State Fusion Estimation for Networked Stochastic Hybrid Systems With Asynchronous Sensors and Multiple Packet Dropouts

This paper is concerned with the state fusion estimation problem for a class of stochastic hybrid systems with asynchronous multi-sensors and multiple packet dropouts. The stochastic packet dropouts over communication channels from sensors to the fusion center are formulated as independent Bernoulli sequences. As one of the most cost-effective estimation approach for hybrid systems, the interactive multiple model framework is adopted, where an input mixing step is introduced at the beginning of each filtering cycle. The asynchronous sensor measurements collected at the fusion center are first aligned to the fusion time, and then fused to update the mode-matched filters based on the quasi-recursive form of linear minimum mean square error estimation, where correlations induced by the synchronization process are carefully calculated and the stochastic packet dropouts are taken into account. Finally, the overall estimate is obtained by further fusing the mode-matched estimates with their updated posterior mode probabilities accordingly. The feasibility and effectiveness of the proposed algorithm is illustrated by a numerical simulation.