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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Xinguang Shao Zhonggai Zhao Fei Liu Biao Huang |
| Copyright Year | 2013 |
| Description | Author affiliation: Key Lab. of Adv. Process Control for Light Ind., Jiangnan Univ., Wuxi, China (Zhonggai Zhao; Fei Liu) || Dept. of Chem. & Mater. Eng., Univ. of Alberta, Edmonton, AB, Canada (Xinguang Shao; Biao Huang) |
| Abstract | Particle filters have become an increasingly useful tool for recursive Bayesian state estimation, especially for nonlinear and non-Gaussian problems. Despite the large number of papers published on particle filters in recent years, one issue that has not been addressed to any significant degree is the robustness. This paper presents a deterministic approach that has emerged in the area of robust filtering, and incorporates it into particle filtering framework. In particular, an ellipsoidal set membership approach is used to define a feasible set for particle sampling that contains the true state of the system, and makes the particle filter robust against unknown but bounded uncertainties. Simulation results show that the proposed algorithm is more robust than the regular particle filter and its variants such as the extended Kalman particle filter. |
| Starting Page | 568 |
| Ending Page | 573 |
| File Size | 433154 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781467347075 |
| ISSN | 19483449 |
| e-ISBN | 9781467347082 |
| DOI | 10.1109/ICCA.2013.6564932 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2013-06-12 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Ellipsoids Robustness Estimation Noise Bayes methods Monte Carlo methods Uncertainty |
| Content Type | Text |
| Resource Type | Article |
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