The nine-axis MEMS inertial measurement units (IMUs) have been widely used in various fields, such as underwater vehicles, unmanned aerial vehicles, and bionic robots. Due to the noises of gyroscope sensors and errors introduced in the solution process, the rotation angles estimated using only angular velocity data usually contain large accumulated errors and have to be corrected by acceleration and geomagnetic measurements. A serious problem is if there is a strong magnetic anomaly field in the environment, the geomagnetic field aiding performance decreases quickly and probably leads to extra errors. To improve the heading and attitude estimation accuracy of the nine-axis MEMS IMU in the magnetic anomaly field, a partially adaptive extended Kalman filter (PADEKF) using double quaternions is proposed in this work. To reduce the coupled influence of magnetic measurement noise on attitude estimation in a single quaternion, the heading and attitude angles are represented with two independent quaternions in the state vector. Self-adaptability design is adopted in the extended Kalman filter (EKF) to improve the robustness of spatially varying magnetic anomaly data. For the case that the strong and quickly varying magnetic anomaly field cannot be well modeled by the PADEKF, a combination algorithm of long and short-term memory (LSTM) neural network and the Runge–Kutta method is given to make good heading estimation. Field experiments in different scenarios are performed and verified the effectiveness of the proposed approach.

中文翻译：

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使用部分自适应解耦扩展卡尔曼滤波器和 LSTM 算法对磁异常场中的 MEMS IMU 进行鲁棒航向和姿态估计

九轴MEMS惯性测量单元（IMU）已广泛应用于水下航行器、无人机、仿生机器人等各个领域。由于陀螺仪传感器的噪声和求解过程中引入的误差，仅使用角速度数据估计的旋转角度通常包含较大的累积误差，并且必须通过加速度和地磁测量来校正。一个严重的问题是，如果环境中存在强磁异常场，地磁场辅助性能会迅速下降，并可能导致额外的误差。为了提高磁异常场中九轴MEMS IMU的航向和姿态估计精度，本文提出了一种使用双四元数的部分自适应扩展卡尔曼滤波器（PADEKF）。为了减少磁测量噪声对单个四元数姿态估计的耦合影响，航向角和姿态角在状态向量中用两个独立的四元数表示。扩展卡尔曼滤波器（EKF）采用自适应设计，提高空间变化磁异常数据的鲁棒性。针对PADEKF无法很好地模拟强且快速变化的磁异常场的情况，给出了长短期记忆（LSTM）神经网络和龙格-库塔方法的组合算法来做出良好的航向估计。在不同场景下进行了现场实验，验证了所提方法的有效性。