On state estimation in switching environments

Abstract

Work concerned with the state estimation in linear discrete-time systems operating in Markov dependent switching environments is discussed. The disturbances influencing the system equations and the measurement equations are assumed to come from one of several Gaussian distributions with different means or variances. By defining the noise in this manner, periodic step changes in the inputs which cannot be feasibly measured for economic or technical reasons can be detected and corrected. These changes can be in the amplitudes of the inputs or in the variances of stochastic inputs. A Bayes estimation procedure is applied to the problem of filtering the observations of the system so that an estimate of the system state is obtained. Computer simulations for the optimal and suboptimal estimators are also presented.