A comparative and combined study of EMIS and GPR detectors by the use of Independent Component Analysis

Axel Morgenstjerne Morgenstjerne, Brian Karlsen, Jan Larsen, Helge B.D. Sørensen, Kaj B. Jakobsen

AbstractIndependent Component Analysis (ICA) is applied to classify unexploded ordnance (UXO) on laboratory UXO
test-field data, acquired by stand-off detection. The data are acquired by an Electromagnetic Induction Spectroscopy
(EMIS) metal detector and a ground penetrating radar (GPR) detector. The metal detector is a GEM-3,
which is a monostatic sensor measuring the response of the environment on a multi-frequency constant wave
excitation field (300 Hz to 25 kHz), and the GPR detector is a stepped-frequency GPR with a monostatic bow-tie
antenna (500MHz to 2.5GHz). For both sensors the in-phase and the quadrature responses are measured at
each frequency. The test field is a box of soil where a wide range of UXOs are placed at selected positions. The
position and movement of both of the detectors are controlled by a 2D-scanner. Thus the data are acquired
at well-defined measurement points. The data are processed by the use of statistical signal processing based
on ICA. An unsupervised method based on ICA to detect, discriminate, and classify the UXOs from clutter is
suggested. The approach is studied on GPR and EMIS data, separately and compared. The potential is an
improved ability: to detect the UXOs, to evaluate the related characteristics, and to reduce the number of false
alarms from harmless objects and clutter.
KeywordsIndependent component analysis. Unsupervised detection, discrimination, and classification. GPR and EMIS detection of UXOs and landmines.
TypeConference paper [With referee]
ConferenceProceedings of the 2005 Detection and Remediation Technologies for Mines and Mine-Like Targets, AeroSense 2005
Year2005
PublisherSPIE
AddressOrlando, FL
Electronic version(s)[pdf]
BibTeX data [bibtex]
IMM Group(s)Intelligent Signal Processing


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