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. The focus of this thesis is on the implementation of two new algorithms to form a new energy-based prescreener in hand-held ground penetrating radar (GPR). First, B-scan signal data is curvelet filtered using either Reverse- Reconstruction followed by Enhancement (RRE) or selectivity with respect to wedge information in the Curvelet transform, Wedge Selection (WS). Next, the result of a bank of matched filter are aggregated 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.