Discrimination mode processing for EMI and GPR sensors for hand-held land mine detection

• Published in: IEEE transactions on geoscience and remote sensing 2004-01, Vol.42 (1), p.249-263
• Main Authors: Ho, K.C., Collins, L.M., Huettel, L.G., Gader, P.D.
• Format: Article
• Language: English
• Subjects: Algorithms; Applied geophysics; Discrimination; Earth sciences; Earth, ocean, space; Electromagnetic interference; Engineering and environment geology. Geothermics; Exact sciences and technology; False alarms; Ground penetrating radar; Humans; Internal geophysics; Landmine detection; Performance evaluation; Pollution, environment geology; Radar detection; Sensor phenomena and characterization; Signal generators; Signal processing; Signal processing algorithms; Testing
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2003.817804

Signal processing algorithms for hand-held mine detection 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 considered: search mode and discrimination mode. Search mode generates an initial detection at a suspected location and discrimination 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. Discrimination mode allows the operator to interrogate the entire suspected location to eliminate false alarms. It does not require that the signal processing algorithm produce an alarm confidence immediately for the current sample location, but rather allows the system to process all the data acquired over the region before producing an alarm. This paper proposes discrimination mode processing 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 discrimination 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 algorithms were examined on a dataset collected at a government test site, and performance was compared with baseline techniques. Experimental results showed that the proposed method can reduce the probability of false alarm by as much as 70% at a 100% correct detection rate and performed comparable to the best human operator on a blind test with data collected at approximately 1000 locations.