Kalman Filtering overWireless Fading Channels
In this paper, the authors consider the estimation of a dynamical system over a wireless fading channel using a Kalman filter. They 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. They consider three cases of "Full knowledge," "No knowledge" and "Partial knowledge," based on the knowledge available on the communication quality. They characterize the dynamics of these scenarios and establish the necessary and sufficient conditions to ensure stability. They, then propose new ways of optimizing the packet drop in order to minimize the average estimation error variance of the Kalman filter.