实验比较了双指数平滑和基于卡尔曼滤波的预测跟踪算法

IEEE Virtual Reality, 2003. Proceedings. Pub Date : 2003-03-22 DOI:10.1109/VR.2003.1191164

J. Laviola

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引用次数: 57

摘要

我们提出了一个实验，比较了双指数平滑和基于卡尔曼滤波的无导数测量模型预测跟踪算法。结果表明，双指数平滑器的运行速度提高了约135倍，预测性能相当。本文简要介绍了实验中使用的算法，并对实验结果进行了讨论。

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

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.

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来源期刊

IEEE Virtual Reality, 2003. Proceedings.

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