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Gaussian filters for nonlinear filtering problems
Abstract
We develop and analyze real-time and accurate filters for nonlinear filtering problems based on the Gaussian distributions. We present the systematic formulation of Gaussian filters and develop efficient and accurate numerical integration of the optimal filter. We also discuss the mixed Gaussian filters in which the conditional probability density is approximated by the sum of Gaussian distributions. A new update rule of weights for Gaussian sum filters is proposed. Our numerical tests demonstrate that new filters significantly improve the extended Kalman filter with no additional cost, and the new Gaussian sum filter has a nearly optimal performance.
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Citations
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
Cited by
Arasaratnam, I., & Haykin, S. (2009) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2009.2019800 · Cited by 3,380
Kalman Filtering and Neural Networks
(2001) · Wiley eBooks · doi:10.1002/0471221546 · Cited by 2,386
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
Applied Stochastic Differential Equations
Särkkä, S., & Solin, A. (2019) · doi:10.1017/9781108186735 · Cited by 326
Advances in intelligent and autonomous navigation systems for small UAS
Bijjahalli, S., Sabatini, R., & Gardi, A. (2020) · Progress in Aerospace Sciences · doi:10.1016/j.paerosci.2020.100617 · Cited by 123
Metadata
- Authors
- Ito, K., & Xiong, K.
- Venue
- IEEE Transactions on Automatic Control
- Published
- 2000-05
- Volume
- 45
- Issue
- 5
- Pages
- 910-927
- ISSN
- 0018-9286 (ISSN-L)
- OpenAlex
- W2099371695
- Cited by
- 1,498
- References
- 1
- FWCI
- 8.75
- Type
- journal article
- Language
- en
- Open access
- closed
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