张宁 Robust Attitude Estimation Using an Adaptive Unscented Kalman Filter
使用自适应无味卡尔曼滤波器进行姿态估计
链接：https://pan.baidu.com/s/1TNeRUK84APiwNv1uyQfhHg
提取码：pbdt
This paper presents the robust Adaptive unscented Kalman filter (RAUKF) for attitude estimation. Since the proposed algorithm represents attitude as a unit quaternion, all basic tools used, including the standard UKF, are adapted to the unit quaternion algebra. Additionally, the algorithm adopts an outlier detector algorithm to identify abrupt changes in the UKF innovation and an adaptive strategy based on covariance matching to tune the measurement covariance matrix online. Adaptation and outlier detection make the proposed algorithm robust to fast and slow perturbations such as magnetic field interference and linear accelerations. Experimental results with a manipulator robot suggest that our method overcomes other algorithms found in the literature.
本文介绍了用于姿态估计的鲁棒自适应无味卡尔曼滤波器（RAUKF）。由于所提出的算法将姿态表示为单位四元数，因此所使用的所有基本工具（包括标准UKF）都适用于单位四元数代数。此外，该算法采用离群值检测器算法来识别UKF创新中的突变，并采用基于协方差匹配的自适应策略在线调整测量协方差矩阵。自适应和离群值检测使所提出的算法对诸如磁场干扰和线性加速度之类的快速和慢速扰动具有鲁棒性。 机械手机器人的实验结果表明，我们的方法优于文献中发现的其他算法。