Texture modelling, estimation and validation using measured sea clutter data

The authors consider modelling and estimating the texture in high resolution nonGaussian sea clutter. Cyclostationarity of sea clutter is investigated and validated by processing measured high-resolution data. The clutter is modelled as a compound Gaussian process and the texture as the superposition of real cosines with unknown frequencies, amplitudes and phases. A method is proposed for estimating the model parameters and retrieving the texture component from the intensity data in the presence of multiplicative noise with unknown power spectral density. The method exploits the clutter cyclostationarity and is based on a RELAXation optimisation approach. The ability of the proposed method to retrieve texture information is investigated by processing simulated and measured sea clutter data