A Class of Chaos-Gaussian Measurement Matrix Based on Logistic Chaos for Compressed Sensing
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.
KeywordsCompressed sensing Logistic chaos Correlation properties Chaos-Gaussian measurement matrix
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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