An information-based complexity framework for optimal induced-norm and set membership state estimation

摘要

In this paper a unified famework 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 interpretation, allowing for a straightforward derivation of 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.