V3I7P3

Application of the Extended Kalman Filter in Motion State Estimation Using a New-Generation Inertial Sensor

Nguyen Ha Giang¹

Abstract

This paper presents a method for evaluating the performance of the Extended Kalman Filter (EKF) in estimating the motion states of ships operating in dynamic ocean wave conditions using the new-generation inertial sensor Xsens MTi-600. A six-degree-of-freedom dynamic model of the ship is constructed, taking into account the influence of random wave disturbances, sensor bias drift, and acceleration measurement noise. The EKF algorithm is designed to fuse information from gyroscopes and accelerometers in order to accurately estimate roll, pitch, and yaw angles and to correct bias errors over time. Simulation results show that EKF effectively compensates for sensor drift, significantly reduces high-frequency noise, and closely tracks the true state values even under strong wave oscillations. The evaluation results of the error spectra and attitude angles demonstrate that EKF operates stably and is well suited for orientation and stabilization applications of marine observation platforms.

Keywords:

Extended Kalman Filter, MEMS inertial sensor, state estimation, ship motion, Euler angles, random wave spectrum.