Event-based State Estimation of a Discrete-State Hidden Markov Model through a Reliable Communication Channel

In this work, a state estimation problem is considered for a hidden Markov model subject to event-based sensor measurement updates sent through a reliable communication channel. The change of probability approach is utilized to consider the estimation problem, and an analytical expression for the probability distributions of the states conditioned on all the past hybrid point-and set-valued measurement information caused by the event-triggering scheme is obtained. Also, it is shown that the scenario with a lossy channel, but without the event-trigger, can be treated as a special case of the reliable channel results, and thus can be solved by applying the proposed results.