This article focuses on distributed filtering for a discrete time-varying system observed by multiple smart sensors, where every sensor only measures partial state information of the target system and then… Click to show full abstract
This article focuses on distributed filtering for a discrete time-varying system observed by multiple smart sensors, where every sensor only measures partial state information of the target system and then sends it to a corresponding remote estimator. Subsequently, the estimator performs the local Kalman filter and shares its estimates with the estimators in its neighborhood in a distributed way. This article aims to reduce the communication rate between sensors and estimators, and guarantee the estimation performance, simultaneously. To achieve this goal, a novel distributed information fusion algorithm is designed by embedding a stochastic event-triggered communication mechanism. Based on a new developed mathematics technique, the consistency and stability of the proposed distributed state estimation algorithms are both ensured. Furthermore, compared with the literature, the stability can be guaranteed with a milder collectively uniformly observable condition. Moreover, the tradeoff between the communication rate and estimation performance is analyzed in a closed-form expression. Finally, the effectiveness of the theoretical results is demonstrated by several comparative numerical examples.
               
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