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 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. |
| Pages | 4288-4292 |
| Number of pages | 5 |
| ISBN (Electronic) | 9781479999880 |
| DOIs | |
| 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 |
Publication series
| Name | ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings |
|---|---|
| Volume | 2016-May |
| ISSN (Print) | 1520-6149 |
Other
| Other | 41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 |
|---|---|
| Country/Territory | China |
| City | Shanghai |
| Period | 3/20/16 → 3/25/16 |
Bibliographical note
Publisher Copyright:© 2016 IEEE.
Keywords
- Cramér-Rao bound (CRB)
- Phase retrieval
- feasible point pursuit (FPP)
- quadratically constrained quadratic programming (QCQP)
- semidefinite programming (SDP)