Generalised Kalman filter tracking with multiplicative measurement noise in a wireless sensor network

Abstract

A new generalised Kalman filtering algorithm using a multiplicative measurement noise model is developed for tracking moving targets in a wireless sensor network. This multiplicative error model facilitates more accurate characterisation of the distance dependence measurement errors of range-estimating sensors. Two new formulations of extended Kalman filter (EKF) and unscented Kalman filter (UKF), called generalised EKF (GEKF) and generalised UKF (GUKF) are derived. Comparing with conventional EKF and UKF formulations, it is shown that GEKF and GUKF can achieve smaller tracking error than traditional EKF and UKF. Simulation results are also reported that demonstrated the superior performance of GEKF and GUKF over existing methods.