Moving Horizon Estimation with Decimated Observations *

This paper addresses the problem of moving horizon (MH) state estimation of discrete lumped nonlinear systems. It is assumed that the measurements of the observed variables are not available at every sampling instant (decimated observations). An estimation algorithm is provided for that purpose, together with results on its convergence. It is shown that, under convenient assumptions, the estimation error is bounded, with a bound that grows with the number of samples between consecutive observations. The algorithm features are illustrated by simulations concerning the application to state estimation in a model of the HIV-1 infection. The simulations show that the MH estimator exhibits superior performance over the extended Kalman filter. This difference of performance increases with the growth of the time interval between consecutive measurements.