Unscented卡尔曼滤波器的扩展对称采样策略

2009 IITA International Conference on Control, Automation and Systems Engineering (case 2009) Pub Date : 2009-07-11 DOI:10.1109/CASE.2009.52

Fuming Sun, Yonghong Ma, Jingli Wang

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引用次数: 3

摘要

本文研究了非线性动态系统的无气味卡尔曼滤波器。首先阐述了基于矩匹配法的UKF采样原理;然后，我们设计了一个扩展的对称采样策略，给定一个n维状态，该策略定义了4n+1个对称点，这些点位于轴上，以充分表示状态的均值和协方差。利用均方误差均方根的平均值比较了UKF和扩展对称UKF (EUKF)的性能。仿真结果表明，在强噪声条件下，EUKF的性能优于UKF，而标量k是影响UKF和EUKF的关键因素。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Extended Symmetric Sampling Strategy for Unscented Kalman Filter

This paper concerns the unscented Kalman filter (UKF) for the nonlinear dynamic systems. The sampling principle of UKF is firstly addressed, which is based on moment matching method. Then we designed an extended symmetric sampling strategy, given an n-dimensional state, which defines 4n+1 symmetric points that lie on axes to fully represent the mean and covariance of the state. The performance of the two UKFs, namely, the UKF and the extended symmetric UKF (EUKF), is compared by using the mean of the root of mean square error. The simulation results showed that EUKF outperforms the UKF in the presence of strong noise and the scalar k is a key factor involved in both UKFs.

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2009 IITA International Conference on Control, Automation and Systems Engineering (case 2009)

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