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Author Rosenberg, Yoav ♦ Werman, Michael
Source CiteSeerX
Content type Text
Publisher IEEE Computer Society Press
File Format PDF
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Optimal Filter ♦ Efficient Optimal Filter ♦ Kalman Filter ♦ Process State ♦ Efficient Practical Implementation ♦ General Distribution Filter ♦ General Filter ♦ Probability Distribution
Description 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. 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. 1. Definition of the problem The notion of filtering is connected with that of a process. The process state at time t is described by a vector, which is unknown and must be computed. An example of such a process is a moving vehicle, where the state is the vehicle's position and speed. The information about the process comes from measurements, where the connection between the process and the measurement is known. Usually there is noise in the system, so that the measurement is described by its probability distribution. In order to estimate ...
In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article
Publisher Date 1997-01-01