Covariance-based estimation algorithms in networked systems with mixed uncertainties in the observations

In this paper a new observation model is proposed for networked systems subject to three sources of uncertainty. On the one hand, the measured outputs can be only noise (uncertain observations) and, on the other hand, one-step delays or packet dropouts may occur randomly during transmission; it is assumed that, at each sampling time, it is not known if some of these uncertainties have occurred. The random uncertainties are modelled by sequences of Bernoulli random variables. Under these assumptions, recursive least-squares linear estimation algorithms are derived by an innovation approach, without requiring knowledge of the signal evolution equation, but only the covariances of the processes involved in the observation model and the uncertainty probabilities. (C) 2013 Elsevier B.V. All rights reserved.