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

Abstract

The nine-axis MEMS Inertial Measurement Units (IMU) 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 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 strong magnetic anomaly field in the environment, the geomagnetic field aiding performance decrease quickly and probably leads to extra errors. To improve the heading and attitude estimation accuracy of the nine-axis MEMS IMU in 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 EKF to improve the robustness to spatially varying magnetic anomaly data. For the case that the strong and quickly varying magnetic anomaly field cannot be well modelled by the PADEKF, a combination algorithm of long and short-term memory (LSTM) neural network and 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.