  Target detection is the front-end stage in any automatic target recognition
system for synthetic aperture radar (SAR) imagery (SAR-ATR). The efficacy of
the detector directly impacts the succeeding stages in the SAR-ATR processing
chain. There are numerous methods reported in the literature for implementing
the detector. We offer an umbrella under which the various research activities
in the field are broadly probed and taxonomized. First, a taxonomy for the
various detection methods is proposed. Second, the underlying assumptions for
different implementation strategies are overviewed. Third, a tabular comparison
between careful selections of representative examples is introduced. Finally, a
novel discussion is presented, wherein the issues covered include suitability
of SAR data models, understanding the multiplicative SAR data models, and two
unique perspectives on constant false alarm rate (CFAR) detection: signal
processing and pattern recognition. From a signal processing perspective, CFAR
is shown to be a finite impulse response band-pass filter. From a statistical
pattern recognition perspective, CFAR is shown to be a suboptimal one-class
classifier: a Euclidian distance classifier and a quadratic discriminant with a
missing term for one-parameter and two-parameter CFAR, respectively. We make a
contribution toward enabling an objective design and implementation for target
detection in SAR imagery.
