GPR Signal Processing for Landmine Detection

Abstract

Landmine detection remains a critical challenge due to the difficulty of identifying buried threats. These hidden explosives pose a significant danger to human lives, hindering economic growth and development efforts. Traditional methods for landmine detection often need to be revised, relying on time-consuming manual techniques or needing more ability to identify non-metallic mines. Fortunately, advancements in technology offer various methods for locating buried landmines. Ground penetrating radar (GPR) has emerged as a powerful tool for subsurface exploration, emitting electromagnetic waves and recording reflections to create an image of buried objects. However, GPR data presents a complex picture, containing reflections from various underground features besides landmines. Effective landmine detection hinges on distinguishing these targets from background clutter. This paper delves into the comparative analysis of feature extraction and classification techniques employed in GPR-based landmine detection. The initial stage involves feature extraction, where algorithms identify and quantify characteristics within the GPR data that discriminate landmines from other objects. Various approaches exist, including image processing techniques like edge detection and statistical methods that analyze signal intensity variations. Machine learning algorithms, such as Support Vector Machines (SVMs) and k-nearest neighbors (k-NN), can learn these discriminatory features from labeled GPR data sets containing confirmed landmine locations. This paper meticulously compares the effectiveness of these techniques using performance metrics like probability of detection (Pd), accuracy, and false alarm rate (FAR). The paper aims to identify the optimal approach for accurate landmine detection by evaluating these metrics across different feature extraction and classification algorithms. This optimal approach should maximize Pd while minimizing FAR, ensuring landmines' safe and efficient identification for humanitarian demining efforts.