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Approximate Bayesian Smoothing with Unknown Process and Measurement Noise Covariances
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
Middle E Technical University, Turkey.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
2015 (English)In: IEEE Signal Processing Letters, ISSN 1070-9908, E-ISSN 1558-2361, Vol. 22, no 12, 2450-2454 p.Article in journal (Refereed) Published
Abstract [en]

We present an adaptive smoother for linear state-space models with unknown process and measurement noise covariances. The proposed method utilizes the variational Bayes technique to perform approximate inference. The resulting smoother is computationally efficient, easy to implement, and can be applied to high dimensional linear systems. The performance of the algorithm is illustrated on a target tracking example.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC , 2015. Vol. 22, no 12, 2450-2454 p.
Keyword [en]
Adaptive smoothing; Kalman filtering; noise covariance; Rauch-Tung-Striebel smoother; sensor calibration; time-varying noise covariances; variational Bayes
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:liu:diva-123059DOI: 10.1109/LSP.2015.2490543ISI: 000364207300007OAI: oai:DiVA.org:liu-123059DiVA: diva2:876661
Note

Funding Agencies|Swedish research council (VR)

Available from: 2015-12-04 Created: 2015-12-03 Last updated: 2015-12-04

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Ardeshiri, TohidÖzkan, EmreGustafsson, Fredrik
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