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A Class of Chaos-Gaussian Measurement Matrix Based on Logistic Chaos for Compressed Sensing

  • Hongbo BiEmail author
  • Xiaoxue Kong
  • Di Lu
  • Ning Li
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 810)

Abstract

Accurate compressed sensing recovery theoretically depends on a large number of random measurements. In this study, we demonstrated the correlation properties of non-piecewise and piecewise Logistic chaos system to follow Gaussian distribution. The correlation properties can generate a class of Chaos-Gaussian measurement matrix with the low complexity, hardware-friendly implementation and desirable sampling efficiency. Thus, the proposed algorithm constructs Chaos-Gaussian measurement matrix by the sequences. Experimental results show that Chaos-Gaussian measurement matrix can provide comparable performance against Gaussian and Bernoulli random measurement matrix.

Keywords

Compressed sensing Logistic chaos Correlation properties Chaos-Gaussian measurement matrix 

Notes

Acknowledgements

This work is supported by the NEPU Natural Science Foundation under Grant No. 2017PYZL-05, JYCX_CX06_2018 and JYCX_JG06_2018.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.School of Electrical and Information EngineeringNortheast Petroleum UniversityDaqingChina

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