  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
interval-quantized 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 event-triggered update based DKF is presented with a
tighter mean square error bound than that of the time-driven one by designing a
proper threshold. Both the two DKFs are shown to provide the upper bounds of
mean square errors online for each sensor. Under mild conditions on systems and
networks, the mean square error boundedness and asymptotic unbiasedness for the
proposed two DKFs are proved. Finally, the numerical simulations demonstrate
the effectiveness of the developed filters.
