Reduced-Order Filtering of Jump Markov Systems with Noise-Free Measurements

Document Type

Article

Publication Date

11-2000

Publisher

Elsevier

Source Publication

Journal of the Franklin Institute

Source ISSN

0016-0032

Original Item ID

DOI: 10.1016/S0016-0032(00)00054-5

Abstract

In continuous-time Kalman filtering for jump Markov systems, it is required that the measurement noise covariance be nonsingular. In this work, the case of noise-free measurements is considered and it is proposed that a reduced-order filter be used to overcome this singularity problem. This filter is optimal in the minimum variance sense and is of dimension (n−p) where n and p are the state and measurement vector dimensions, respectively. After the optimal filter equations are derived for the finite-time case, we focus on the infinite-time case and characterize the set of all assignable estimation error covariances and parametrize the set of all estimator gains. The conditions for the existence of the optimal steady-state filter are obtained in terms of the system theoretic properties of the original signal model.

Comments

Journal of the Franklin Institute, Vol. 337, No. 7 (November 2000): 923-928. DOI.

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