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Statistical analyses of measured radar ground clutter data
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资源说明：The performance of ground-based surveillance radars strongly
depends on the distribution and spectral characteristics of ground
clutter. To design signal processing algorithms that exploit the
knowledge of clutter characteristics, 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-Gaussianity 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 based on
cumulants of order higher than the second; to this purpose
parametric autoregressive (AR) modeling of the clutter process
was developed. The test is computationally attractive and has
constant false alarm rate (CFAR). Incoherent analysis has also
been carried out by checking the fitting to Rayleigh, Weibull,
log-normal, and K-distribution models. Finally, a new modified
Kolmogorov—Smirnoff (KS) goodness-of-fit test is proposed; this
modified test guarantees good fitting in the distribution tails,
which is of fundamental importance for a correct design of CFAR
processors.
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