Entropy analysis of estimating systems

A study of the use of entropy as a criterion function for analyzing the performance of sampled-data estimating systems is presented, and performance bounds are obtained for a broad class of such systems. The general result is that the difference between the entropy of the signal to be estimated and the entropy of its estimate based on the output of a noisy sensor can never be larger than the mutual information between the sensor input and output. An interesting, but not totally satisfactory, sufficient condition for attainment of the bound is developed.