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Examines the problem of optimal state estimation or filtering in stochastic systems, through the use of an approach based on information theoretic measures. Comparison of the minimum mean-square measure and the information theoretic measures; Re-examination of the R.E. Kalman's filtering theory; Kalman's filter for as the optimal filter for linear Gaussian system.

Studies the effect of state quantization in scalar discrete-time linear control systems by analyzing the system as a partially observed stochastic system. Presentation of a framework for optimal state information gathering or filtering; Classification of family of probing controls; Optimization of the controlled Markov chain (CMC) model; Simulation example of the optimization; Conclusions