论文信息 - Robust filtering with missing data and a deterministic description of noise and uncertainty

Robust filtering with missing data and a deterministic description of noise and uncertainty

The paper considers the problem of robust state estimation for the case in which some of the measurement data is missing. This problem is considered within the framework of a class of uncertain discrete-time systems with a deterministic description of noise and uncertainty. The main result is a recursive scheme for constructing an ellipsoidal state estimation set of all states consistent with the available measured output and the given noise and uncertainty description.

Ian R. Petersen | Andrey V. Savkin

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