Least squares phase retrieval using feasible point pursuit

Cheng Qian, Nikolaos Sidiropoulos, Kejun Huang, Lei Huang, H. C. So

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

Phase retrieval has recently attracted renewed interest. It is revisited here through a new approach based on nonconvex quadratically constrained quadratic programming (QCQP). A least-squares (LS) formulation is adopted, and a recently developed non-convex QCQP approximation technique called feasible point pursuit (FPP) is tailored to obtain a new LS-FPP phase retrieval algorithm. The Cramér-Rao bound (CRB) is also derived for phase retrieval under additive white Gaussian noise. We demonstrate through simulations that the LS-FPP method outperforms the prior art and its mean square error approaches the CRB.

Original languageEnglish (US)
Title of host publication2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4288-4292
Number of pages5
ISBN (Electronic)9781479999880
DOIs
StatePublished - May 18 2016
Event41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Shanghai, China
Duration: Mar 20 2016Mar 25 2016

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2016-May
ISSN (Print)1520-6149

Other

Other41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016
CountryChina
CityShanghai
Period3/20/163/25/16

Keywords

  • Cramér-Rao bound (CRB)
  • Phase retrieval
  • feasible point pursuit (FPP)
  • quadratically constrained quadratic programming (QCQP)
  • semidefinite programming (SDP)

Fingerprint Dive into the research topics of 'Least squares phase retrieval using feasible point pursuit'. Together they form a unique fingerprint.

Cite this