基于旋转矩阵KF的低成本MEMS姿态解算

发布时间：2018-03-03 16:10

本文选题：姿态估计　切入点：卡尔曼滤波　出处：《测控技术》2016年02期 　论文类型：期刊论文

【摘要】：针对低成本MEMS器件组合的姿态检测系统在运动加速度干扰下姿态估计精度较差等问题,提出了一种基于旋转矩阵卡尔曼滤波器(KF)的姿态解算方法。为了克服四元数法观测方程为非线性的缺点,该方法以旋转矩阵部分元素建立状态方程,并对量测加速度采用状态反馈估计的运动加速度进行补偿,减小了外部加速度的干扰,然后通过构造水平观测向量降低了计算复杂度,并给出了量测噪声协方差的推导。最后设计了卡尔曼滤波器对量测信息实现融合。动静态测试表明,该方法消除了累计误差,与无迹卡尔曼滤波(UKF)相比,提高了在运动加速度干扰下的姿态估计精度。
[Abstract]:Aiming at the problem of poor precision in attitude motion acceleration interference estimation of low cost MEMS device integrated attitude detection system, proposes a rotation matrix Calman filter (KF) based on the attitude calculation method. In order to overcome the four element method for nonlinear measurement equation of the shortcomings, the method to establish the state equation of rotation matrix elements. And the measured acceleration the acceleration state feedback estimation compensation, reduce the interference of the external acceleration, then by constructing level observation vector reduces the computational complexity, and gives the derivation of measurement noise covariance matrix. Finally we design Calman filter to measure information fusion. The static and dynamic tests show that the method can eliminate the cumulative error, and no trace of Calman filter (UKF) compared to increase in the acceleration under the disturbance of the attitude estimation accuracy.

【作者单位】：华东理工大学信息科学与工程学院;
【分类号】：TH-39;TN713

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