Infoscience

Conference paper

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.

    Reference

    • EPFL-CONF-233676

    Record created on 2018-01-04, modified on 2018-01-16

Fulltext

  • There is no available fulltext. Please contact the lab or the authors.

Related material