Compressive Phase Retrieval From Squared Output Measurements Via Semidefinite Programming

Abstract

Given a linear system in a real or complex domain, linear regression aims to recover the model parameters from a set of observations. Recent studies in compressive sensing have successfully shown that under certain conditions, a linear program, namely, l1-minimization, guarantees recovery of sparse parameter signals even when the system is underdetermined. In this paper, we consider a more challenging problem: when the phase of the output measurements from a linear system is omitted. Using a lifting technique, we show that even though the phase information is missing, the sparse signal can be recovered exactly by solving a simple semidefinite program when the sampling rate is sufficiently high, albeit the exact solutions to both sparse signal recovery and phase retrieval are combinatorial. The results extend the type of applications that compressive sensing can be applied to those where only output magnitudes can be observed. We demonstrate the accuracy of the algorithms through theoretical analysis, extensive simulations and a practical experiment.

Parts of the derivations have submitted to the 16th IFAC Symposium on System Identification, SYSID 2012, and parts to the 51st IEEE Conference on Decision and Control, CDC 2012

References in corpus (4)

• Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization
• Compressive Phase Retrieval From Squared Output Measurements Via Semidefinite Programming
• Phase retrieval and saddle-point optimization
• On the performance of algorithms for the minimization of ${\ell }_1$-penalized functionals

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