A Novel Search Algorithm and Scan Time Estimation in Airborne Ground Penetrating Radar Using Cell Footprint Meshing

Airborne Ground Penetrating Radar (A-GPR) systems in multilayer environments often exhibit inefficiencies stemming from suboptimal search patterns and inaccurate scan time estimations, resulting in protracted operation times and elevated energy consumption. To mitigate these challenges, this paper introduces a novel theoretical search algorithm predicated on cell footprint meshing. This approach leverages antenna footprint dimensions to optimize search paths, thereby reducing scan time and enhancing energy efficiency. By categorizing the target area into surface, subsurface, and return cells based on signal reception characteristics, the algorithm aligns search patterns (row-wise or column-wise) with the derived cell configurations and dimensions. Precise computations of wave penetration and two-way travel time (TWT), factoring in stacking parameters and drone velocity, are integral to optimal cell design and comprehensive area coverage. Simulation results indicate that this deterministic, geometry-based method yields an approximate 24% reduction in search time, consequently minimizing resource utilization. The inherent scalability of this algorithm renders it particularly advantageous for missions constrained by time and resources and establishes a framework for enhancing A-GPR system performance. However, as a theoretical investigation, the absence of experimental validation due to the limited availability of real-world A-GPR systems and data necessitates future field studies. This work underscores the critical role of the cellular network and search pattern selection in augmenting operational efficiency and provides a foundational basis for subsequent development and practical implementation in radar-based reconnaissance.