Robust Heading and Attitude Estimation of MEMS IMU in Magnetic Anomaly Field Using a Partially Adaptive Decoupled Extended Kalman Filter and LSTM Algorithm

• Published in: IEEE transactions on instrumentation and measurement 2024, Vol.73, p.1-13
• Main Authors: Li, Hui, Chang, Shuai, Yao, Qi, Wan, Chengcheng, Zou, Guo-Ji, Zhang, Da-Long
• Format: Article
• Language: English
• Subjects: Acceleration; Algorithms; Angular velocity; Attitudes; Bionics; Errors; Estimation; Extended Kalman filter; Extended Kalman filter (EKF); Geomagnetic field; Geomagnetism; Inertial platforms; Kalman filters; Long short term memory; long short-term memory (LSTM); Magnetic anomalies; magnetic anomaly data; Magnetic field measurement; Magnetic measurement; Magnetic separation; Micromechanical devices; Neural networks; nine-axis MEMS inertial measurement unit (IMU); Noise measurement; partially adaptive; Quaternions; Runge-Kutta method; State vectors; Underwater vehicles; Unmanned aerial vehicles
• ISSN: 0018-9456; 1557-9662
• DOI: 10.1109/TIM.2024.3381659

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.