Adaptive-Gain Regulation of Extended Kalman Filter for Use in Inertial and Magnetic Units Based on Hidden Markov Model

Abstract

Magnetic and inertial measurement units (MIMUs) are promising tools for attitude tracking of moving bodies without location restriction. An extended Kalman filter (EKF) is a commonly used attitude algorithm for MIMUs, and its Kalman gain is usually regulated according to the measurements of the accelerometer for the best integrated performance, i.e., the best performance for both when the carrier is motionless and when the carrier is moving. A hidden Markov model (HMM) is introduced, and then trained using static measurements of the accelerometer. Once the body has a movement, the match probability between the dynamic measurements of the accelerometer and the trained HMM will decrease, which is then used for the timely regulation of the Kalman gain to make the EKF rely more on the measurements of the gyroscope for attitude calculation. A slight revision to the introduced HMM is given for the improvement of the smoothness of the outputs of the HMM when the carrier is motionless. A relationship between the Kalman gain and the output of the HMM is also given, and the value range of the outputs of the HMM is readjusted in order to fit that relationship. Six competitive methods are compared with our method, and simulation and experiment tests validate the superiority of our method.