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Proceedings Paper

Machine learning based automatic target recognition algorithm applicable to ground penetrating radar data
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Paper Abstract

Handheld, vehicle mounted and air-borne Ground Penetrating Radar (GPR) systems have been identified as potential technology solutions for detection of current and evolving 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. With the ever-increasing complexity of target configuration and their deployment scenarios it is becoming a challenge to develop ATR algorithms robust enough to detect and identify GPR signatures of a wide variety of threat objects. The aim of this research is to design a potential solution for detection of threat objects using GPR data and reducing the number of false alarms. In this paper, a Machine Learning (ML) based ATR algorithm applicable to GPR data is developed to detect complex patterns and trends relevant to a multitude of threat objects. 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 GPR data of threat objects (both conventional and improvised) commonly found in realistic operational scenarios. Lane based summaries of the algorithm performance are presented in terms of the probability of detection threat objects and false alarm rate. Preliminary results of the proposed ML techniques have shown promise of achieving a high detection rate and a low false alarm rate in multiple GPR data sets collected in challenging geographical locations.

Paper Details

Date Published: 10 May 2019
PDF: 17 pages
Proc. SPIE 11012, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIV, 1101202 (10 May 2019); doi: 10.1117/12.2517195
Show Author Affiliations
Canicious Abeynayake, Defence Science and Technology Group (Australia)
Vidar Son, Univ. of South Australia (Australia)
Md. Hedayetul Islam Shovon, Univ. of South Australia (Australia)
Hiroshi Yokohama, Defence Science and Technology Group (Australia)

Published in SPIE Proceedings Vol. 11012:
Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIV
Steven S. Bishop; Jason C. Isaacs, Editor(s)

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