Robustness and risk-sensitive filtering

• Published in: IEEE transactions on automatic control Vol. 47; no. 3; pp. 451 - 461
• Main Authors: Boel, R.K., James, M.R., Petersen, I.R.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.03.2002; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Applied sciences; Automatic control; Computer science; control theory; systems; Control system analysis; Control theory. Systems; Error analysis; Errors; Estimators; Exact sciences and technology; Filtering; Filters; Filtration; Minimax techniques; Noise robustness; Probability; Robust control; Robustness; Signal processing; State estimation; Statistical distributions; Upper bound; Upper bounds; H infinite optimization; Upper bound; Uncertain system; Filtering; Probabilistic approach; Filter; Estimation; Observer; Distance measurement; Robustness; Risk analysis; Modeling
• ISSN: 0018-9286; 1558-2523
• DOI: 10.1109/9.989082

This paper gives a precise meaning to the robustness of risk-sensitive filters for problems in which one is uncertain as to the exact value of the probability model. It is shown that risk-sensitive estimators (including filters) enjoy an error bound which is the sum of two terms, the first of which coincides with an upper bound on the error that one would obtain if one knew exactly the underlying probability model, while the second term is a measure of the distance between the true and design probability models. The paper includes a discussion of several approaches to estimation, including H/sub /spl infin// filtering.