Phase retrieval has been mainly considered in the presence of Gaussian noise. However, the performance of the algorithms proposed under the Gaussian noise model severely degrades when grossly corrupted data, i.e., outliers, exist. This paper investigates techniques for phase retrieval in the presence of heavy-tailed noise, which is considered a better model for situations where outliers exist. An ℓ lp-norm (0<p<2) based estimator is proposed for fending against such noise, and two-block inexact alternating optimization is proposed as the algorithmic framework to tackle the resulting optimization problem. Two specific algorithms are devised by exploring different local approximations within this framework. Interestingly, the core conditional minimization steps can be interpreted as iteratively reweighted least squares and gradient descent. Convergence properties of the algorithms are discussed, and the Cramér-Rao bound (CRB) is derived. Simulations demonstrate that the proposed algorithms approach the CRB and outperform state-of-the-art algorithms in heavy-tailed noise.
|Original language||English (US)|
|Number of pages||14|
|Journal||IEEE Transactions on Signal Processing|
|State||Published - Nov 15 2017|
Bibliographical noteFunding Information:
Manuscript received October 4, 2016; revised March 20, 2017, June 6, 2017, and July 14, 2017; accepted July 14, 2017. Date of publication August 15, 2017; date of current version September 26, 2017. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Xavier Mestre. The work of N. D. Sidiropoulos was supported by NSF CIF-1525194. (Corresponding author: Nicholas D. Sidiropoulos.) C. Qian, X. Fu, and N. D. Sidiropoulos are with the Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN 55455 USA (e-mail: firstname.lastname@example.org; email@example.com; firstname.lastname@example.org).
© 2017 IEEE.
- Cramér-Rao bound (CRB)
- Phase retrieval
- gradient descent
- impulsive noise
- iterative reweighted least squares (IRLS)