A Bayesian Framework for Robust Kalman Filtering Under Uncertain Noise Statistics Conference Paper

abstract

• 2016 IEEE. In this paper, we propose a Bayesian framework for robust Kalman filtering when noise statistics are unknown. The proposed intrinsically Bayesian robust Kalman filter is robust in the Bayesian sense meaning that it guarantees the best average performance relative to the prior distribution governing unknown noise parameters. The basics of Kalman filtering such as the projection theorem and the innovation process are revisited and extended to their Bayesian counterparts. These enable us to design the intrinsically Bayesian robust Kalman filter in a similar way that one can find the classical Kalman filter for a known model.

name of conference

• 2016 50th Asilomar Conference on Signals, Systems and Computers

authors

• Dougherty, Edward

published proceedings

• 2016 50TH ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS AND COMPUTERS

altmetric score

• 1

author list (cited authors)

• Dehghannasiri, R., Esfahani, M. S., & Dougherty, E. R.

citation count

• 3

complete list of authors

• Dehghannasiri, Roozbeh||Esfahani, Mohammad Shahrokh||Dougherty, Edward R

publication date

• November 2016

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

published in

• Conference record / Asilomar Conference on Signals, Systems & Computers. Asilomar Conference on Signals, Systems & Computers  Journal

keywords

• Bayesian Innovation Process
• Bayesian Projection Theorem
• Intrinsically Bayesian Robust
• Kalman Filter

Digital Object Identifier (DOI)

• 10.1109/acssc.2016.7869637

International Standard Book Number (ISBN) 13

• 9781538639542

start page

• 1545

end page

• 1549

URL

• http://dx.doi.org/10.1109/acssc.2016.7869637