Ground tracking for improved landmine detection in a GPR system

TL;DR

This paper introduces ground bounce tracking algorithms using Kalman and particle filters to reduce interference in GPR data, significantly enhancing landmine detection accuracy.

Contribution

It presents novel GB tracking methods with adaptive features and automatic parameter tuning, improving landmine detection in varied ground conditions.

Findings

Enhanced GB tracking improves landmine detection accuracy.

Particle filter approach models GB as a hidden state for better tracking.

Experimental results show superior performance over existing methods.

Abstract

Ground penetrating radar (GPR) provides a promising technology for accurate subsurface object detection. In particular, it has shown promise for detecting landmines with low metal content. However, the ground bounce (GB) that is present in GPR data, which is caused by the dielectric discontinuity between soil and air, is a major source of interference and degrades landmine detection performance. To mitigate this interference, GB tracking algorithms formulated using both a Kalman filter (KF) and a particle filter (PF) framework are proposed. In particular, the location of the GB in the radar signal is modeled as the hidden state in a stochastic system for the PF approach. The observations are the 2D radar images, which arrive scan by scan along the down-track direction. An initial training stage sets parameters automatically to accommodate different ground and weather conditions. The features associated with the GB description are updated adaptively with the arrival of new data. The prior distribution for a given location is predicted by propagating information from two adjacent channels/scans, which ensures that the overall GB surface remains smooth. The proposed algorithms are verified in experiments utilizing real data, and their performances are compared with other GB tracking approaches. We demonstrate that improved GB tracking contributes to improved performance for the landmine detection problem.