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State of the art and prospects of structured sensing matrices in compressed sensing

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Abstract

Compressed sensing (CS) enables people to acquire the compressed measurements directly and recover sparse or compressible signals faithfully even when the sampling rate is much lower than the Nyquist rate. However, the pure random sensing matrices usually require huge memory for storage and high computational cost for signal reconstruction. Many structured sensing matrices have been proposed recently to simplify the sensing scheme and the hardware implementation in practice. Based on the restricted isometry property and coherence, couples of existing structured sensing matrices are reviewed in this paper, which have special structures, high recovery performance, and many advantages such as the simple construction, fast calculation and easy hardware implementation. The number of measurements and the universality of different structure matrices are compared.

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Authors and Affiliations

  1. Automatic Complex Communication Networks, Signal and Systems Center, Royal Institute of Technology (KTH), Stockholm, 10044, Sweden

    Kezhi Li

  2. Department of Automation, University of Science and Technology of China, Hefei, 230027, China

    Shuang Cong

Authors
  1. Kezhi Li
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  2. Shuang Cong
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Correspondence to Shuang Cong.

Additional information

Kezhi Li is now a research assistant in the Automatic Complex Communication Networks, Signal and Systems Centre, School of Electrical Engineering at the Royal Institute of Technology (KTH), Sweden. He received his PhD in the Imperial College London in 2013. His research interests include signal processing, compressed sensing and its applications in communication, system identification and tomography.

Shuang Cong is a professor in the Department of Automation at the University of Science and Technology of China. She received her PhD in system engineering from the University of Rome “La Sapienza”, Rome, Italy, in 1995. Her research interests include advanced control strategies for motion control, fuzzy logic control, neural networks design and applications, robotic coordination control, and quantum system control.

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Li, K., Cong, S. State of the art and prospects of structured sensing matrices in compressed sensing. Front. Comput. Sci. 9, 665–677 (2015). https://doi.org/10.1007/s11704-015-3326-8

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  • DOI: https://doi.org/10.1007/s11704-015-3326-8

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