Inh.: Dr. Renate Gorre
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Series in Communication Theory
Edited by Helmut Bölcskei
Performance of Super-Resolution Methods in
Parameter Estimation and System Identification
1st edition 2017. XXVI, 244 pages; € 64.00.
Extracting information from low-resolution data is a challenging task in many applications ranging from astronomy and radar to medical imaging, microscopy, and spectroscopy. This work examines the performance of super-resolution methods that enable the recovery of the high-frequency information of a signal from low-frequency, i.e., low-resolution, measurements by exploiting a priori knowledge on the signal structure.
First, we develop a mathematical theory of super-resolution from short-time Fourier transform (STFT) measurements. Then, we study the problem of identifying a linear time-varying system characterized by a discrete set of delays and Doppler shifts, which is closely related to the super-resolution problem from STFT measurements. Finally, we analyze the performance of so-called high-resolution parametric subspace methods for the estimation of the spectrum of a signal from a finite number of samples. Our results are enabled by new bounds on the extremal singular values of complex-valued Vandermonde matrices, with entries in the unit disk.
About the author:
Céline Aubel was born in Épinal, France, in 1988. She received the Engineering Degree from École Supérieure d'Électricité (Supélec), France, and the MSc in Electrical Engineering and Information Technology from ETH Zurich, Switzerland, both in 2011, after which she joined the Communication Technology Laboratory at ETH Zurich as a research assistant. She graduated from ETH Zurich with the Dr. sc. degree in 2017.
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