Simultaneous localization and tracking via real-time nonparametric belief propagation

Target tracking in wireless sensor networks is traditionally achieved by localization and tracking (LAT), where the sensors are first localized, and in a later stage the target is tracked. This approach is sub-optimal since the sensor-target observations are not used to refine the position estimates of the sensors. In contrast, simultaneous localization and tracking (SLAT) uses these observations to track the target while simultaneously localizing the sensors. In this paper, we propose a novel centralized SLAT method based on real-time nonparametric belief propagation, which has nearly the same complexity and the same communication cost as LAT, and can provide both sensors´ and target´s estimated distributions in non-Gaussian form.