Curvelet filter based prescreener for explosive hazard detection in hand-held ground penetrating radar

摘要

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.