An Experiment Comparing Double Exponential Smoothing and Kalman Filter-Based Predictive Tracking Algorithms

Authors:
Joseph J. LaViola Jr.
Affiliations:
-
Venue:
VR '03 Proceedings of the IEEE Virtual Reality 2003
Year:
2003

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Abstract

We present an experiment comparing double exponentialsmoothing and Kalman filter-based predictive trackingalgorithms with derivative free measurement models. Ourresults show that the double exponential smoothers run approximately135 times faster with equivalent prediction performance.The paper briefly describes the algorithms usedin the experiment and discusses the results.