A novel signal processing technique for clutter reduction in GPR measurements of small, shallow land mines

TLDR

Ground‑penetrating radar is commonly employed to detect subsurface antipersonnel mines, but ground‑bounce clutter hampers detection. The study develops a signal‑processing technique to reduce ground‑bounce clutter in GPR measurements. The method models clutter with a simple parametric model and separates mine and clutter signals via coupled iterative procedures. The algorithm outperforms existing clutter‑reduction methods, provides useful target features for mine detection, and significantly lowers false‑alarm rates.

Abstract

A signal processing technique is developed to reduce clutter due to ground bounce in ground penetrating radar (GPR) measurements. This technique is especially useful when a GPR is used to detect subsurface antipersonnel mines. The GPR clutter is modeled using a simple parametric model. Buried mine and clutter contributions are separated through a pair of coupled iterative procedures. The algorithm outperforms existing clutter reduction approaches and also yields target features that are useful for detection and identification of these mines. The proposed technique effectively reduces clutter resulting in a significant decrease in false alarm rates.