Distributed estimation for stochastic hybrid systems with event-triggered sensor schedule

This study deals with the state estimation problem for stochastic hybrid systems with event-triggered sensor schedule. By employing the basic interacting multiple model (IMM) approach, a novel event-triggered state estimator has been proposed for stochastic hybrid systems based on a closed-loop schedule rule. To compute the mode probabilities in the IMM estimator, a set of sigma points are generated to produce the pseudo-measurements when the sensor measurements are unavailable. Then, the event-triggered state estimator is extended to develop a distributed estimator in the sense of linear minimum mean square error, where the fused estimates are used to implement the re-initialisation in the interacting stage of the IMM estimator. The performance of proposed estimators is illustrated through the Monte Carlo simulations involving tracking a manoeuvring target in the two-dimensional experiment. Simulation results show that the performance of the proposed estimator is compared with that of the existing filter with a reduced computational burden.

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