  In this paper, we propose an adaptive matched detector of a signal corrupted
by a non-Gaussian noise with an inverse gamma texture. The detector is formed
using a set of secondary data measurements, and is analytically shown to have a
constant false alarm rate. The analytic performance is validated using Monte
Carlo simulations, and the proposed detector is shown to offer preferable
performance as compared to the related one-step generalized likelihood ratio
test (1S-GLRT) and the adaptive subspace detector (ASD).
