29 April 2010 Preprocessing of GPR data for syntactic landmine detection and classification
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Abstract
Syntactic pattern recognition is being used to detect and classify non-metallic landmines in terms of their range impedance discontinuity profile. This profile, extracted from the ground penetrating radar's return signal, constitutes a high-range-resolution and unique description of the inner structure of a landmine. In this paper, we discuss two preprocessing steps necessary to extract such a profile, namely, inverse filtering (deconvolving) and binarization. We validate the use of an inverse filter to effectively decompose the observed composite signal resulting from the different layers of dielectric materials of a landmine. It is demonstrated that the transmitted radar waveform undergoing multiple reflections with different materials does not change appreciably, and mainly depends on the transmit and receive processing chains of the particular radar being used. Then, a new inversion approach for the inverse filter is presented based on the cumulative contribution of the different frequency components to the original Fourier spectrum. We discuss the tradeoffs and challenges involved in such a filter design. The purpose of the binarization scheme is to localize the impedance discontinuities in range, by assigning a '1' to the peaks of the inverse filtered output, and '0' to all other values. The paper is concluded with simulation results showing the effectiveness of the proposed preprocessing technique.
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Ahmed O. Nasif, Ahmed O. Nasif, Kenneth J. Hintz, Kenneth J. Hintz, Nathalia Peixoto, Nathalia Peixoto, } "Preprocessing of GPR data for syntactic landmine detection and classification", Proc. SPIE 7664, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XV, 76642F (29 April 2010); doi: 10.1117/12.852439; https://doi.org/10.1117/12.852439
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