Electromagnetic induction sensing (EMI), between ~ 10's of Hz and 100's of kHz, may show the strongest promise for discrimination of subsurface, shallow metallic objects such as unexploded ordnance (UXO). While EMI signals penetrate the soil readily, resolution is low and responses are sometimes ambiguous. For crucial discrimination progress, maximum data diversity is desirable in terms of look angles, frequency spectrum, and full vector scattered field data. Newly developed instrumentation now offers the possibility of full vector UWB EMI data with flexible look angle and sensor distance/sweep, defined by precise laser positioning. Particulars of the equipment and resulting data are displayed. An indication is given of potential advantages for reducing the chronic ill-conditioning of inversion calculations with EMI data, when one takes advantage of the data diversity made possible by the instrumental advances. Some EMI measurement issues cannot be solved by EMI data diversity, as when small surface clutter above a much larger UXO effectively blinds an EMI sensor. EMI surveying must be supplemented by or sometimes replaced by ground penetrating radar (GPR) approaches in such instances.
KEYWORDS: Electromagnetic coupling, General packet radio service, Magnetism, Sensors, Feature extraction, Electromagnetism, Data processing, Data modeling, Polarization, Ground penetrating radar
In highly contaminated unexploded ordnance (UXO) cleanup sites, multiple metallic subsurface objects may appear within the field of view of the sensor simultaneously, both for electromagnetic induction (EMI) and ground penetrating radar (GPR). Sensor measurements consist of an a priori unknown mixture of the objects' responses. The two sensing systems can provide different kinds of information, which are complementary and could together produce enhanced UXO discrimination in such cases. GPR can indicate the number of objects and their approximate locations and orientations. This data can then serve as prior information in EMI modeling based on the standardized excitation approximation (SEA). The method is capable of producing very fast, ultra-high fidelity renderings of each object’s response, including all effects of near and far field observation, non-uniform excitation, geometrical and material heterogeneity, and internal interactions. Given good position information, the SEA formulation inverts successfully for EMI parameters for each of the two objects, using EMI data in which their signals overlap. The values of the inferred parameters, in terms of their frequency and spatial patterns for an object's response to each basic excitation, are unique characteristics of the object and could thus serve as a basis for classification.
This paper presents an automatic UXO classification system using neural network and fuzzy inference based on the classification rules developed by the OSU. These rules incorporate scattering pattern, polarization and resonance features extracted from an ultra-wide bandwidth, fully polarimetric radar system. These features allow one to discriminate an elongated object. The algorithm consists of two stages. The first-stage classifies objects into clutter (group-A and D), a horizontal linear object (group-B) and a vertical linear object (group-C) according to the spatial distribution of the Estimated Linear Factor (ELF) values. Then second-stage discriminates UXO-LIKE targets from clutters under groups B and C. The rule in the first-stage was implemented by neural network and rules in the second-stage were realized by fuzzy inference with quantitative variables, i.e. ELF level, flatness of Estimated Target Orientation (ETO), the consistency of the target orientation, and the magnitude of the target response. It was found that the classification performance of this automatic algorithm is comparable with or superior to that obtained from a trained expert. However, the automatic classification procedure does not require the involvement of the operator and assigns a unbiased quantitative confidence level (or quality factor) associated with each classification. Classification error and inconsistency associated with fatigue, memory fading or complex features should be greatly reduced.
KEYWORDS: Neural networks, Target detection, General packet radio service, Antennas, Binary data, Signal to noise ratio, Ground penetrating radar, Detector development, Monte Carlo methods, Roads
Ground penetrating radar (GPR) has been widely used for the detection and location of buried objects. However, the detection method is often subjected to operator's interpretation due to large quantities of data and undesired clutter and noise. Such a detection method is neither reliable nor efficient.
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