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
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;
Conference_Titel :
Virtual Reality, 2003. Proceedings. IEEE
Print_ISBN :
0-7695-1882-6
DOI :
10.1109/VR.2003.1191164
