Liu Ning, Qi Wenhao, Su Zhong, Feng Qunzhuo, Yuan Chaojie
Beijing Key Laboratory of High Dynamic Navigation Technology, Beijing Information Science & Technological University, Beijing 100101, China.
Micromachines (Basel). 2022 Aug 9;13(8):1283. doi: 10.3390/mi13081283.
Aiming at the problem of the weak dynamic performance of the gradient descent method in the attitude and heading reference system, the susceptibility to the interference of accelerometers and magnetometers, and the complex calculation of the nonlinear Kalman Filter method, an extended Kalman filter suitable for a low-cost magnetic, angular rate, and gravity (MARG) sensor system is proposed. The method proposed in this paper is a combination of a two-stage gradient descent algorithm and the extended Kalman filter (GDEKF). First, the accelerometer and magnetometer are used to correct the attitude angle according to the two-stage gradient descent algorithm. The obtained attitude quaternion is combined with the gyroscope measurement value as the observation vector of EKF and the calculated attitude of the gyroscope and the bias of the gyroscope are corrected. The elimination of the bias of the gyroscope can further improve the stability of the attitude observation results. Finally, the MARG sensor system was designed for mathematical model simulation and hardware-in-the-loop simulation to verify the performance of the filter. The results show that compared with the gradient descent method, it has better anti-interference performance and dynamic performance, and better measurement accuracy than the extended Kalman filter.
针对梯度下降法在姿态航向参考系统中动态性能较弱、易受加速度计和磁力计干扰以及非线性卡尔曼滤波法计算复杂等问题，提出了一种适用于低成本磁、角速率和重力（MARG）传感器系统的扩展卡尔曼滤波器。本文提出的方法是一种两级梯度下降算法与扩展卡尔曼滤波器（GDEKF）的组合。首先，利用加速度计和磁力计根据两级梯度下降算法校正姿态角。将得到的姿态四元数与陀螺仪测量值相结合，作为扩展卡尔曼滤波器的观测向量，并对计算得到的陀螺仪姿态和陀螺仪偏差进行校正。消除陀螺仪偏差可以进一步提高姿态观测结果的稳定性。最后，对MARG传感器系统进行了数学模型仿真和硬件在环仿真，以验证滤波器的性能。结果表明，与梯度下降法相比，它具有更好的抗干扰性能和动态性能，并且比扩展卡尔曼滤波器具有更好的测量精度。
