Adaptive constant-false-alarm-rate (CFAR) processors utilizing
structured covariance matrices  James Edward Whitney, Marquette
University   In radar systems and signal processing, it is
common to detect a signal in a background of non-homogeneous
noise. Radar detection algorithms are usually implemented by
taking a snapshot of one or more range gates adjacent to the
cell under test, forming an analysis window, and calculating the
noise statistics within this window. The spatial statistics of
the noise vary with time, but for a finite size analysis window,
the background can be assumed homogeneous. Several variants of
detection processor exist, the two primary categories being
scalar and vector. Both types of processor are dependent on the
statistics of the background noise when setting the detection
threshold. In the scalar case, the true variance of the
background, $\sigma\sbsp{n}{2}$, is unknown. In the vector case,
this unknown variance is incorporated within the noise sample
covariance matrix. Scalar proc