Distributed filtering for uncertain systems under switching sensor networks and quantized communications Abstract   This paper considers the distributed filtering problem for a class of stochastic uncertain systems under quantized data flowing over switching sensor networks. Employing the biased noisy observations of the local sensor and intervalquantized messages from neighboring sensors successively, an extended state based distributed Kalman filter (DKF) is proposed for simultaneously estimating both system state and uncertain dynamics. To alleviate the effect of observation biases, an eventtriggered update based DKF is presented with a tighter mean square error (MSE) bound than that of the timedriven one by designing a proper threshold. Both the two DKFs are shown to provide the upper bounds of MSE online for each sensor. Under mild conditions on systems and networks, the MSE boundedness and asymptotic unbiasedness for the proposed two DKFs are proved. Finally, numerical simulations demonstrate the effectiveness of the developed filters.

Optimal Distributed Fusion Filtering with the Mean and Variance Information of the State The distributed optimal (linear minimum variance) fusion filtering problem for multisensor discretetime stochastic systems is considered. Based on the projection theory, a new fusion algorithm is presented. The mean and variance information of the state can be computed offline. The local filters are Kalman filters using local measurement data. After obtaining the local filtering error variances and cross covariances, using the new fusion algorithm, the distributed optimal fusion filter is derived. Commonly, this fusion filter is better than (at least, as good as) the fusion filter weighted by matrices. A numerical example shows the effectiveness of the new fusion algorithm.