An Introduction to the Kalman Filter

An Introduction to the Kalman Filter (1995)

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by Greg Welch , Gary Bishop

Venue:	UNIVERSITY OF NORTH CAROLINA AT CHAPEL HILL
Citations:	445 - 12 self

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Datum	Value	Source
TITLE	An Introduction to the Kalman Filter	INFERENCE
AUTHOR NAME	Greg Welch	user correction
AUTHOR AFFIL	Department of Computer Science; University of North Carolina at Chapel Hill	user correction
AUTHOR ADDR	CB 3175, 236 Frederick P. Brooks, Jr. Building, Chapel Hill, NC 27599-3175	user correction
AUTHOR NAME	Gary Bishop	user correction
AUTHOR AFFIL	Department of Computer Science; University of North Carolina at Chapel Hill	user correction
AUTHOR ADDR	CB 3175, 255 Sitterson Hall, Chapel Hill, NC 27599-3175	user correction
ABSTRACT	In 1960, R.E. Kalman published his famous paper describing a recursive solution to the discrete-data linear filtering problem. Since that time, due in large part to advances in digital computing, the Kalman filter has been the subject of extensive research and application, particularly in the area of autonomous or assisted navigation. The Kalman filter is a set of mathematical equations that provides an efficient computational (recursive) means to estimate the state of a process, in a way that minimizes the mean of the squared error. The filter is very powerful in several aspects: it supports estimations of past, present, and even future states, and it can do so even when the precise nature of the modeled system is unknown. The purpose of this paper is to provide a practical introduction to the discrete Kalman filter. This introduction includes a description and some discussion of the basic discrete Kalman filter, a derivation, description and some discussion of the extended Kalman filter, and a relatively simple (tangible) example with real numbers & results.	user correction
YEAR	1995	INFERENCE
VENUE	UNIVERSITY OF NORTH CAROLINA AT CHAPEL HILL	user correction
VENUE TYPE	Technical Report	user correction
TECH	TR 95-041	user correction
CITATIONS	10 found	ParsCit 1.0