Approximate Bayesian Smoothing with Unknown Process and Measurement Noise Covariances
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
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
Institute of Electrical and Electronics Engineers (IEEE), 2015. Vol. 22, no 12, 2450-2454 p.
Adaptive smoothing, Kalman filtering, noise covariance, Rauch-Tung-Striebel smoother, sensor calibration, time-varying noiseco variances, variational Bayes
IdentifiersURN: urn:nbn:se:liu:diva-121617DOI: 10.1109/LSP.2015.2490543OAI: oai:DiVA.org:liu-121617DiVA: diva2:857313
At the time for thesis presentation publication was in status: Manuscript2015-09-282015-09-282016-08-04Bibliographically approved