Approximate Bayesian Smoothing with Unknown Process and Measurement Noise Covariances

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Date

2015-12-01

Author

Ardeshiri, Tohid
Özkan, Emre
Orguner, Umut
Gustafsson, Fredrik

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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.

Subject Keywords

Adaptive smoothing, Variational Bayes, Time-varying noise covariances, Sensor calibration, Rauch-Tung-Striebel smoother, Noise covariance, Kalman filtering

URI

https://hdl.handle.net/11511/43860

Journal

IEEE SIGNAL PROCESSING LETTERS

DOI

https://doi.org/10.1109/lsp.2015.2490543

Collections

Department of Electrical and Electronics Engineering, Article

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Citation Formats

T. Ardeshiri, E. Özkan, U. Orguner, and F. Gustafsson, “Approximate Bayesian Smoothing with Unknown Process and Measurement Noise Covariances,” IEEE SIGNAL PROCESSING LETTERS, pp. 2450–2454, 2015, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/43860.