An Adaptive Kalman Filter Based Ocean Wave Prediction Model Using Motion Reference Unit Data

Abstract

Fleets like the ocean drilling platforms need to remain stationary relative to the bottom of the ocean, therefore the ship or platform need to pay close attention to the fluctuation of ocean currents. The analysis of the ocean waves is of great significance to the stability of the ocean operation platforms and the safety of the staffs onboard. An effective ocean current prediction model is helpful both economically and ecologically. The fluctuations in the ocean waves can be seen as a series of sinusoidal time series data with different frequencies and is usually captured by the sensors known as MRU (Motion Reference Unit). The study aims to analyze and accurately predict the movement of the ocean in the future time based on the historical movement of the ocean currents collected by MRU. All of these data have a fixed high resolution acquisition frequency. This research focuses on how to effectively fill in the missing values in the time series of MRU data. We also aim to accurately predict the future ocean wave. Therefore, an novel ARIMA (Autoregressive Integrated Moving Average) Model based missing data completion method is proposed to fill the data by artificial approximation of missing data. More importantly, an novel adaptive Kalman filter based Ocean Wave Prediction model is proposed to predict the ocean current in the near future by leveraging dynamic wave length. Experiment results validates the correctness of the ARIMA model based missing data completion method. The adaptive Kalman filter based Ocean Wave Prediction model is also shown to be effective by outperforming three base line prediction models.