General class of non-Gaussian coherent clutter models

The author proposes a scheme to treat radar clutter as a non-Gaussian process. The approach is based on the concept of a fluctuating number of Gaussian scatterers. These scatterers can be of different types and may be grouped into classes on the basis of common statistical properties such as their covariance matrix and their number distribution functions (DFs). Using this idea, general expressions for the in-phase (I) and quadrature (Q) time-sampled joint multivariate probability density functions (PDF) for clutter and clutter plus Ricean target are first obtained and analysed. Starting with these general expressions, corresponding univariate PDFs for the amplitude squared or radar cross-section (RCS) are also obtained. Next, a simple example of one class of fluctuating scatterers plus a Gaussian background is considered in greater detail. In this model a negative binomial number DF is used to describe the fluctuations in the number of scatterers. The resulting PDF of the clutter RCS is a four-parameter function which under certain conditions is reduced to the K-PDF. The parameters are then expressed in terms of the first four moments of the PDF, and using some highly non-Gaussian sea clutter data the model is shown to fit the data better than other popular models. The paper concludes with a discussion of the merits of the model and of possible extensions