Explosive hazards, above and below ground, are a serious threat to civilians and soldiers. In an attempt to mitigate these
threats, different forms of explosive hazard detection (EHD) exist; e.g., multi-sensor hand-held platforms, downward
looking and forward looking vehicle mounted platforms, etc. Robust detection of these threats resides in the processing
and fusion of different data from multiple sensing modalities, e.g., radar, infrared, electromagnetic induction (EMI), etc.
Herein, we focus on a new energy-based prescreener in hand-held ground penetrating radar (GPR). First, we Curvelet
filter B-scan signal data using either Reverse-Reconstruction followed by Enhancement (RRE) or selectivity with respect
to wedge information in the Curvelet transform. Next, we aggregate the result of a bank of matched filters and run a size
contrast filter with Bhattacharyya distance. Alarms are then combined using weighted mean shift clustering. Results are
demonstrated in the context of receiver operating characteristics (ROC) curve performance on data from a U. S. Army
test site that contains multiple target and clutter types, burial depths and times of the day.