State Smoothing by Sum-of-Norms Regularization
2010 (English)In: Proceedings of the 49th Conference on Decision and Control, 2010, 2880-2885 p.Conference paper (Refereed)
The presence of abrupt changes, such as impulsive disturbances and load disturbances, make state estimation considerably more difficult than the standard setting with Gaussian process noise. Nevertheless, this type of disturbances is commonly occurring in applications which makes it an important problem. An abrupt change often introduces a jump in the state and the problem is therefore readily treated by change detection techniques. In this paper, we take a rather different approach. The state smoothing problem for linear state space models is here formulated as a least-squares problem with sum-of-norms regularization, a generalization of the ℓ1-regularization. A nice property of the suggested formulation is that it only has one tuning parameter, the regularization constant which is used to trade off fit and the number of jumps.
Place, publisher, year, edition, pages
2010. 2880-2885 p.
Smoothing, State estimation, Signal processing, Sparsity, lL-regularization
IdentifiersURN: urn:nbn:se:liu:diva-62903DOI: 10.1109/CDC.2010.5717386ISBN: 978-1-4244-7745-6OAI: oai:DiVA.org:liu-62903DiVA: diva2:375029
49th Conference on Decision and Control, Atlanta, GA, USA, 15-17 December, 2010