SINR performance of matched illumination signals with dynamic target models

Matched illumination (MI) radar signals provide improved target signal to interference noise ratios (SINR) and better spread ambiguity function performance compared to conventional radar in the presence of range-spread targets. Performance improvements reported in literature are based on the assumption of perfect knowledge of responses of the extended targets as well as interference spectra. In this paper, we analyze the SINR performance of MI systems from the learning phase to illumination phase using complex geometric theory of diffraction (GTD)-based target models. We numerically evaluate the worst-case performance degradation arising from aspect change of the target models from learning to illumination phase. The results set the stage for inclusion of a scheduler, which would facilitate selective MI based on system parameters and the targets being tracked.