter-based Uncertainty Suppression Strategy

Max Lichtenstein and Gabriel Elkaim

Peer Reviewed

Abstract:	Animal-mounted GPS-based location tracking has become a core tool for wildlife ecologists. However, the lifetimes of animal-mounted devices (or “tags”) are typically limited by battery life, and GPS tracking occupies a large portion of their energy budgets. In this paper, we propose several Kalman filterbased algorithms that reduce the GPS duty cycle while still maintaining a threshold of tracking accuracy, and test these algorithms using a human subject. These algorithms leverage lowpower accelerometry measurements to estimate the uncertainty in the tag’s location, then schedule GPS measurements to suppress that uncertainty. We show that these strategies can reduce average GPS uptime, though at the cost of fidelity in some cases.
Published in:	2020 IEEE/ION Position, Location and Navigation Symposium (PLANS) April 20 - 23, 2020 Hilton Portland Downtown Portland, Oregon
Pages:	1472 - 1475
Cite this article:	Lichtenstein, Max, Elkaim, Gabriel, "Efficient GPS Scheduling in Wildlife Tags using an Extended Kalman Filter-based Uncertainty Suppression Strategy," 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS), Portland, Oregon, April 2020, pp. 1472-1475.
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