State estimation via limited capacity noisy communication channels

Abstract

The paper addresses a state estimation problem involving communication errors and capacity constraints. Discrete-time partially observed linear systems perturbed by stochastic unbounded additive disturbances are studied. Unlike the classic theory, the sensor signals are communicated to the estimator over a limited capacity noisy digital link modeled as a stochastic discrete memoryless channel. It is shown that the capability of the noisy channel to ensure state estimation with a bounded in probability error is identical to its capability to transmit information with as small probability of error as desired. In other words, the classic Shannon capacity of the channel constitutes the boundary of the observability domain. It is shown that whenever the Shannon capacity bound is met, a reliable observation can be ensured by means of a state estimator consuming a bounded (as time progresses) computational complexity and memory per unit time. The corresponding state estimator is constructed explicitly and is based on the classic block coding approach, so that traditional block encoding–decoding procedures can be employed for its implementation.