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

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Cognitive Internet of Things: Frameworks, Tools and Applications (ISAIR 2018)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 810))

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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.

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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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Authors and Affiliations

  1. School of Electrical and Information Engineering, Northeast Petroleum University, Daqing, China

    Hongbo Bi, Xiaoxue Kong, Di Lu & Ning Li

Authors
  1. Hongbo Bi
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  2. Xiaoxue Kong
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  3. Di Lu
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  4. Ning Li
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Corresponding author

Correspondence to Hongbo Bi .

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Editors and Affiliations

  1. Department of Mechanical and Control Engineering, Kyushu Institute of Technology, Kitakyushu, Japan

    Huimin Lu

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Bi, H., Kong, X., Lu, D., Li, N. (2020). A Class of Chaos-Gaussian Measurement Matrix Based on Logistic Chaos for Compressed Sensing. In: Lu, H. (eds) Cognitive Internet of Things: Frameworks, Tools and Applications. ISAIR 2018. Studies in Computational Intelligence, vol 810. Springer, Cham. https://doi.org/10.1007/978-3-030-04946-1_18

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