The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
A Student's t filter for heavy tailed process and measurement noise
Open accessAbstract
We consider the filtering problem in linear state space models with heavy tailed process and measurement noise. Our work is based on Student's t distribution, for which we give a number of useful results. The derived filtering algorithm is a generalization of the ubiquitous Kalman filter, and reduces to it as special case. Both Kalman filter and the new algorithm are compared on a challenging tracking example where a maneuvering target is observed in clutter.
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Citations
Cites
Estimation with Applications to Tracking and Navigation: Theory, Algorithms and Software
Bar‐Shalom, Y., Li, X. ‐., & Kirubarajan, T. (2002) · doi:10.1002/0471221279 · Cited by 6,782
Cited by
A Novel Adaptive Kalman Filter With Inaccurate Process and Measurement Noise Covariance Matrices
Huang, Y., Zhang, Y., Wu, Z., Li, N., & Chambers, J. (2018) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2017.2730480 · Cited by 731
Metadata
- Authors
- Roth, M., Ozkan, E., & Gustafsson, F.
- Venue
- 2013 IEEE International Conference on Acoustics, Speech and Signal Processing
- Publisher
- IEEE
- Published
- 2013-05
- Pages
- 5770-5774
- ISSN
- 1520-6149 (ISSN-L)
- OpenAlex
- W2062100941
- Cited by
- 229
- References
- 1
- FWCI
- 2.38
- Type
- conference paper
- Language
- en
- Open access
- green
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- Roth, M., Ozkan, E., & Gustafsson, F. (2013). A Student's t filter for heavy tailed process and measurement noise. 2013 IEEE International Conference on Acoustics, Speech and Signal Processing. https://doi.org/10.1109/icassp.2013.6638770
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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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