In this paper a unified framework founded on Information-Based Complexity is introduced, to study set membership and optimal induced-norm state estimation problems, for linear systems subject to norm bounded process noise and measurement errors. The proposed approach leads to a clean geometric picture of the problem, allowing for a straightforward derivation of several existing results. Moreover, it permits to tackle new estimation problems in which both induced-norm optimization and consistency of the estimate with the noise bound are required.

Garulli, A., Vicino, A., & Zappa, G. (1999). Optimal induced norm and set membership state smoothing and filtering for linear systems with bounded disturbances. AUTOMATICA, 35(5), 767-776 [10.1016/S0005-1098(98)00212-X].

Optimal induced norm and set membership state smoothing and filtering for linear systems with bounded disturbances

GARULLI, ANDREA;VICINO, ANTONIO;
1999

Abstract

In this paper a unified framework founded on Information-Based Complexity is introduced, to study set membership and optimal induced-norm state estimation problems, for linear systems subject to norm bounded process noise and measurement errors. The proposed approach leads to a clean geometric picture of the problem, allowing for a straightforward derivation of several existing results. Moreover, it permits to tackle new estimation problems in which both induced-norm optimization and consistency of the estimate with the noise bound are required.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11365/17869
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