Sparse spectral factorization: Unicity and Reconstruction Algorithms

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.


Published in:
Proceedings of the 36th International Conference on Acoustics, Speech and Signal Processing (ICASSP), 5976-5979
Presented at:
International Conference on Acoustics, Speech and Signal Processing (ICASSP), Prague, Czech Republic, May 22-27, 2011
Year:
2011
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 Record created 2011-04-01, last modified 2018-03-17

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