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 language||English (US)|
|Title of host publication||2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||5|
|State||Published - May 18 2016|
|Event||41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Shanghai, China|
Duration: Mar 20 2016 → Mar 25 2016
|Name||ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings|
|Other||41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016|
|Period||3/20/16 → 3/25/16|
Bibliographical noteFunding Information:
The work of N. Sidiropoulos was supported by NSF CIF-1525194. K. Huang was supported by a UMII dissertation fellowship. C. Qian is now as an exchanging PhD student with the Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN 55455 USA, supported in part by the Chinese Scholarship Council
© 2016 IEEE.
Copyright 2016 Elsevier B.V., All rights reserved.
- Cramér-Rao bound (CRB)
- Phase retrieval
- feasible point pursuit (FPP)
- quadratically constrained quadratic programming (QCQP)
- semidefinite programming (SDP)