DOI: 10.3724/SP.J.1004.2013.01980

Acta Automatica Sinica (自动化学报) 2013/39:12 PP.1980-1995

Research Advances on Structured Compressive Sensing

Compressive sensing (CS) is a newly developed theoretical framework for information acquisition and processing. Using the non-linear optimization methods, the signals can be recovered from fewer linear and non-adaptive measurements by taking advantage of the sparsity or compressibility inherent in real world signals. Structured compressive sensing is a new framework which can treat more general signal classes to achieve the accurate and effective reconstruction in practice by introducing the prior information matching with data acquisition hardware and complicated signal models to traditional compressive sensing. In this paper, the basic models and key techniques of structured compressive sensing are introduced in terms of the structured measurements, the structured dictionary representation and the structured signal reconstruction, which correspond to three basic aspects of compressive sensing, and the recent developments of structured compressive sensing are reviewed in detail. Finally, the current and future challenges of the structured compressive sensing are discussed.

Key words:Compressive sensing (CS), compressive measurement, sparse representation, signal reconstruction, structured model

ReleaseDate:2014-07-21 17:04:33

Funds:National Basic Research Program of China (973 Program) (2013CB329402), National Natural Science Foundation of China (61072106, 61072108, 61173090, 61272023), Fund for Foreign Scholars in University Research and Teaching Programs (111 Project) (B07048), Program for Cheung Kong Scholars and Innovative Research Team in University (IRT1170), National Research Foundation for the Doctoral Program of Higher Education of China (20110203110006), and the Open Research Fund Program of Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China (IPIU012011002)