Robust Smoothing for State-Space Models with Unknown Noise Statistics Conference Paper

abstract

• 2018 IEEE. The Kalman smoother provides optimal smoothing for fully-known state-space models. However, model uncertainty degrades the performance of the smoother dramatically. In this paper, we are concerned with state-space models, in which noise statistics are unknown and propose an optimal Bayesian Kalman smoother (OBKS), which is optimal relative to the posterior distribution of the unknown noise parameters. The Bayesian innovation process and Bayesian orthogonality principle lie at the heart of the proposed smoothing framework. Through introducing the effective Kalman smoothing gain, we develop a recursive forward-backward structure, which is analogous to that of the classical Kalman smoother. We demonstrate the effectiveness of the proposed smoother by applying it to a target tracking example.

name of conference

• 2018 52nd Asilomar Conference on Signals, Systems, and Computers

authors

• Dougherty, Edward
• Qian, Xiaoning

published proceedings

• 2018 CONFERENCE RECORD OF 52ND ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS, AND COMPUTERS

author list (cited authors)

• Dehghannasiri, R., Qian, X., & Dougherty, E. R.

citation count

• 0

complete list of authors

• Dehghannasiri, Roozbeh||Qian, Xiaoning||Dougherty, Edward R

publication date

• October 2018

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
• Innovation Process
• Kalman Smoother
• Orthogonality Principle
• Uncertain State-space Model

Digital Object Identifier (DOI)

• 10.1109/acssc.2018.8645194

International Standard Book Number (ISBN) 13

• 9781538692189

start page

• 1024

end page

• 1028

volume

• 00

URL

• http://dx.doi.org/10.1109/acssc.2018.8645194