Bayesian Kalman Filtering in the Presence of Unknown Noise Statistics Using Factor Graphs Conference Paper

abstract

• 2017 IEEE. We propose an optimal Bayesian Kalman filtering framework that provides optimal performance relative to the posterior distribution of unknown noise parameters obtained from incorporating data into the prior distribution. The structure of the proposed filter is similar to that of classical Kalman filtering except the use of posterior effective noise statistics. We introduce a factor-graph-based approach to compute the likelihood function required for computing the posterior effective statistics. The performance of the proposed method is verified by applying it to a target tracking example.

name of conference

• 2017 51st Asilomar Conference on Signals, Systems, and Computers

authors

• Qian, Xiaoning

published proceedings

• 2017 FIFTY-FIRST ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS, AND COMPUTERS

author list (cited authors)

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

citation count

• 3

complete list of authors

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

publication date

• October 2017

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 Robustness
• Factor Graph
• Kalman Filter
• Uncertain Noise Parameters

Digital Object Identifier (DOI)

• 10.1109/acssc.2017.8335159

International Standard Book Number (ISBN) 13

• 9781538618233

start page

• 166

end page

• 170

volume

• 2017-October

URL

• http://dx.doi.org/10.1109/acssc.2017.8335159