Novel quaternion Kalman filter
Novel quaternion Kalman filter
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DOI：
10.1109/taes.2006.1603413
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发表时间：
2006-01-01
影响因子：
4.4
通讯作者：
Oshman, Y
中科院分区：
文献类型：
--
作者：
Choukroun, D;Bar-Itzhack, IY;Oshman, Y
本文提出了一种新的卡尔曼滤波器（KF），用于估计姿态四元数以及陀螺随机漂移的矢量测量。对测量方程进行特殊处理，得到一个线性伪测量方程，其误差与状态有关。由于四元数运动学方程是线性的，因此两者的组合产生线性KF，其消除了通常的线性化过程并且对初始估计误差不太敏感。给出了系统状态相关噪声协方差矩阵的一般精确表达式。此外，分析显示如何有效地计算这些协方差矩阵。一个自适应版本的过滤器也被开发来处理建模误差的动态系统噪声统计。进行了蒙特-卡罗模拟，证明了两个版本的过滤器的效率。在高初始估计误差的特定情况下，典型的扩展卡尔曼滤波器（EKF）无法收敛，而所提出的滤波器成功。
This paper presents a novel Kalman filter (KF) for estimating the attitude-quaternion as well as gyro random drifts from vector measurements. Employing a special manipulation on the measurement equation results in a linear pseudo-measurement equation whose error is state-dependent. Because the quaternion kinematics equation is linear, the combination of the two yields a linear KF that eliminates the usual linearization procedure and is less sensitive to initial estimation errors. General accurate expressions for the covariance matrices of the system state-dependent noises are developed. In addition, an analysis shows how to compute these covariance matrices efficiently. An adaptive version of the filter is also developed to handle modeling errors of the dynamic system noise statistics. Monte-Carlo simulations are carried out that demonstrate the efficiency of both versions of the filter. In the particular case of high initial estimation errors, a typical extended Kalman filter (EKF) fails to converge whereas the proposed filter succeeds.