Robust receding-horizon estimation for discrete-time linear systems in the presence of bounded uncertainties

Receding-horizon state estimation is addressed for a class of uncertain discrete-time linear systems with disturbances acting on the dynamic and measurement equations. The estimates are obtained by minimizing a least-squares cost function in the worst case, i.e., by solving a min-max problem. With respect to previous results (see [1]), the proposed solution is not conservative and, if the computation is too demanding, the problem may be solved approximately with a reduced computational burden. The stability of the estimation errors is guaranteed under suitable conditions. Simulation results are quite satisfying in performance if compared with other methods.