Convergence of discrete-time Kalman filter estimate to continuous-time estimate for systems with unbounded observation

Atte Aalto (1, 2, 3)

1 M3DISIM - Mathematical and Mechanical Modeling with Data Interaction in Simulations for Medicine
2 Department of Mathematics and Systems Analysis [Aalto]
3 University of Luxembourg [Luxembourg]

In this article, we complement recent results on the convergence of the state estimate obtained by applying the discrete-time Kalman filter on a time-sampled continuous-time system. As the temporal discretization is refined, the estimate converges to the continuous-time estimate given by the Kalman–Bucy filter. We shall give bounds for the convergence rates for the variance of the discrepancy between these two estimates. The contribution of this article is to generalize the convergence results to systems with unbounded observation operators under different sets of assumptions, including systems with diagonaliz-able generators, systems with admissible observation operators, and systems with analytic semigroups. The proofs are based on applying the discrete-time Kalman filter on a dense, numerable subset on the time interval [0,T] and bounding the increments obtained. These bounds are obtained by studying the regularity of the underlying semigroup and the noise-free output.

Mots clés: Boundary control systems; Temporal discretization; Infinite-dimensional systems; Kalman filter; Sampled data

Domaines: Optimisation et contrôle [math.OC]

Atte Aalto. Convergence of discrete-time Kalman filter estimate to continuous-time estimate for systems with unbounded observation. Mathematics of Control, Signals, and Systems, 2018, 30 (3), pp.9. ⟨10.1007/s0049⟩. ⟨hal-01236950⟩