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 language | English |
|---|---|
| Pages (from-to) | 654-659 |
| Number of pages | 6 |
| Journal | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition |
| State | Published - 1997 |
| Event | Proceedings of the 1997 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - San Juan, PR, USA Duration: 17 Jun 1997 → 19 Jun 1997 |