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SPARSE SPECTRAL FACTORIZATION: UNICITY AND RECONSTRUCTION ALGORITHMS

Full Paper at IEEE Xplore

Compressed Sensing and Sparse Representation of Signals

Přednášející: Juri Ranieri, Autoři: Yue Lu, Harvard University, United States; Martin Vetterli, Ecole Polytechnique Fédérale de Lausanne, Switzerland

Spectral factorization is a classical tool in signal processing and communications. It also plays a critical role in X-ray crystallography, in the context of phase retrieval. In this work, we study the problem of sparse spectral factorization, aiming to recover a one-dimensional sparse signal from its autocorrelation. We present a sufficient condition for the recovery to be unique, and propose an iterative algorithm that can obtain the original signal (up to a sign change, time-shift and time-reversal). Numerical simulations verify the effectiveness of the proposed algorithm.

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  Informace o přednášce

Nahráno: 2011-05-27 14:25 - 14:45, Club B
Přidáno: 15. 6. 2011 07:14
Počet zhlédnutí: 106
Rozlišení videa: 1024x576 px, 512x288 px
Délka videa: 0:18:33
Audio stopa: MP3 [6.26 MB], 0:18:33