Fast 3-D Qualitative Method for Through-Wall Imaging and Structural Health Monitoring
A fast non-iterative 3-D imaging method is developed which incorporates several critical approximations to overcome computational burden and data collection constraints associated with imaging layered media. The method first invokes dyadic Green's function to relate the object reflectivity function (or image) to the collected data. Then, calculating the reflectivity function requires solving a computationally-intensive and ill-posed inverse problem. The data collection constraints and layered media modeling uncertainties also add to the complexity associated with this inverse problem. To address these challenges, a set of approximations are considered which simplify the imaging problem and cast it into a de-convolution problem with much less computational burden which is implemented using fast Fourier transform. The performance of the method is verified for through-wall imaging and structural health monitoring, indicating that it is reasonably tolerant to constraint associated with data collection limitations and modeling inaccuracies.
M. B. Fallahpour and R. Zoughi, "Fast 3-D Qualitative Method for Through-Wall Imaging and Structural Health Monitoring," IEEE Geoscience and Remote Sensing Letters, vol. 12, no. 12, pp. 2463-2467, Institute of Electrical and Electronics Engineers (IEEE), Dec 2015.
The definitive version is available at https://doi.org/10.1109/LGRS.2015.2484260
Electrical and Computer Engineering
Keywords and Phrases
Data Acquisition; Fast Fourier Transforms; Iterative Methods; Reflection; Retaining Walls; Structural Health Monitoring; Uncertainty Analysis; Computational Burden; Dyadic Green's Functions; Ill-Posed Inverse Problem; Imaging Problems; Model Inaccuracy; Qualitative Method; Reflectivity Functions; Through-Wall Imaging; Inverse Problems; Approximation Methods; Green's Function Methods; Imaging; Mathematical Model; Nonhomogeneous Media; Radar Imaging; Synthetic Aperture Radar
International Standard Serial Number (ISSN)
Article - Journal
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