HMM Based Adaptive Kalman Filter for Orientation Estimation

Abstract

An adaptive orientation estimation method based on hidden Markov model (HMM) using multiple Magnetic Angular Rate Gravity (MARG) sensor is proposed to address the problem of linear acceleration and ferromagnetic disturbance. Two parallel Kalman filters containing first-order Gauss-Markov disturbance models are implemented to estimate the disturbances before estimating the orientation. It is important that the noise covariance of the disturbance model should match the magnitude of the disturbance to ensure the estimation performance; otherwise, the disturbance estimation accuracy will reduce significantly. Therefore, an adaptive Kalman filter based on HMM is designed to adjust the disturbance noise covariance. After the disturbances are separated, the estimated gravity acceleration and geomagnetic field as well as the gyroscope measurement are taken as the inputs, and the orientation estimation procedure is established by using the multiplicative extended Kalman filter (MEKF). Experiments show that the proposed method achieves a higher level of accuracy in comparison with other orientation estimation methods under various conditions.