Sequence Unscented Kalman Filtering algorithm

Huiping Li, D. Xu, Jiang Jun, Fubin Zhang
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引用次数: 6

Abstract

Unscented Kalman Filter (UKF) has been proved to be a superior alternative to the extended Kalman filter (EKF) when solving the nonlinear system in recent years. In order to improve the real-time of the UKF, A new kind of UKF called Sequence UKF is proposed in this paper. Like Rao-Blackwellised Unscented Kalman Filter (RBUKF) [4], it also deals with nonlinear stochastic discrete-time system with linear measurement equation, however it can decrease the computational complexity with the same filtering accuracy. This algorithm reduces the measurement vector to scalars in measurement-update of UKF by sequence method, so it can avoid inversing the covariance of measurement and reduce a great mount of computation bound. Special algorithm is deduced in this paper. The high performance of sequence UKF is verified by using Monte Carlo simulations.
序列无气味卡尔曼滤波算法
近年来,无气味卡尔曼滤波器(Unscented Kalman Filter, UKF)在求解非线性系统时已被证明是一种优于扩展卡尔曼滤波器(extended Kalman Filter, EKF)的方法。为了提高UKF的实时性,本文提出了一种新的UKF——序列UKF。与Rao-Blackwellised Unscented Kalman Filter (RBUKF)[4]一样,它也处理具有线性测量方程的非线性随机离散系统,但它可以在相同滤波精度的情况下降低计算量。该算法采用序列法对UKF进行测量更新时,将测量向量简化为标量,避免了测量协方差的反演,大大减少了计算量。本文推导了一种特殊的算法。通过蒙特卡罗仿真验证了序列UKF的高性能。
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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