Predicting sea waves in the presence of pink noise
Neural Networks (IJCNN), 2016 International Joint Conference
This is the author accepted manuscript. The final version is available from IEEE via the DOI in this record.
It has been shown that the power output of some wave energy converters can be greatly increased if they respond to very short-term predictions of the shapes of the waves. Observations of sea waves are traditionally made using buoys carrying GPS and accelerometers. The recent development of low-cost MEMS devices has led to cheaper devices, but also to a renewed interest in the effects of pink (1=f ) noise and signal processing methods for mitigating its effects. Bandpass filtering reduces the effects of this noise, but its remaining influence disrupts, in particular, the phase of the signal, which has significant consequences for prediction. We introduce a Bayesian model that promotes the smooth theoretical spectral shapes of the signal and the pink noise and estimates the true signal from one or more sets of observations recorded in parallel. The signal we are aiming to discover is the profile of sea waves at a fixed location; the spectral shape is determined by the Pierson- Moskowitz model. We demonstrate the model on synthetic data and give some preliminary results for the prediction of real sea waves
The author wishes to thank Dr John Duncan (DE&S, Ministry of Defence) for providing data used in section IV which were recorded during an NSRS exercise Golden Arrow in November 2014.
2016 International Joint Conference on Neural Networks (IJCNN), running together under the umbrella of WCCI 2016: IEEE World Congress on Computational Intelligence (24-29 July 2016) Vancouver, Canada.
2016 IEEE Congress on Evolutionary Computation (CEC), 24-29 July, pp. 2321-2328