State estimation with uncertain parametric models

This paper develops robust estimation algorithms for state-space models that are subject to bounded parametric uncertainties. Compared with existing robust filters, the new filters perform data regulaarization rather than de-regularization and they do not require existence conditions. The resulting filter structures also turn out to be similar to various (time- and measurement-update, prediction, and information) forms of the Kalman filter, albeit ones that operate on corrected parameters rather than on the given nominal parameters.

Published in:
Proceedings of the 8th IEEE Mediterranean Conference on Control & Automation
Presented at:
8th IEEE Mediterranean Conference on Control & Automation, Rio, Greece, June, 2000

 Record created 2018-01-04, last modified 2018-09-13

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