To improve the accuracy of attitude and heading reference systems for moving vehicles, an effective orientation estimation method is proposed. The method uses an odometer, a low-cost magnetic, angular rate, and gravity sensor. This study addresses the problems of non-orthogonal error, carrier magnetic field interference and calibration to obtain accurate, long-term, stable magnetic field strength. A neural network fusion 12-parameter ellipse fitting method is proposed to eliminate the soft magnetic field and hard magnetic field interference. The interference to the accelerometer from linear acceleration is eliminated by using an odometer and a gyroscope, and the high-frequency noise from the accelerometer is eliminated by using a low-pass filter. An improved method to evaluate vehicle attitude is proposed and utilized to compensate for filtered accelerometer measurement when the vehicle is moving at a uniform, accelerate and steering state. The proposed method uses an effective adaptive Kalman filter based on the error state model to reduce dynamic perturbations. Filter gain is adaptively tuned under different moving modes by adjusting the noise matrix. The effectiveness of the algorithm is verified by experiments and simulations in multiple operating conditions.

中文翻译：

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基于多传感器信息融合的车辆姿态航向参考系统研究

为了提高移动车辆姿态航向参考系统的精度，提出了一种有效的方向估计方法。该方法使用里程表、低成本的磁性、角速度和重力传感器。该研究解决了非正交误差、载波磁场干扰和校准问题，以获得准确、长期、稳定的磁场强度。提出了一种神经网络融合12参数椭圆拟合方法来消除软磁场和硬磁场干扰。采用里程计和陀螺仪消除线加速度对加速度计的干扰，采用低通滤波器消除加速度计的高频噪声。提出了一种改进的车辆姿态评估方法，用于补偿车辆在匀速、加速和转向状态下移动时滤波后的加速度计测量值。所提出的方法使用基于误差状态模型的有效自适应卡尔曼滤波器来减少动态扰动。通过调整噪声矩阵，在不同的运动模式下自适应地调整滤波器增益。通过在多种工况下的实验和仿真验证了算法的有效性。