The potential for automatic target recognition (ATR) processing of foliage-penetrating (FOPEN) synthetic-aperture radar (SAR) imagery requires very high bandwidth occupancies to achieve sufficient range resolution for the ATR task. The U.S. Army Research Laboratory (ARL) ultra-wideband (UWB) FOPEN SAR -- with greater than 95 percent bandwidth occupancy -- provides a suitable testbed for evaluation of resonance-based ATR approaches. Current resonance-extraction techniques (e.g., SEM) typically have poor performance in the presence of noise, and are often computationally intensive. Recently developed at ARL, the `spectral correlation method' uses linear transforms -- such as Fourier and wavelets -- to resolve resonant components; these transforms are generally quite fast, and have straightforward implementations. Creating a synthetic version of the ringdown and projecting onto the desired transform basis provides a set of expected spectral coefficients (the `spectral template'). The spectral template is correlated with the spectral coefficients acquired from the projection of the focused image data onto the same basis function set; the correlation coefficient is then passed through a simple threshold detector. This yields a fast, efficient scheme for recognition of target resonance effects in UWB imagery. Recent advances in this area include a reduction in false-alarm rate by two orders of magnitude, a reduction in processing time by three orders of magnitude, and recognition of a tactical target.