State estimation for linear systems with communication channels

This work studies the state estimation problem for linear time invariant systems with reliable/unreliale communication channels. The states of the systems in remote sites are estimated based on observations which are quantized and sent through communication channels. In this work, Lloyd-Max quantization laws are adopted. A design approach for a Kalman filter based state estimator is proposed in the case when the communication channels are noise free. And then the estimator design approach is extended to the case when the network channels have contain data drop rates. To evaluate the performance of the estimators, the Cramer-Rao lower bounds (CRLBs) of estimation errors are studied by applying Bayesian Fisher information. The analytic solutions of the CRLBs are presented in terms of the system's model and characteristics of the quantization law.