DocumentCode
3351139
Title
An experiment comparing double exponential smoothing and Kalman filter-based predictive tracking algorithms
Author
LaViola, Joseph J., Jr.
Author_Institution
Technol. Center for Adv. Sci. Comput. & Visualization, Brown Univ., Providence, RI, USA
fYear
2003
fDate
22-26 March 2003
Firstpage
283
Lastpage
284
Abstract
We present an experiment comparing double exponential smoothing and Kalman filter-based predictive tracking algorithms with derivative free measurement models. Our results show that the double exponential smoothers run approximately 135 times faster with equivalent prediction performance. The paper briefly describes the algorithms used in the experiment and discusses the results.
Keywords
Kalman filters; prediction theory; smoothing methods; Kalman filter; derivative free measurement models; double exponential smoothing; predictive tracking; time series; Equations; Interpolation; Kalman filters; Prediction algorithms; Predictive models; Quaternions; Scientific computing; Smoothing methods; Vectors; Visualization;
fLanguage
English
Publisher
ieee
Conference_Titel
Virtual Reality, 2003. Proceedings. IEEE
ISSN
1087-8270
Print_ISBN
0-7695-1882-6
Type
conf
DOI
10.1109/VR.2003.1191164
Filename
1191164
Link To Document