We address the problem of detection of targets obscured by a forest canopy
using an ultrawideband (UWB) radar. The forest clutter observed in the rada
r imagery is a highly impulsive random process that is more accurately mode
led with the recently proposed class of alpha-stable processes as compared
with Gaussian, Weibull, and K-distribution models. With this more accurate
model, segmentation is performed on the imagery into forest and clear regio
ns. Further, a region-adaptive symmetric alpha stable (S alpha S) constant
false-alarm rate (CFAR) detector Is introduced and its performance is compa
red with the Weibull and Gaussian CFAR detectors. The results on real data
show that the S alpha S CFAR performs better than the Weibull and Gaussian
CFAR detectors in detecting obscured targets.