This paper presents a robust orientation estimation algorithm by using magnetic, angular rate, and gravity sensors. The robustness is achieved by two online methods: compensation of hard iron effect for the magnetometer and separation of the accelerometer signals into gravity projections and linear accelerations. Further, direct cosine matrix is used for the rotation matrix relative to the north, east, down frame. The fusion equations are solved using state-constrained Kalman filter. The simulation and experiment results show the effectiveness of the proposed algorithm in estimating the orientation under acceleration and hard iron influence.