TR2018-143
Terahertz Imaging of Multi-Level Pseudo-Random Reflectance
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- , "Terahertz Imaging of Multi-Level Pseudo-Random Reflectance", International Conference on Infrared, Millimeter, and Terahertz Waves (IRMMW-THz), DOI: 10.1109/irmmw-thz.2018.8510277, September 2018.BibTeX TR2018-143 PDF
- @inproceedings{Wang2018sep4,
- author = {Fu, H. and Wang, P., and Koike-Akino, T. and Ma, R. and Wang, B. and Orlik, P.V. and Tsujita, W. and Sadamoto, K. and Sawa, Y. and Kato, K. and Nakajima, M.},
- title = {Terahertz Imaging of Multi-Level Pseudo-Random Reflectance},
- booktitle = {International Conference on Infrared, Millimeter, and Terahertz Waves (IRMMW-THz)},
- year = 2018,
- month = sep,
- doi = {10.1109/irmmw-thz.2018.8510277},
- url = {https://www.merl.com/publications/TR2018-143}
- }
- , "Terahertz Imaging of Multi-Level Pseudo-Random Reflectance", International Conference on Infrared, Millimeter, and Terahertz Waves (IRMMW-THz), DOI: 10.1109/irmmw-thz.2018.8510277, September 2018.
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Abstract:
This paper introduces a terahertz (THz)-based absolute positioning system with a single THz transceiver as the read head and a multi-level pseudo-random reflectance pattern (e.g., multi-level m-sequences) as the high-resolution scale in a compressed scanning mode. One of key technical challenges here is to computationally recover the multi-level pseudo-random reflectance pattern from compressed measurements. To this end, we develop a variational Bayesian approach to exploit the finite alphabet of reflectance levels and enable a pixel-wise iterative inference for fast recovery. Numerical results confirm the effectiveness of the proposed method.