The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Recursive Noise Adaptive Kalman Filtering by Variational Bayesian Approximations
Abstract
This article considers the application of variational Bayesian methods to joint recursive estimation of the dynamic state and the time-varying measurement noise parameters in linear state space models. The proposed adaptive Kalman filtering method is based on forming a separable variational approximation to the joint posterior distribution of states and noise parameters on each time step separately. The result is a recursive algorithm, where on each step the state is estimated with Kalman filter and the sufficient statistics of the noise variances are estimated with a fixed-point iteration. The performance of the algorithm is demonstrated with simulated data.
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Cites
A New Approach to Linear Filtering and Prediction Problems
Kalman, R. E. (1960) · Journal of Basic Engineering · doi:10.1115/1.3662552 · Cited by 31,751
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Stochastic Processes and Filtering Theory
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Estimation with Applications to Tracking and Navigation: Theory, Algorithms and Software
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Information theory, inference, and learning algorithms
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Information Theory, Inference, and Learning Algorithms
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Stochastic models, estimation, and control
(1982) · Mathematics in Science and Engineering · doi:10.1016/s0076-5392(08)x6186-7 · Cited by 3,229
Kalman Filtering and Neural Networks
(2001) · Wiley eBooks · doi:10.1002/0471221546 · Cited by 2,386
A new approach for filtering nonlinear systems
Julier, S. J., Uhlmann, J. K., & Durrant-Whyte, H. F. (2005) · Proceedings of 1995 American Control Conference - ACC'95 · doi:10.1109/acc.1995.529783 · Cited by 1,926
Cited by
A Novel Adaptive Kalman Filter With Inaccurate Process and Measurement Noise Covariance Matrices
Huang, Y., Zhang, Y., Wu, Z., Li, N., & Chambers, J. (2018) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2017.2730480 · Cited by 731
Adaptive Metropolis algorithm using variational Bayesian adaptive Kalman filter
Mbalawata, I. S., Särkkä, S., Vihola, M., & Haario, H. (2015) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2014.10.006 · Cited by 54
Self-citation
Metadata
- Authors
- Sarkka, S., & Nummenmaa, A.
- Venue
- IEEE Transactions on Automatic Control
- Published
- 2009-03
- Volume
- 54
- Issue
- 3
- Pages
- 596-600
- ISSN
- 0018-9286 (ISSN-L)
- 1558-2523
- OpenAlex
- W2095376999
- Cited by
- 732
- References
- 24
- FWCI
- 9.04
- Type
- journal article
- Language
- en
- Open access
- closed
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