State estimation with partially observed inputs: a unified Kalman filtering approach. (English) Zbl 1269.93113

Li, Baibing

Automatica 49, No. 3, 816-820 (2013).

Summary: For linear stochastic time-varying state space models with Gaussian noises, this paper investigates state estimation for the scenario where the input variables of the state equation are not fully observed but rather the input data are available only at an aggregate level. Unlike the existing filters for unknown inputs that are based on the approach of minimum-variance unbiased estimation, this paper does not impose the unbiasedness condition for state estimation; instead it incorporates a Bayesian approach to derive a modified Kalman filter by pooling the prior knowledge about the state vector at the aggregate level with the measurements on the output variables at the original level of interest. The estimated state vector is shown to be a minimum-mean-square-error estimator. The developed filter provides a unified approach to state estimation: it includes the existing filters obtained under two extreme scenarios as its special cases, i.e., the classical Kalman filter where all the inputs are observed and the filter for unknown inputs.

MSC:

93E10	Estimation and detection in stochastic control theory
93E11	Filtering in stochastic control theory
93C55	Discrete-time control/observation systems
93C05	Linear systems in control theory

Keywords:

Bayesian inference; data aggregation; input observability; Kalman filters; state space models; state estimation; minimum-mean-square-error estimator; Gaussian noises