General filter for measurements with any probability distribution

Yoav Rosenberg*, Michael Werman

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

11 Scopus citations

Abstract

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. In this paper we 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.

Original languageAmerican English
Pages (from-to)654-659
Number of pages6
JournalProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
StatePublished - 1997
EventProceedings of the 1997 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - San Juan, PR, USA
Duration: 17 Jun 199719 Jun 1997

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