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21 May 2015 Terrain clutter simulation using physics-based scattering model and digital terrain profile data
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Localization of a wireless capsule endoscope finds many clinical applications from diagnostics to therapy. There are potentially two approaches of the electromagnetic waves based localization: a) signal propagation model based localization using a priori information about the persons dielectric channels, and b) recently developed microwave imaging based localization without using any a priori information about the persons dielectric channels. In this paper, we study the second approach in terms of a variety of frequencies and signal-to-noise ratios for localization accuracy. To this end, we select a 2-D anatomically realistic numerical phantom for microwave imaging at different frequencies. The selected frequencies are 13:56 MHz, 431:5 MHz, 920 MHz, and 2380 MHz that are typically considered for medical applications. Microwave imaging of a phantom will provide us with an electromagnetic model with electrical properties (relative permittivity and conductivity) of the internal parts of the body and can be useful as a foundation for localization of an in-body RF source. Low frequency imaging at 13:56 MHz provides a low resolution image with high contrast in the dielectric properties. However, at high frequencies, the imaging algorithm is able to image only the outer boundaries of the tissues due to low penetration depth as higher frequency means higher attenuation. Furthermore, recently developed localization method based on microwave imaging is used for estimating the localization accuracy at different frequencies and signal-to-noise ratios. Statistical evaluation of the localization error is performed using the cumulative distribution function (CDF). Based on our results, we conclude that the localization accuracy is minimally affected by the frequency or the noise. However, the choice of the frequency will become critical if the purpose of the method is to image the internal parts of the body for tumor and/or cancer detection.
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James Park, Joel T. Johnson, Kung-Hau Ding, Kristopher Kim, and Joseph Tenbarge "Terrain clutter simulation using physics-based scattering model and digital terrain profile data", Proc. SPIE 9461, Radar Sensor Technology XIX; and Active and Passive Signatures VI, 94610C (21 May 2015);

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