The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
A Novel Adaptive Kalman Filter With Inaccurate Process and Measurement Noise Covariance Matrices
Open accessAbstract
In this paper, a novel variational Bayesian (VB)-based adaptive Kalman filter (VBAKF) for linear Gaussian state-space models with inaccurate process and measurement noise covariance matrices is proposed. By choosing inverse Wishart priors, the state together with the predicted error and measurement noise covariance matrices are inferred based on the VB approach. Simulation results for a target tracking example illustrate that the proposed VBAKF has better robustness to resist the uncertainties of process and measurement noise covariance matrices than existing state-of-the-art filters.
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Cites
Book Review: Pattern Recognition and Machine Learning
Nasrabadi, N. M. (2007) · Journal of Electronic Imaging · doi:10.1117/1.2819119 · Cited by 22,066
Pattern Recognition and Machine Learning
Neal, R. M. (2007) · Technometrics · doi:10.1198/tech.2007.s518 · Cited by 4,571
Adaptive Kalman Filtering for INS/GPS
Mohamed, A. H., & Schwarz, K. P. (1999) · Journal of Geodesy · doi:10.1007/s001900050236 · Cited by 1,092
The variational approximation for Bayesian inference
Tzikas, D. G., Likas, A. C., & Galatsanos, N. P. (2008) · IEEE Signal Processing Magazine · doi:10.1109/msp.2008.929620 · Cited by 1,039
Approaches to adaptive filtering
Mehra, R. (1972) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1972.1100100 · Cited by 943
Recursive Noise Adaptive Kalman Filtering by Variational Bayesian Approximations
Sarkka, S., & Nummenmaa, A. (2009) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2008.2008348 · Cited by 732
Approximate Inference in State-Space Models With Heavy-Tailed Noise
Agamennoni, G., Nieto, J. I., & Nebot, E. M. (2012) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2012.2208106 · Cited by 281
A Student's t filter for heavy tailed process and measurement noise
Roth, M., Ozkan, E., & Gustafsson, F. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6638770 · Cited by 229
Approximate Bayesian Smoothing with Unknown Process and Measurement Noise Covariances
Ardeshiri, T., Özkan, E., Orguner, U., & Gustafsson, F. (2015) · IEEE Signal Processing Letters · doi:10.1109/lsp.2015.2490543 · Cited by 122
Multivariate Spectral Estimation Based on the Concept of Optimal Prediction
Zorzi, M. (2014) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2014.2359713 · Cited by 64
Cited by
Enhanced Deep Learning-Aided Kalman Filter Based on KalmanNet for Robust Nonlinear State Estimation
Fu, T., Mao, G., Wang, Z., & Wang, K. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3672319 · Cited by 6
Metadata
- Authors
- Huang, Y., Zhang, Y., Wu, Z., Li, N., & Chambers, J.
- Venue
- IEEE Transactions on Automatic Control
- Published
- 2018-02
- Volume
- 63
- Issue
- 2
- Pages
- 594-601
- ISSN
- 0018-9286 (ISSN-L)
- 1558-2523
- 2334-3303
- OpenAlex
- W2737188128
- Cited by
- 731
- References
- 11
- FWCI
- 21.16
- Type
- journal article
- Language
- en
- Open access
- green
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