A ternary detection test with applications to the sidelobe blanking problem

We address adaptive discrimination between the signal of interest and a coherent interferer. To this end, we propose detectors derived resorting to a GLRT implementation of a generalized Neyman-Pearson rule (i.e., for multiple hypotheses). The adaptive detectors rely on secondary data, free of signal components, but sharing the statistical characterization of the noise in the cell under test, in order to guarantee the CFAR property. A preliminary performance assessment, conducted by Monte Carlo simulation, shows that, for the considered case study, the proposed algorithm can outperform the SLC/SLB adaptive detector under certain circumstances.