Event-Triggered Risk-Sensitive State Estimation for Hidden Markov Models
Jiapeng Xu, Daniel W. C. Ho, Fangfei Li, Wen Dong Yang, Yang Shan Tang · IEEE Transactions on Automatic Control · 2019
An event-triggered risk-sensitive state estimation problem for hidden Markov models is investigated in this work. The event-triggered scheme considered is fairly general, which covers most existing event-triggered conditions. By utilizing the reference probability measure approach, this estimation problem is reformulated as an equivalent one and solved. We show that the event-triggered risk-sensitive maximum a posteriori probability estimates can be obtained based on a newly defined unnormalized information state, which has a linear recursive form. Furthermore, the explicit solutions for two major classes of event-triggered conditions are derived if the measurement noise is Gaussian. A numerical comparison is provided to illustrate the effectiveness of the proposed results.