A numerical-integration perspective on Gaussian filters
A numerical-integration perspective on Gaussian filters
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DOI：
10.1109/tsp.2006.875389
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发表时间：
2006-08-01
影响因子：
5.4
通讯作者：
Hu, Xiaoping
中科院分区：
文献类型：
--
作者：
Wu, Yuanxin;Hu, Dewen;Hu, Xiaoping
本文提出了一个数值积分的角度来看高斯滤波器。高斯滤波器是贝叶斯推断的近似，高斯后验概率密度假设是有效的。在文献中，存在一种高斯滤波器的变体，它们来自非常不同的背景。从数值积分的角度来看，各种版本的高斯滤波器仅在其近似多重统计积分的特定处理方面彼此不同。第一次为分析和比较高斯滤波器的精度、效率和稳定性提供了一个共同的基础。该研究有助于在实际应用中选择合适的高斯滤波器，并通过采用更好的数值积分方法来设计更有效的滤波器。
This paper proposes a numerical-integration perspective on the Gaussian filters. A Gaussian filter is approximation of the Bayesian inference with the Gaussian posterior probability density assumption being Valid. There exists a variation of Gaussian filters in the literature that derived themselves from very different backgrounds. From the numerical-integration viewpoint, various versions of Gaussian filters are only distinctive from each other in their specific treatments of approximating the multiple statistical integrations. A common base is provided for the first time to analyze and compare Gaussian filters with respect to accuracy, efficiency and stability factor. This study is expected to facilitate the selection of appropriate Gaussian filters in practice and to help design more efficient filters by employing better numerical integration methods.