State estimation with quantised sensor information in wireless sensor networks
Jiahui Xu, Jinming Li · IET Signal Processing · 2011
The problem of state estimation with quantised measurements is considered for general vector state-vector observation model in wireless sensor networks (WSNs), which broadens the scope of sign of innovations Kalman filtering (SOI-KF) and multiple-level quantised innovations Kalman filter (MLQIKF). Adhering to the limited power and bandwidth resources WSNs must operate with, this paper introduces a novel decentralised unscented Kalman filtering (UKF) estimators based on quantised measurement innovations. In the quantisation approach, the region of a measurement innovation is partitioned into L contiguous, non-overlapping intervals. After quantised, the measurement information is broadcasted by using a variable number of bytes coding method. A filtering algorithm for general vector state-vector observation case is developed based on the quantised measurement information. Performance analysis and Monte Carlo simulations reveal that under the same bandwidth constraint condition, the performance of novel quantised UKF tracker, indeed better than those of SOI-KF and MLQIKF in error covariance matrix (ECM) and root mean-square error (RMSE) and almost identical to these of an UKF based on analogue-amplitude observations.