Event-triggered state estimation for stochastic hybrid systems with missing measurements

• Published in: IET control theory & applications 2018-12, Vol.12 (18), p.2551-2561
• Main Authors: Jin, Zengwang, Hu, Yanyan, Sun, Changyin
• Format: Article
• Language: English
• Subjects: closed loop systems; closed‐loop event‐triggered mechanism; computational complexity; event‐triggered hybrid state estimation; Markov chains; Markov processes; missing measurements; Monte Carlo methods; Monte Carlo simulation; networked systems; optimal estimation; power resources; Research Article; state estimation; statistical analysis; statistical information; stochastic hybrid systems; stochastic systems; target tracking
• ISSN: 1751-8644; 1751-8652; 1751-8652
• DOI: 10.1049/iet-cta.2018.5568

This study is concerned with the event-triggered state estimation problem for a class of stochastic hybrid systems with missing measurements in a networked environment. Two independent Markov chains are introduced to, respectively, characterise the stochastic measurement missing and the possible modal (or mode) transition of the system. In consideration of the constrained bandwidth and limited power resources of networked systems, a closed-loop event-triggered mechanism based on the measurement innovation is designed to trigger data transmission only when trigger conditions are satisfied. To keep the exponentially increasing number of full hypothesis sequences in optimal estimation to bounded computational complexity, the interacting multiple model framework is extended to tackle event-triggered sampling with the statistical information implicit in event-triggered conditions sufficiently explored and the possible measurement missing taken into account. A Monte Carlo simulation involving tracking a two-dimensional manoeuvring target with two operational modes is provided to demonstrate the effectiveness and efficiency of the proposed event-triggered hybrid state estimation in the presence of missing measurements.