An experiment comparing double exponential smoothing and Kalman filter-based predictive tracking algorithms

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.