Distributed estimation for stochastic hybrid systems with event‐triggered sensor schedule
Cui Zhang, Yingmin Jia · IET Control Theory and Applications · 2016
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.