State estimation for networked systems: an extended IMM algorithm

In this article, the problem of state estimation for networked systems (NSs) with three kinds of observation uncertainties (i.e. missing measurements, packet delays and packet dropouts) and without timestamps in the measurement data is investigated. Both the measurement state and network transmission state are assumed to follow a Markov process, which can capture the temporal correlation nature of the measurement process and network channels. The NS is modelled as a special Markovian jump linear system (MJLS). Then, by modifying the widely adopted interacting multiple models (IMM) algorithm, an extended IMM algorithm for the state estimation of the MJLS is proposed. The multiple filters strategy adopted in this article takes advantage of the particular characteristics of each mode as much as possible and updates the probability estimation of each mode; ultimately, it achieves better estimation performance than the single filter strategy used in existing approaches. Another contribution of this article is the extension of the standard IMM algorithm to handle some special characteristics of the MJLS established herein. The effectiveness and advantage of the proposed method are verified by simulation.