The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
Mixture Kalman Filters
Abstract
Summary: In treating dynamic systems, sequential Monte Carlo methods use discrete samples to represent a complicated probability distribution and use rejection sampling, importance sampling and weighted resampling to complete the on-line ‘filtering’ task. We propose a special sequential Monte Carlo method, the mixture Kalman filter, which uses a random mixture of the Gaussian distributions to approximate a target distribution. It is designed for on-line estimation and prediction of conditional and partial conditional dynamic linear models, which are themselves a class of widely used non-linear systems and also serve to approximate many others. Compared with a few available filtering methods including Monte Carlo methods, the gain in efficiency that is provided by the mixture Kalman filter can be very substantial. Another contribution of the paper is the formulation of many non-linear systems into conditional or partial conditional linear form, to which the mixture Kalman filter can be applied. Examples in target tracking and digital communications are given to demonstrate the procedures proposed.
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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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On sequential Monte Carlo sampling methods for Bayesian filtering
Doucet, A., Godsill, S., & Andrieu, C. (2000) · Statistics and Computing · doi:10.1023/a:1008935410038 · Cited by 4,411
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Filtering via Simulation: Auxiliary Particle Filters
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Monte Carlo Filter and Smoother for Non-Gaussian Nonlinear State Space Models
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Cited by
Djuric, P. M., Kotecha, J. H., Zhang, J., Huang, Y., Ghirmai, T., Bugallo, M. F., & Miguez, J. (2003) · IEEE Signal Processing Magazine · doi:10.1109/msp.2003.1236770 · Cited by 965
Particle filter theory and practice with positioning applications
Gustafsson, F. (2010) · IEEE Aerospace and Electronic Systems Magazine · doi:10.1109/maes.2010.5546308 · Cited by 720
A Survey of Sequential Monte Carlo Methods for Economics and Finance
Creal, D. (2012) · Econometric Reviews · doi:10.1080/07474938.2011.607333 · Cited by 301
Time series forecasting for nonlinear and non-stationary processes: a review and comparative study
Cheng, C., Sa-Ngasoongsong, A., Beyca, O., Le, T., Yang, H., Kong, Z. (., & Bukkapatnam, S. T. S. (2015) · IIE Transactions · doi:10.1080/0740817x.2014.999180 · Cited by 287
Metadata
- Authors
- Chen, R., & Liu, J. S.
- Venue
- Journal of the Royal Statistical Society Series B: Statistical Methodology
- Publisher
- Oxford University Press (OUP)
- Published
- 2000-09-01
- Volume
- 62
- Issue
- 3
- Pages
- 493-508
- ISSN
- 1369-7412 (ISSN-L)
- 1467-9868
- OpenAlex
- W2022023686
- Cited by
- 600
- References
- 22
- FWCI
- 15.83
- Type
- journal article
- Language
- en
- Open access
- closed
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