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;
1999-01-01
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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https://hdl.handle.net/11365/17869
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