Refined autoregressive moving average modeling of underwater heave process

Earlier treatments of the underwater dynamic motion effects including the heave, or vertical dynamic motion, phenomenon relied on frequency response methods and Kalman filtering for the compensation task. An alternative model of the heave process is proposed. The model is based on transforming the underlying time series using exponential operations and then finding autoregressive integrated moving-average (ARIMA) representations of the time series. A refined ARMA model based on modeling of a series of innovations is also proposed. A computational comparison of the performance of two estimators is conducted using a real heave record as a base case. The refined ARMA model gives better results than the other alternative models investigated.<
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