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• W2912688846 abstract "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." @default.
• W2912688846 created "2019-02-21" @default.
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• W2912688846 date "2019-10-01" @default.
• W2912688846 modified "2023-10-15" @default.
• W2912688846 title "Event-Triggered Risk-Sensitive State Estimation for Hidden Markov Models" @default.
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• W2912688846 doi "https://doi.org/10.1109/tac.2019.2894616" @default.
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