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SPARSE SPECTRAL FACTORIZATION: UNICITY AND RECONSTRUCTION ALGORITHMS

Full Paper at IEEE Xplore

Compressed Sensing and Sparse Representation of Signals

Presented by: Juri Ranieri, Author(s): 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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  Lecture Information

Recorded: 2011-05-27 14:25 - 14:45, Club B
Added: 15. 6. 2011 07:14
Number of views: 106
Video resolution: 1024x576 px, 512x288 px
Video length: 0:18:33
Audio track: MP3 [6.26 MB], 0:18:33