Structured Compressed Sensing: From Theory to Applications

TL;DR

This paper reviews the evolution of structured compressed sensing, emphasizing the transition from theoretical foundations to practical hardware implementations and broader signal models.

Contribution

It provides a comprehensive overview of structured CS, highlighting new sensing architectures, extended signal models, and the connection between theory and real-world applications.

Findings

Structured sensing architectures align with hardware constraints.

Extended signal models include continuous-time signals.

Bridging theory and practice enhances CS applicability.

Abstract

Compressed sensing (CS) is an emerging field that has attracted considerable research interest over the past few years. Previous review articles in CS limit their scope to standard discrete-to-discrete measurement architectures using matrices of randomized nature and signal models based on standard sparsity. In recent years, CS has worked its way into several new application areas. This, in turn, necessitates a fresh look on many of the basics of CS. The random matrix measurement operator must be replaced by more structured sensing architectures that correspond to the characteristics of feasible acquisition hardware. The standard sparsity prior has to be extended to include a much richer class of signals and to encode broader data models, including continuous-time signals. In our overview, the theme is exploiting signal and measurement structure in compressive sensing. The prime focus is bridging theory and practice; that is, to pinpoint the potential of structured CS strategies to emerge from the math to the hardware. Our summary highlights new directions as well as relations to more traditional CS, with the hope of serving both as a review to practitioners wanting to join this emerging field, and as a reference for researchers that attempts to put some of the existing ideas in perspective of practical applications.