Processing of tissue sensing adaptive radar data - an analogue for georadar

Adrian D. Smith, Jeremie Bourqui, Yuhong (Kay) Liu, Elise C. Fear, and Robert J. Ferguson

ABSTRACT

Frequency domain georadar data have several advantages over time domain data but are much more cumbersome to acquire. A medical imaging technique developed in the Department of Electrical Engineering at the University of Calgary known as "tissue sensing adaptive radar" (TSAR) makes use of monostatic radar data acquired in the frequency domain. Simulated data were generated and processed using a workflow that has been previously developed and implemented sucessfully on georadar data. We discover that the simulated TSAR data are mixed phase, violating the minimum-phase assumptions of deconvolution. We show through synthetic examples that deconvolution of these data does not recover reflectivity accurately. Although nonstationary Gabor deconvolution has been shown to be effective when applied to georadar data, our work with the TSAR data shows that we must take care to ensure that radar data be minimum phase during deconvolution, especially with data acquired in the frequency domain.

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