Recursive filtering for time-varying systems under duty cycle scheduling based on collaborative prediction

In this paper, a novel recursive filtering scheme combined with collaborative prediction (CP) method is proposed for a class of linear time-varying systems under duty cycle communication scheduling. The communication between the sensor nodes and the remote filter is implemented through wireless networks. In order to save energy even further, the working states of sensor nodes alternate between activation and dormancy depending on the preset duty cycle. The aim of this paper is to design a usable recursive filtering scheme for linear time-varying system subject to the duty cycle scheduling (DCS) in the case of limited energy. The DCS is modeled according to the corresponding scheduling rule. The unsent measurement outputs due to sensors being dormancy state are predicted by CP algorithm, based on which a recursive filtering scheme is developed. Also, the filter gain is calculated by minimizing the trace of error covariance. Subsequently, the boundedness of the designed filtering algorithm is analyzed. Finally, a numerical simulation example is presented to illustrate the effectiveness of the proposed recursive filtering approach based on CP algorithm subject to the DCS. (C) 2020 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.