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Sigma-point Kalman filtering for integrated GPS and inertial navigation

机译：用于集成GPS和惯性导航的Sigma-point Kalman滤波

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摘要

A sigma-point Kalman filter is derived for integrating GPS measurements with inertial measurements from gyros and accelerometers to determine both the position and the attitude of a moving vehicle. Sigma-point filters use a carefully selected set of sample points to more accurately map the probability distribution than the linearization of the standard extended Kalman filter (KKF), leading to faster convergence from inaccurate initial conditions in position/attitude estimation problems. The filter formulation is based on standard inertial navigation equations. The global attitude parameterization is given by a quaternion, while a generalized three-dimensional attitude representation is used to define the local attitude error. A multiplicative quaternion-error approach is used to guarantee that quaternion normalization is maintained in the filter. Simulation and experimental results are shown to compare the performance of the sigma-point filter with a standard EKF approach.
机译：派生了一个sigma-point Kalman滤波器，用于将GPS测量值与陀螺仪和加速度计的惯性测量值结合在一起，以确定行驶中的车辆的位置和姿态。与标准扩展卡尔曼滤波器（KKF）的线性化相比，西格玛点滤波器使用经过精心选择的一组采样点来更准确地映射概率分布，从而可以从位置/姿态估计问题中不准确的初始条件更快地收敛。滤波器公式基于标准惯性导航方程式。全局姿态参数化由四元数给出，而广义的三维姿态表示用于定义局部姿态误差。使用乘法四元数误差方法来确保在滤波器中保持四元数归一化。仿真和实验结果表明，它可以将sigma-point滤波器的性能与标准EKF方法进行比较。

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