Automatic target detection and discrimination algorithm applicable to ground penetrating radar data

Abstract

Ground Penetrating Radar (GPR) is considered as one of the promising technologies to address the challenges of detecting buried threat objects. However, the success rate of the GPR systems are limited by operational conditions and the robustness of automatic target recognition (ATR) algorithms embedded with the systems. In this paper an alternate ATR algorithm applicable to GPR is developed by combining image pre-processing and machine learning techniques. The aim of this research was to design a potential solution for detection of threat alarms using GPR data and reducing the number of false alarms through classification into one of the predefined categories of target types. The proposed ATR algorithm has been validated using a data set acquired by a vehicle-mounted GPR array. The data set utilized in this investigation involved greyscale GPR images of threat objects (both conventional and improvised) commonly found in realistic operational scenarios. Target based summaries of the algorithm performance are presented in terms of the probability of detection, false alarm rate, and confidence of allocating detections to a predefined target class.