Sequential fusion estimations for asynchronous sensor networks

Abstract

This paper presents a hybrid sequential fusion estimation method for target tracking in asynchronous wireless sensor networks (WSNs). The model mismatching caused by asynchronous sampling, as well as model uncertainties, is compensated by introducing a time-varying fading factor into the unscented Kalman filter (UKF) and the square root unscented strong tracking filter (SR-USTF) is proposed to improve the stability of the USTF. Moreover, a hybrid sequential measurement fusion estimation method, combining the merits of the UKF and the USTF, is presented and it is able to deal with communication uncertainties such as delays and packet losses in a uniform framework. Simulations of mobile robot tracking are provided to show the effectiveness and superiorities of the proposed hybrid sequential fusion estimation method.