论文标题
在复杂条件下具有可变自适应方法的智能Quaternion SVDCKF AHR估计
An Intelligent Quaternion SVDCKF AHRS Estimation with Variable Adaptive Methods in Complex Conditions
论文作者
论文摘要
本文提出了与可变自适应方法（VAM）相结合的智能奇异值分解kalman滤波器（SVDCKF），旨在解决小UAV的复杂和动态条件中的态度和标题参考系统（AHR）。考虑到季度AHRS模型的非线性和状态协方差矩阵的非阳性确定性，SVDCKF算法均使用SVD和CKF呈现，以便更好地获得滤波器的准确性和可靠性。此外，在复杂的飞行条件下导致的加速度计测量值中值的变化不同。因此，VAM旨在处理加速度的三轴值，并智能调整测量噪声矩阵ra。此外，在特殊情况下是否使用加速度的三轴值来计算磁力计的三轴值的标题测量值。仿真和实验结果表明，所提出的滤波器算法比互补过滤器（CF）和误差状态卡尔曼滤波器（ESKF）具有更出色的态度解决方案的精度和鲁棒性。
Aimed at solving the problem of Attitude and Heading Reference System(AHRS) in the complex and dynamic conditions for small-UAV, An intelligent Singular Value Decomposition Cubature Kalman Filter(SVDCKF) combined with the Variable Adaptive Methods(VAM) is proposed in this paper. Considering the nonlinearity of quaternion AHRS model and non-positive definite of the state covariance matrix, the SVDCKF algorithm is presented with both the SVD and CKF in order to better obtain the filter accuracy and reliability. Additionally, there are the different changes of the values in the accelerometer measurement resulting from the complex flying conditions. Thus, the VAM is designed to deal with three-axis values of the acceleration and tune intelligently the measurement noise matrix Ra. Moreover, the heading measurement from the three-axis values of the magnetometer is calculated according to the whether to use the three-axis values of the acceleration in the special situations. The simulation and experiment results demonstrate that the proposed filter algorithm has the more excellent attitude solution accuracy and robustness than both the Complementary Filter(CF) and the Error State Kalman Filter(ESKF).
