Title :
A general filter for measurements with any probability distribution
Author :
Rosenberg, Yoav ; Werman, Michael
Author_Institution :
Inst. of Comput. Sci., Hebrew Univ., Jerusalem, Israel
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
Keywords :
adaptive filters; filtering theory; measurement theory; probability; compact distributions; discrete distributions; general distribution filter; optimal filter; probability distribution; Computer science; Current measurement; Gaussian noise; Information filtering; Information filters; Noise measurement; Probability distribution; State estimation; Time measurement; Vehicles;
Conference_Titel :
Computer Vision and Pattern Recognition, 1997. Proceedings., 1997 IEEE Computer Society Conference on
Conference_Location :
San Juan
Print_ISBN :
0-8186-7822-4
DOI :
10.1109/CVPR.1997.609395
