Dual mode detectors combining metal detection and ground-penetrating radar are increasingly being used during humanitarian demining operations because of their ability to discriminate metal clutter. There are many reports in the academic literature studying metal detector and ground-penetrating radar systems individually. However, the combination of these techniques has received much less attention. This paper describes the development of a novel dual modality landmine detector, which integrates spectroscopic metal detection with ground-penetrating radar. This paper presents a feature-level sensor fusion strategy based on three features extracted from the two sensors. This paper shows how the data from the two components can be fused together to enrich the feedback to the operator. The algorithms presented in this paper are targeted at automating the location of buried, visibly obscured objects; however, the system described is also capable of collecting information which could also be used for the potential classification of such items.

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The Magnetic Polarizability Tensor (MPT) is a representative property of an electrically conducting or magnetic object that includes information about the object's characteristics such as, shape, size and material. The MPT is especially relevant to metal detection (MD) and can be used to improve MD performance by helping to distinguish between objects. This paper describes an instrument intended to measure the MPT of objects such as anti-personnel landmines and metallic clutter, up to 130 mm in diameter. The instrument uses a novel multi-coil geometry to generate a uniform electromagnetic field over the volume containing the test object to accurately determine the MPT. Performance tests of the system shows peak variance in the MPT is approximately 15 mm 3 . Typical experimental repeatability is better than one percent for tests involving copper disks. Additionally, simulated data as well as previously published simulated and experimental data are used as a validation method of the experimental results. Good agreement between these and the measured MPTs of example targets are seen, proving the system's capability of characterizing metallic objects.

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Antennas are an important component in ground penetrating radar (GPR) systems. Although there has been much research reported on the design of individual antennas, there is less research reported on the design of the geometry of bi-static antennas. This paper considers the effects of key parameters in the setup of a GPR head consisting of a bi-static bow-tie pair to show the effect of these parameters on the GPR performance. The parameters investigated are the antenna separation, antenna height above the soil, and antenna input impedance. The investigation of the parameters was performed by simulation and measurements. It was found when the bi-static antennas were separated by 7 cm to 9 cm and were operated close to the soil (2 cm to 4 cm), the reflected signal from a near-surface object is relatively unaffected by height variation and object depth. An antenna input impedance of 250 Ω was chosen to feed the antennas to reduce the late-time ringing. Using these results, a new GPR system was designed and then evaluated at a test site near Benkovac, Croatia.

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The detection of small metallic objects buried in mineralized soil poses a challenge for metal detectors, especially when the response from the metallic objects is orders of magnitude below the response from the soil. This article describes a new, handheld, detector system based on magnetic induction spectroscopy (MIS), which can be used to detect buried metallic objects, even in challenging soil conditions. Experimental results consisting of 1669 passes across either buried objects or empty soil are presented. Fourteen objects were buried at three different depths in three types of soil including nonmineralized and mineralized soils. A novel processing algorithm is proposed to demonstrate how spectroscopy can be used to detect metallic objects in mineralized soils. The algorithm is robust across all types of soil, objects, and depths used in this experiment and achieves a true positive rate over 99% at a false-positive rate of less than 5%, based on just a single pass over the object. It has also been shown that the algorithm does not have to be trained separately for each soil type. The data gathered in the experiment are also published to enable more research on the processing algorithms for MIS-based detectors.

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