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Nonlinear filtering update phase via the Single Point Truncated Unscented Kalman filter

机译:通过单点截断无味卡尔曼滤波器进行非线性滤波更新阶段

摘要

A fast algorithm to approximate the first two moments of the posterior probability density function (pdf) in nonlinear non-Gaussian Bayesian filtering is proposed. If the pdf of the measurement noise has a bounded support and the measurement function is continuous and bijective, we can use a modified prior pdf that meets Bayes' rule exactly. The central idea of this paper is that a Kalman filter applied to a modified prior distribution can improve the estimate given by the conventional Kalman filter. In practice, bounded support is not required and the modification of the prior is accounted for by adding an extra-point to the set of sigma-points used by the unscented Kalman filter.
机译:提出了一种快速估计非线性非高斯贝叶斯滤波中后验概率密度函数(pdf)的前两个矩的算法。如果测量噪声的pdf具有有限的支持,并且测量函数是连续的且是双射的,则可以使用完全符合贝叶斯规则的修改后的pdf。本文的中心思想是,将卡尔曼滤波器应用于修正的先验分布可以改善常规卡尔曼滤波器给出的估计。在实践中,不需要有界支持,并且对先验的修改是通过在无味卡尔曼滤波器使用的sigma-points集合中添加一个额外的点来解决的。

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