期刊
IEEE SIGNAL PROCESSING LETTERS
卷 22, 期 12, 页码 2450-2454出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LSP.2015.2490543
关键词
Adaptive smoothing; Kalman filtering; noise covariance; Rauch-Tung-Striebel smoother; sensor calibration; time-varying noise covariances; variational Bayes
资金
- Swedish research council (VR)
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.
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