Heave motion estimation based on the Cubature Kalman Filter combined with Fast Fourier Transform of acceleration autocorrelation

When a ship operates at sea, the heave motion, one of the six degrees of freedom (6-DOF) caused by waves, significantly affects personnel transfer, cargo lifting, and other operations. To mitigate these effects, compensating for the heave motion is essential. However, effective heave compensation relies on the heave displacement information, which can be estimated from the more easily measured heave acceleration. In the process of heave motion estimation, the key parameters are the main frequency components of the acceleration, which can be identified by Fast Fourier Transform (FFT) analysis. Conventional FFT spectrum analysis may lead to inaccurate frequency identification if the time window for FFT analysis is not an integer multiple of the actual signal period, which adversely affects the heave displacement estimation. This paper proposes a heave motion estimation method based on the Cubature Kalman Filter (CKF) combined with the FFT of acceleration autocorrelation. This method improves the accuracy of ship heave displacement estimation by more accurately identifying the time window for FFT analysis, which is an integer multiple of the least common multiple (LCM) of the acceleration components' periods. The proposed method's effectiveness is validated through both simulation and experimental results. Simulation results show that the proposed method reduces the MAE by 73.6 % for multi-frequency regular heave displacement, and by 6.4 % for irregular displacement, compared to the traditional method. In the experiments, the proposed method reduces the MAE for irregular heave displacement by 7.1 % compared to the traditional method. The results demonstrate that ship heave displacement estimation using autocorrelation analysis achieves higher accuracy. • A novel method based on CKF combined with FFT of acceleration autocorrelation is proposed to estimate heave displacement. • Autocorrelation combined with FFT improves accuracy in identifying main frequencies of acceleration signals. • Simulations and experiments confirm higher accuracy of the proposed method in displacement estimation.