The US Army Research Laboratory (ARL), as part of customer and mission-funded applied research programs, has been evaluating the use of low-frequency, ultra-wideband imaging radar to detect fields of buried mines. An instrumentation- grade measurement system has been designed and implemented by ARL. Four data collection campaigns in support of ground- penetrating radar objectives have led to the establishment of a significant and unique database of radar imagery. We are using these data to develop mine-detection algorithms that can aid an operator in separating mines from background clutter. This paper reviews recent findings and result from ARL's modeling, phenomenology, and algorithm development efforts. At SPIE '98, we reported on the performance of a physics-based mine-detection algorithm using data collected at Yuma Proving Ground (YPG) in January 1996. Subsequent measurements were made using the ARL BoomSAR at YPG in October 1997, January 1998, and June 1998. Most of the mines from the January 1996 experiment were still in place during the 1997/1998 experiments. Additional mines and unexploded ordnance were added to the YPG test after the January 1996 experiment. This paper discusses the difference in soil conditions from these data collections and the impact that may have on a mine's radar cross section (RCS) and detection performance. Detection results for M20 mines under different soil conditions will be shown. The detection algorithm invokes phenomenologically sound feature that exploit the expected mine RCS, texture, frequency dependent scattering, and model-based image correlation. Performance assessments, in terms of receiver operating characteristics, detail the detection capabilities at various false alarm rates. Finally, new imagery will be presented that shows the positive contrast of low metal content above dielectric background.