Sparse Signal Recovery With Minimization of 1-Norm Minus 2-Norm

Wen, Jinming; Weng, Jian; Tong, Chao; Ren, Chao; Zhou, Zhengchun

doi:10.1109/tvt.2019.2919612

Cited by 53 publications

(29 citation statements)

References 34 publications

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“…In order to solve this question, MAOMP algorithm was proposed. e MAOMP algorithm uses (8), (9), and (10) to estimate sparsity:…”

Section: Maomp Algorithmmentioning

confidence: 99%

“…If (8) is satisfied, the initial sparsity will be increased by 1. If (8) is not satisfied, MAOMP will use (9) and (10) to continue estimating the sparsity. e variable step method can be expressed as (9) and (10).…”

Section: Maomp Algorithmmentioning

confidence: 99%

“…If (8) is not satisfied, MAOMP will use (9) and (10) to continue estimating the sparsity. e variable step method can be expressed as (9) and (10). In (9) and 10, it can be seen that the step size is gradually reduced to 1 with the increase of iteration number.…”

Section: Maomp Algorithmmentioning

confidence: 99%

“…e variable step method can be expressed as (9) and (10). In (9) and 10, it can be seen that the step size is gradually reduced to 1 with the increase of iteration number. is can make the sparsity value more accurate, so the algorithm can improve reconstruction accuracy.…”

Section: Maomp Algorithmmentioning

confidence: 99%

“…e sparse signal reconstruction methods based on l 1 -norm include the basis pursuit (BP) method [6], iterative thresholding (IT) method [7], and homotopy method [8]. Some researchers also proposed the sparse signal recovery method with minimization of l 1 -norm minus l 2 -norm [9]. e matching pursuit algorithm is also a reconstruction algorithm based on l 0 -norm.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Improved Generalized Sparsity Adaptive Matching Pursuit Algorithm Based on Compressive Sensing

Zhao

Jia

2020

Journal of Electrical and Computer Engineering

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The modified adaptive orthogonal matching pursuit algorithm has a lower convergence speed. To overcome this problem, an improved method with faster convergence speed is proposed. In respect of atomic selection, the proposed method computes the correlation between the measurement matrix and residual and then selects the atoms most related to residual to construct the candidate atomic set. The number of selected atoms is the integral multiple of initial step size. In respect of sparsity estimation, the proposed method introduces the exponential function to sparsity estimation. It uses a larger step size to estimate sparsity at the beginning of iteration to accelerate the algorithm convergence speed and a smaller step size to improve the reconstruction accuracy. Simulations show that the proposed method has better performance in terms of convergence speed and reconstruction accuracy for one-dimension signal and two-dimension signal.

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“…In order to solve this question, MAOMP algorithm was proposed. e MAOMP algorithm uses (8), (9), and (10) to estimate sparsity:…”

Section: Maomp Algorithmmentioning

confidence: 99%