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Discrimination mode processing for emi and gpr sensors for hand-held land mine detection.pdf

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Discrimination mode processing for emi and gpr sensors for hand-held land mine detection

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 42, NO. 1, JANUARY 2004 249 Discrimination Mode Processing for EMI and GPR Sensors for Hand-Held Land Mine Detection K. C. Ho, Senior Member, IEEE, Leslie M. Collins, Senior Member, IEEE, Lisa G. Huettel, Member, IEEE, and Paul D. Gader, Senior Member, IEEE Abstract—Signal processing algorithms for hand-held mine de- tection sensors are described. The goals of the algorithms are to provide alarms to a human operator indicating the likelihood of the presence of a buried mine. Two modes of operations are con- sidered: search mode and discrimination mode. Search mode gen- erates an initial detection at a suspected location and discrimina- tion mode confirms that the suspected location contains a land mine. Search mode requires that the signal processing algorithm generate a detection confidence value immediately at the current sample location and no delay in producing an alarm confidence is tolerable. Search mode detection has a high false-alarm rate. Dis- crimination mode allows the operator to interrogate the entire sus- pected location to eliminate false alarms. It does not require that the signal processing algorithm produce an alarm confidence im- mediately for the current sample location, but rather allows the system to process all the data acquired over the region before pro- ducing an alarm. This paper proposes discrimination mode pro- cessing algorithms for metal detectors (MDs), or electromagnetic induction sensors (EMIs), ground-penetrating radars (GPRs), and their fusion. The MD discrimination mode algorithm employs a model-based approach and uses the target model parameters to discriminate between mines and clutter objects. The GPR discrim- ination mode algorithm uses the consistency of detection as well as the shape of the detection peaks over several sweeps to improve the discrimination accuracy. The performances of the proposed algo- rithms were examined on a dataset collected at a government test