Ground tracking for improved landmine detection in a GPR system

2025-06-22

AI Summary

Ground bounce (GB) interference—caused by dielectric discontinuity at the air–ground interface—severely degrades detection performance for low-metal landmines in ground-penetrating radar (GPR). To address this, we propose an adaptive ground echo tracking framework that integrates particle filtering with Kalman filtering. The ground surface position is modeled as a latent state; a spatiotemporal prior is constructed from 2D radar image sequences, enabling robust surface contour estimation and GB suppression via cross-channel information propagation and adaptive feature updating. Experiments on real-world GPR data demonstrate that our method significantly improves GB localization accuracy—reducing mean error by 38.6%—and increases landmine detection recall by 22.4%. Moreover, it exhibits enhanced robustness to terrain variations and noise. The framework provides a deployable, real-time processing solution for low-metal target detection.

Technology Category

• Intelligent Robots: State Estimation
• Natural Language Processing: Language Grounding & Multi-modal NLP
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Mitigate ground bounce interference in GPR landmine detection
• Track ground bounce using Kalman and particle filters
• Improve landmine detection accuracy in varying conditions

Innovation

• Kalman filter for ground bounce tracking
• Particle filter for hidden state modeling
• Adaptive feature updating with new data