Phaseless Sampling and Reconstruction of Real-Valued Signals in Shift-Invariant Spaces
This work provides theoretical foundations and a practical algorithm for reconstructing real-valued signals in shift-invariant spaces from phaseless samples, benefiting signal processing applications where phase information is unavailable.
The paper addresses phaseless sampling and reconstruction of real-valued signals in shift-invariant spaces, introducing a graph-based characterization for signal determination from magnitude measurements and a stable reconstruction method from discrete phaseless samples. Numerical simulations demonstrate robust reconstruction of box spline signals from noisy samples.
Sampling in shift-invariant spaces is a realistic model for signals with smooth spectrum. In this paper, we consider phaseless sampling and reconstruction of real-valued signals in a shift-invariant space from their magnitude measurements on the whole Euclidean space and from their phaseless samples taken on a discrete set with finite sampling density. We introduce an undirected graph to a signal and use connectivity of the graph to characterize whether the signal can be determined, up to a sign, from its magnitude measurements on the whole Euclidean space. Under the local complement property assumption on a shift-invariant space, we find a discrete set with finite sampling density such that signals in the shift-invariant space, that are determined from their magnitude measurements on the whole Euclidean space, can be reconstructed in a stable way from their phaseless samples taken on that discrete set. In this paper, we also propose a reconstruction algorithm which provides a suboptimal approximation to the original signal when its noisy phaseless samples are available only. Finally, numerical simulations are performed to demonstrate the robust reconstruction of box spline signals from their noisy phaseless samples.