SuperLectures.com

SPARSE SPECTRAL FACTORIZATION: UNICITY AND RECONSTRUCTION ALGORITHMS

Compressed Sensing and Sparse Representation of Signals

Full Paper at IEEE Xplore

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.

  Speech Transcript

|

  Slides

Enlarge the slide | Show all slides in a pop-up window

0:00:29

  1. slide

0:00:47

  2. slide

0:01:17

  3. slide

0:02:29

  4. slide

0:04:48

  5. slide

0:05:18

  6. slide

0:06:37

  7. slide

0:07:55

  8. slide

0:08:47

  9. slide

0:09:34

 10. slide

0:10:56

 11. slide

0:11:46

 12. slide

0:12:43

 13. slide

0:13:12

 14. slide

0:17:51

     7. slide

  Comments

Please sign in to post your comment!

  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