Error-state Kalman Filter-Based Heave Motion Fusion Method of Unmanned Surface Vessel

This paper presents a novel heave motion fusion method based on the Inertial Measurement Unit (IMU) and Global Navigation Satellite System (GNSS). Firstly, velocity measurement is obtained via Time-differenced Carrier Phase (TDCP) technique. Then, by assigning the weight factors through the carrier-to-noise density ratio to control the velocity error at the millimeter level. Finally, this paper proposes an Error-state Kalman Filter (ESKF) method to integrate the accuracy velocity of GNSS into the heave motion estimation framework of IMU. Compared with the heave estimation based only on IMU, the proposed method omits the high-pass filtering of the heave velocity and only needs to high-pass filter the heave displacement. Results of the simulation are given to verify the availability and advantages of the proposed fusion method in Unmanned Surface Vessel (USV).