An Encoding–Decoding-Based State Estimation Scheme for State-Saturated Systems Under Buffer-Aided Mechanism

Abstract

In this brief, the encoding-decoding-based state estimation problem is investigated for a class of state-saturated systems under buffer-aided mechanism where the wireless communication is unreliable. The buffer-aided mechanism is exploited to improve the data utilization and estimation performance with the capability of storing the undelivered signals and then transmits them to the remote estimator at each transmission instant. To overcome inherent communication limitations, a novel probabilistic encoding-decoding scheme is introduced to compress the sensor measurements. The objective of this brief is to develop an encoding-decoding-based estimator such that the estimation error covariance is minimized in certain sense for all possible state saturations as well as the effects induced by buffer-aided mechanism. The estimation error covariance conditioned on the transmission intervals is first obtained in recursions and then is minimized by the choice of the appropriate estimator gain. Finally, simulation results are presented to demonstrate the validity of the proposed estimation scheme.