Target tracking with distance-dependent measurement noise in wireless sensor networks

A distributed extended Kalman filter (EKF) algorithm is developed for tracking moving targets in a wireless sensor network equipped with distance estimating sensors. In particular, a distance-dependent measurement error of range-estimating sensors is modeled as a multiplicative noise in the observation model. A new formulation of EKF, called generalized EKF (GEKF) based on the multiplicative noise model is developed. Compared to conventional EKF formulation, it is shown that GEKF can achieve smaller estimation error than traditional EKF. Simulation results also demonstrated superior performance of GEKF.