Distributed unscented Kalman filtering for nonlinear systems: A mixed event‐triggered strategy

Abstract

In this article, the problem of distributed state estimation is addressed for nonlinear systems based on the unscented Kalman filter (UKF) framework. The local estimates are obtained from distributed UKFs with stochastic event-triggered schedules. The consensus of estimated states is achieved by employing the covariance intersection fusion method, while a novel event-triggered strategy for the node-to-node communication is developed. Furthermore, the boundedness and convergence of the covariance matrix are analyzed. Compared with the existing works on distributed Kalman filtering, a new nonlinear global observability condition is proposed to relax the constraint that the system is necessarily observable by each sensor, and the relationship between the convergence and the event-triggering parameters is revealed. Finally, simulations demonstrate the results in this article.

Novel event‐triggered distributed state estimation algorithm for nonlinear systems over wireless sensor networks

“…In Theorem 2, the assumption we used to develop the stability analysis of the EDCKF algorithm is reasonable and is widely utilized in the state-of-the-art researches. [21][22][23][26][27][28][29][30][31]38 Moreover, although the compensation diagonal matrices 𝜂 i k and 𝜃 i k are unknown, the stability of the EDCKF algorithm is still achieved, which indicates that the stability of the proposed algorithm will not be influenced by the system model.…”

“…Unlike collecting local estimation results through a central fusion node, communication between estimators increases the redundancy, robustness, and flexibility of the system. It will enhance the application of distributed state estimation to applications such as autonomous vehicles, multiple unmanned aerial vehicle systems, the Internet of Things, and leader–follower architecture systems 28,29 . Generally, to obtain a fused estimation, weighted average consensus is used in distributed sensor networks while each estimator iteratively exchanges its local information pair $$$ {I}_k^i=\left({y}_k^i,{Y}_k^i\right) $$$ with the estimator within , $$$ i=1,\dots, N $$$.…”

“…It will enhance the application of distributed state estimation to applications such as autonomous vehicles, multiple unmanned aerial vehicle systems, the Internet of Things, and leader-follower architecture systems. 28,29 Generally, to obtain a fused estimation, weighted average consensus is used in distributed sensor networks while each estimator iteratively exchanges its local information pair…”

“…To this end, the event‐triggered mechanism, in which data transmission over estimator‐to‐estimator or sensor‐to‐estimator communication channel is executed only when a sending criteria is satisfied, is adopted in the WSNs. Driven by the event triggering mechanism, several consensus based distributed state estimation algorithms belonging to the third scope have been developed to achieve a desirable balance between communication burden and estimation accuracy 24‐29 . In References 24‐27, different triggering conditions related to local state estimation results are considered for reducing the data transmission in estimator‐to‐estimator communication channel during the consensus processing.…”

“…Driven by the event triggering mechanism, several consensus based distributed state estimation algorithms belonging to the third scope have been developed to achieve a desirable balance between communication burden and estimation accuracy. [24][25][26][27][28][29] In References 24-27, different triggering conditions related to local state estimation results are considered for reducing the data transmission in estimator-to-estimator communication channel during the consensus processing. In particular, consensus based distributed H ∞ filtering algorithm with the time-varying triggering parameter is derived in Reference 27.…”