Noise Folding in Completely Perturbed Compressed Sensing

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Abstract

This paper first presents a new generally perturbed compressed sensing (CS) model y = (A + E) (x + u) + e, which incorporated a general nonzero perturbation E into sensing matrix A and a noise u into signal x simultaneously based on the standard CS model y = A x + e and is called noise folding in completely perturbed CS model. Our construction mainly will whiten the new proposed CS model and explore in restricted isometry property (R I P) and coherence of the new CS model under some conditions. Finally, we use OMP to give a numerical simulation which shows that our model is feasible although the recovered value of signal is not exact compared with original signal because of measurement noise e, signal noise u, and perturbation E involved.

Figures

  • Figure 1: (a) Recover 𝑥 from 𝑦 = 𝐴𝑥 + 𝑒. The error of the recovered signal value and original signal value is 6.4835𝑒 − 004 by the Euclidean norm which indicates that the recovered signal and original signal are almost the same. (b) Recover𝑥 from𝑦 = 𝐴(𝑥+𝑢)+ 𝑒.The error of the recovered signal value and the original signal value is 6.2258 by the Euclidean norm which means that the recovered signal and original signal are quite different. (c) Recover 𝑥 from 𝑦 = (𝐴+𝐸)(𝑥+𝑢)+𝑒.The error of the recovered signal value and the original signal value is 5.8855 by the Euclideannormwhich indicates that the recovered signal is much different from the original signal. Compared with the change (error) between the recovery signal and the original signal in (a), the changes (errors) in (b) and (c) differ little. What is more, comparing the change between the recovery signal and the original signal of (c) with that of (b), the change of (c) is quite a little bigger because 𝐸 is involved in (c) but 𝐸 is not involved in (b).

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APA

Zhou, L., Niu, X., & Yuan, J. (2016). Noise Folding in Completely Perturbed Compressed Sensing. Journal of Applied Mathematics, 2016. https://doi.org/10.1155/2016/5094239

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