Locally-Adaptive Detection Algorithm for Forward-Looking Ground-Penetrating Radar

摘要

This paper proposes an effective anomaly detection algorithm for a forward-looking ground-penetrating radar (FLGPR). One challenge for threat detection using FLGPR is its high dynamic range in response to different kinds of targets and clutter objects. The application of a fixed threshold for detection often yields a large number of false alarms. We propose a locally-adaptive detection method that adjusts the detection criteria automatically and dynamically across different spatial regions, which improves the detection of weak scattering targets. The paper also examines a spectrum-based classifier. This classifier rejects false alarms (FAs) by classifying each alarm location based on its spatial frequency-spectrum. Experimental results for the improved detection techniques are demonstrated by field data measurements from a US Army test site.