Predicting the sea surface from high resolution multi-beam FMCW radar data

Abstract

In this thesis a deterministic wave model is used to reconstruct and predict the sea surface motion from FMCW (Frequency Modulated Continuous Wave) radar data, produced by Radac. The deterministic model that is used to do this is based on the linear wave theory. The radar is looking horizontally straight towards the waves in 5 separate beam directions of -40,-20,0, 20 and 40 degrees. Using the FMCW principle the backscatterd signal is converted into velocity and spatial range information. After some compensations (current for example) this velocity data can be treated as horizontal component of the orbital velocity of the wave. By using a least-squares solving approach (the trust-region reflective algorithm) on these orbital velocities and the expression that holds for them in the linear wave theory the model can be fitted to the measurements. The result of the least squares solver consists of a set of parameters for wave amplitude, phase and frequency. With these parameters the deterministic motion of the sea surface can be computed. This method is tested using artificial data and a generalized one directional case (using information from 1 beam under assumption of infinitely long-crested waves). For the experiments with artificial data consisting of waves with Hs = 2 meters (significant waveheight) the results are promising. A prediction time of 30 seconds over a range of 150 meters with an average error of 15 cm in the one directional model (fitted on 10 second data over 384 meters) can be achieved. For the multi directional model this lies between 20 and 30 seconds with an average error of 25 cm, depending on the spreading of the waves. Experiments with real data show less impressive results, an accurate reconstruction of the surface can be given, but the predictive capability is very limited.