Kalman Filtering over Wireless Fading Channels

In this chapter, we consider the estimation of a dynamical system over a wireless fading channel using a Kalman filter. We develop a framework for understanding the impact of stochastic communication noise, packet drop and the knowledge available on the link qualities on Kalman filtering over fading channels. We consider three cases of “full knowledge”, “no knowledge” and “partial knowledge”, based on the knowledge available on the communication quality. We characterize the dynamics of these scenarios and establish the necessary and sufficient conditions to ensure stability. We then propose new ways of optimizing the packet drop in order to minimize the average estimation error variance of the Kalman filter. We consider both adaptive and non-adaptive optimization of the packet drop. Our results show that considerable performance improvement can be achieved by using the proposed framework.