Abstract
Probabilistic models have been used extensively in the past to underpin classification algorithms in statistical pattern recognition. The most widely used model is the Gaussian distribution. However, signals of impulsive nature usually deviate from Gaussian and it is necessary to work with more realistic models. K-distribution is one of the long-tailed density which is known in the signal processing community for fitting the radar sea clutter accurately. The work presented in this thesis reflects the efforts made to model the background features, extracted from the sea images, by using a K-distribution. A novel approach for estimating the parameter of K-distribution is presented. The method utilises the empirical characteristic function, and is proven to perform better than any existing estimation technique. A classifier is then developed from the empirical characteristic function. This technique is applied to a problem of automatic target recognition with promising results.