ML Anthology

A General Filter for Measurements with Any Probability Distribution

Yoav Rosenberg, Michael Werman
CVPR 1997 pp. 654-659
doi:10.1109/CVPR.1997.609395 /cvpr/1997/rosenberg1997cvpr-general/

Abstract

The Kalman filter is a very efficient optimal filter, however it has the precondition that the noises of the process and of the measurement are Gaussian. The authors introduce 'the general distribution filter' which is an optimal filter that can be used even where the distributions are not Gaussian. An efficient practical implementation of the filter is possible where the distributions are discrete and compact or can be approximated as such.

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Cite

Text

Rosenberg and Werman. "A General Filter for Measurements with Any Probability Distribution." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1997. doi:10.1109/CVPR.1997.609395

Markdown

[Rosenberg and Werman. "A General Filter for Measurements with Any Probability Distribution." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1997.](https://mlanthology.org/cvpr/1997/rosenberg1997cvpr-general/) doi:10.1109/CVPR.1997.609395

BibTeX

@inproceedings{rosenberg1997cvpr-general,
  title     = {{A General Filter for Measurements with Any Probability Distribution}},
  author    = {Rosenberg, Yoav and Werman, Michael},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {1997},
  pages     = {654-659},
  doi       = {10.1109/CVPR.1997.609395},
  url       = {https://mlanthology.org/cvpr/1997/rosenberg1997cvpr-general/}
}