In this dissertation a combination of numerical and statistical methods are used to analyse and model
periodic breaking ocean wave radar data in a littoral coastal zone. Analysis, based primarily on
post-aligned parallel breaking waves, is considered for this research. Measurement data, obtained from
the CSIR, is used to parametrise the model variables. Returns are corrected against the incoming wave
angle, with respect to the radar, through a Radon transform analysis followed by a data alignment
correction step. This ensures that the periodic breaking waves are then parallel to the time axis. The
assumption is made that the fractional change in grazing angle has negligible effects on the clutter
statistics. Subsequently statistical analysis is performed on the angular corrected measured data.
Several different segments of a breaking wave are independently considered and analysed in terms of
their distribution and correlation properties. Statistical results show that the best fitting distribution is a
function of the sub-section of the sea wave and a function of the noise floor of the measuring radar.
This is in part due to the radar signal not being able to reflect between the troughs of an ocean wave,
because of the low grazing angle. Crest sections of an ocean wave tend to be distributed according to
Rayleigh and K-distributions.
After data processing, statistical parameters are obtained, which allows for the successful simulation of
statistically similar periodic breaking wave range-time clutter data. Angular data is captured for post
skewing of the approaching ocean waves. Statistical correlation times and distribution parameters are
analysed and recorded for use in the data generation process. Further datasets containing small rigid
inflatable boats crossing the sea wave crest are compared with pure sea clutter datasets and discussed.
Influences on the Radon transform and the resulting effects are also discussed.
Based on these models, methods and simulations, future work will include using the resultant model to
develop improved detection algorithms, which measure and detect small targets in periodic breaking
waves in littoral coastal zones. This could lead to integrating environmental wave structures, which may
be independently tracked and categorised, into the algorithm design and ultimately lead to improving
small vessel detection using low grazing angle coastal ground radar through classification.
Dissertation (MEng)--University of Pretoria, 2019.