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A reinforcement learning (RL)-based hybrid method for ground penetrating radar (GPR)-driven buried object detection by Mahmut Nedim Alpdemir; Mehmet Sezgin is a Computer Science article available to read on EtoBox.

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## Abstract Ground penetrating radar (GPR) systems are effective sensors for discovering various types of objects buried underground, such as military mines, metal objects, and pieces of underground infrastructures. A GPR system can be manually operated by a human or can be an integral part of a host platform. The host platform may be semi- or fully autonomous and may operate in different environments such as land vehicles or more recently air-borne drones. One challenge for the fully or semi-autonomous host platforms in particular is to find an efficient search procedure that would reduce the operation time and optimize resource utilization. Most of the current approaches are based on pre-defined search patterns which, for large and sparse areas, could mean unnecessary waste of time and resources. In this paper, we introduce a method that combines a coarse and therefore relatively low cost initial search pattern with a Reinforcement Learning (RL) driven efficient navigation path for eventual target detection, by exploiting the signal processing pipeline of the onboard GPR. We illustrate the applicability of the method using a well-known, high fidelity GPR simulation environment an

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Author

Mahmut Nedim Alpdemir; Mehmet Sezgin

Publisher

Springer Science and Business Media LLC

Published

2024

Language

EN

Field

Computer Science (Physical Sciences)