Statistical analyses of measured radar ground clutter data

The performance of ground-based surveillance radars strongly depends on the distribution and spectral characteristics of ground clutter To design sign al processing algorithms that exploit the knowledge of clutter characterist ics, a preliminary statistical analysis of ground-clutter data is necessary . We report the results of a statistical analysis of X-band ground-clutter data from the MIT Lincoln Laboratory Phase One program. Data non-Gaussianit y of the in-phase and quadrature components was revealed, first by means of histogram and moments analysis, and then by means of a Gaussianity test ba sed on cumulants of order higher than the second; to this purpose parametri c autoregressive (AR) modeling of the clutter process was developed. The te st is computationally attractive and has constant false alarm rate (CFAR). Incoherent analysis has also been carried out by checking the fitting to Ra yleigh, Weibull, log-normal, and K-distribution models. Finally, a new modi fied Kolmogorov-Smirnoff (KS) goodness-of-fit test is proposed; this modifi ed test guarantees good fitting in the distribution tails, which is of fund amental importance for a correct design of CFAR processors.