Structured Compressed Sensing: From Theory to Applications

By Marco F. Duarte, Yonina C. Eldar. IEEE Transactions on Signal Processing, 2011

1,121 citations recorded by the source corpus.

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.

Subfields: Biomedical Engineering, Computational Mechanics, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/4a02e7a79dadc209e052ab980176f98bd89e5908

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Citations in the corpus

Phase Retrieval with Application to Optical Imaging: A contemporary overview (2015, background): “This yields the constraint , p X p j k j j jk 1 2 # h = = ( / ) 1 2 c m / / / corresponding exactly to a low mixed l1 2 - norm constraint on X [111].”
Distributed Compressive CSIT Estimation and Feedback for FDD Multi-User Massive MIMO Systems (2014, methodology): “works [29]–[32], many later works [33]–[38] have considered structured sparse signals and looked for approaches to exploit the structured sparsity properties.”
Toward real-time terahertz imaging (2018, methodology): “The greedy algorithms are alternatives to the optimization-based algorithms [358].”
Using deep stacking network to improve structured compressed sensing with Multiple Measurement Vectors (2013, background): “An overview of this topic is presented in section V of [6].”
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications (2014, methodology): “A simple introduction to CS is provided in [88].”
A Systematic Review of Compressive Sensing: Concepts, Implementations and Applications (2018, background): “The advantages of structured random matrices are faster acquisition, lesser storage requirement, reproducibility and reduced transmission overhead, while the drawback is the requirement of higher number of measurements compared to random matrices [53]–[55].”
Distributed Compressive Sensing: A Deep Learning Approach (2015, methodology): “In [6], a thorough review on CS methods that exploit the structure present in the sparse signal or in the measurements is presented.”
Compressive Sensing in Electromagnetics - A Review (2015, background): “In addition to their fundamental advantages over Shannon’s theorem-based methodologies, the success of CS formulations is also related to their intrinsic flexibility and generality [1, 11, 12], the effectiveness and the numerical efficiency of the corresponding retrieval techniques [10], and the wid”