A comparison of Kalman-based schemes for localization and tracking in sensor systems

The challenge of target tracking is one of the most important applications of WSNs (Wireless Sensor Networks). Traditionally, Kalman filter and its derivatives are some of the most popular algorithms for solving the signal tracking problem. In a WSNs tracking application, the target motion and state update dynamics might be modelled by linear or non-linear structures depending on the specific scenario. This paper compares extended Kalman Filters with the P, PV and PVA dynamics models for object tracking in sensor networks.