2019 Scientific Conference on Network, Power Systems and Computing
A Compressive Sensing Scheme Based on Zadoff-Chu Measurement Matrix
Zhongpeng Wang, Shoufa Chen, Linpeng Ye
This paper proposes a compressive sensing framework based on encrypted discrete cosine transform (DCT) sparse and encrypted measurement matrix using partial chaotic Zadoff-Chu matrix transform (ZCMT) for image data. In our proposed scheme, a ZCMT is encrypted by a chaotic sequence, in which the initial value is generated by hashing the transformed image data. In the receiver side, the conventional OMP algorithm is employed to recover the original image data. The PSNR and security performances are evaluated by computer simulation. The simulation results are show that the proposed secure CS scheme has better performances that those of the other measurement matrices schemes.
Compressive sensing, Chaos, Zadoff-Chu matrix transform, measurement matrix, sparse basis matrix
Cite this paper:
Zhongpeng Wang, Shoufa Chen, Linpeng Ye, A Compressive Sensing Scheme Based on Zadoff-Chu Measurement Matrix. 2019 Scientific Conference on Network, Power Systems and Computing (NPSC 2019), 2019: 140-143. DOI: https://doi.org/10.33969/EECS.V3.032.