Adaptive Clutter-Edge CFAR Detection For Pulsed Radar With Target Excision And Edge-Aware Threshold Selection

Constant false-alarm rate (CFAR) detection is fundamental to radar signal processing. Cell-averaging CFAR (CA-CFAR) is optimal in homogeneous Rayleigh clutter but suffers severe detection loss at clutter edges and in the presence of interfering targets. Order-statistic CFAR (OS-CFAR) is more robust at transitions but sacrifices detection sensitivity in uniform backgrounds. This paper proposes Adaptive Clutter-Edge CFAR (ACE-CFAR), a three-mode detector that (i) tests for clutter-power transitions using a leading-versus-lagging window ratio, (ii) selects the lower-power reference window at detected edges to prevent threshold inflation, and (iii) applies median-gated target excision in homogeneous regions to eliminate masking by nearby interferers. A soft sigmoid transition blends the two modes. The method is evaluated on synthetic Rayleigh-clutter range profiles with Swerling-I targets across four scenarios (homogeneous, clutter-edge, multi-target, and mixed) at seven input SNR levels using 200 Monte Carlo trials. ACE-CFAR more than doubles the probability of detection at clutter edges (Pd = 0.61 versus 0.25 for CA-CFAR and 0.20 for OS-CFAR) while maintaining competitive detection in homogeneous clutter (Pd = 0.71 versus 0.65). The measured false-alarm rate is modestly elevated (7-19 times the design Pfa) as an explicit trade-off for the detection gain, and is discussed openly. The results demonstrate that ACE-CFAR occupies a favourable operating point on the detection-versus-false-alarm surface that neither baseline reaches.