An Adaptive Robust Cubature Kalman Filter Based on Sage-Husa Estimator for Improving Ship Heave Measurement Accuracy

High-pass and Kalman filtering are two digital filtering methods for ship heave acceleration signals. However, the traditional Kalman method has dynamic model errors due to the dependence on the fast Fourier transform (FFT) identification model. The presence of factors such as unknown statistical properties of the noise and anomalous measurement information at the same time can cause degradation in the accuracy of the heave solution or even lead to Kalman filter divergence. This study proposes an adaptive robust cubature Kalman filter based on the Sage-Husa noise estimator (SARCKF) for ship heave motion estimation to obtain higher heave measurement accuracy. Unlike the traditional method, which adjusts the process covariance matrix by prior knowledge, the new way estimates the statistical characteristics of noise online by Sage estimator, improving the real-time filtering performance. The Huber robust method is used to correct the measurement covariance matrix to suppress the outliers in the measurement information. Then, based on the strong tracking theory, an adaptive factor is derived and combined with the Huber method to form a new cost function, effectively compensating for the model error. The simulation results show that SARCKF can still make the estimation accuracy of heave displacement reach 5% of the maximum amplitude when the statistical characteristics of noise are unknown and the model has errors. In addition, the proposed method has stronger robustness than the traditional strong tracking filter (STF).