The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
Nonlinear filters: beyond the Kalman filter
Abstract
Nonlinear filters can provide estimation accuracy that is vastly superior to extended Kalman filters for some important practical applications. We compare several types of nonlinear filters, including: particle filters (PFs), unscented Kalman filters, extended Kalman filters, batch filters and exact recursive filters. The key practical issue in nonlinear filtering is computational complexity, which is often called "the curse of dimensionality". It has been asserted that PFs avoid the curse of dimensionality, but this is generally incorrect. Well-designed PFs with good proposal densities sometimes avoid the curse of dimensionality, but not otherwise. Future research in nonlinear filtering will exploit recent progress in quasi-Monte Carlo algorithms (rather than boring old Monte Carlo methods), as well as ideas borrowed from physics (e.g., dimensional interpolation) and new mesh-free adjoint methods for solving PDEs. This tutorial was written for normal engineers, who do not have nonlinear filters for breakfast.
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Cites
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Novel approach to nonlinear/non-Gaussian Bayesian state estimation
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A new method for the nonlinear transformation of means and covariances in filters and estimators
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Beyond the Kalman filter particle filters for tracking applications
Ristić, B., Arulampalam, S., & Gordon, N. (2004) · Cited by 3,372
Gaussian filters for nonlinear filtering problems
Ito, K., & Xiong, K. (2000) · IEEE Transactions on Automatic Control · doi:10.1109/9.855552 · Cited by 1,498
A survey of convergence results on particle filtering methods for practitioners
Crisan, D., & Doucet, A. (2002) · IEEE Transactions on Signal Processing · doi:10.1109/78.984773 · Cited by 919
Unscented Filtering for Spacecraft Attitude Estimation
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Cited by
Arasaratnam, I., & Haykin, S. (2009) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2009.2019800 · Cited by 3,380
Cubature Kalman Filtering for Continuous-Discrete Systems: Theory and Simulations
Arasaratnam, I., Haykin, S., & Hurd, T. R. (2010) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2010.2056923 · Cited by 580
Particle filtering and moving horizon estimation
Rawlings, J. B., & Bakshi, B. R. (2006) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2006.05.031 · Cited by 317
Zhu, J., Ge, Z., Song, Z., & Gao, F. (2018) · Annual Reviews in Control · doi:10.1016/j.arcontrol.2018.09.003 · Cited by 308
A Systematization of the Unscented Kalman Filter Theory
Menegaz, H. M. T., Ishihara, J. Y., Borges, G. A., & Vargas, A. N. (2015) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2015.2404511 · Cited by 278
Metadata
- Authors
- Daum, F.
- Venue
- IEEE Aerospace and Electronic Systems Magazine
- Published
- 2005-08
- Volume
- 20
- Issue
- 8
- Pages
- 57-69
- ISSN
- 0885-8985 (ISSN-L)
- OpenAlex
- W2105532434
- Cited by
- 673
- References
- 20
- FWCI
- 17.18
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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