Handheld Ground-Penetrating Radar Antenna Position Estimation Using Factor Graphs

Accurate localization of handheld ground-penetrating radar (HH-GPR) systems is critical for high-quality subsurface imaging and precise geospatial mapping of detected buried objects. In our previous works, we demonstrated that a UWB positioning system with an extended Kalman filter (EKF) employing a proprietary pendulum (PND) dynamics model yielded highly accurate results. Building on that foundation, we present a factor-graph-based estimation algorithm to further enhance the accuracy of HH-GPR antenna trajectory estimation. The system was modeled under realistic conditions, and both the EKF and various factor-graph algorithms were implemented. Comparative evaluation indicates that the factor-graph approach achieves an improvement in localization accuracy from over 30 to almost 50 percent compared to the EKF PND. The sparse matrix representation inherent in the factor graph enabled an efficient iterative solution of the underlying linearized system. This enhanced positioning accuracy is expected to facilitate the generation of clearer, more distinct underground images, thereby supporting the potential for more reliable identification and classification of buried objects and infrastructure.