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

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 filter-based 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 low-power 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.