Smoothing inertial neurodynamic approach for sparse signal reconstruction via Lp-norm minimization

2021 ◽  
Vol 140 ◽  
pp. 100-112
Author(s):  
You Zhao ◽  
Xiaofeng Liao ◽  
Xing He ◽  
Rongqiang Tang ◽  
Weiwei Deng
Keyword(s):  
Signal Reconstruction ◽  
Sparse Signal ◽  
Sparse Signal Reconstruction ◽  
Lp Norm ◽  
Norm Minimization

scholarly journals A New Smoothed L0 Regularization Approach for Sparse Signal Recovery

2019 ◽  
Vol 2019 ◽  
pp. 1-12
Author(s):  
Jianhong Xiang ◽  
Huihui Yue ◽  
Xiangjun Yin ◽  
Linyu Wang
Keyword(s):  
Signal Reconstruction ◽  
Real Data ◽  
Sparse Signal ◽  
Sparse Signal Recovery ◽  
Sparse Signals ◽  
Signal Recovery ◽  
Sparse Signal Reconstruction ◽  
System Equation ◽  
Norm Minimization ◽  
Better Than

Sparse signal reconstruction, as the main link of compressive sensing (CS) theory, has attracted extensive attention in recent years. The essence of sparse signal reconstruction is how to recover the original signal accurately and effectively from an underdetermined linear system equation (ULSE). For this problem, we propose a new algorithm called regularization reweighted smoothed L0 norm minimization algorithm, which is simply called RRSL0 algorithm. Three innovations are made under the framework of this method: (1) a new smoothed function called compound inverse proportional function (CIPF) is proposed; (2) a new reweighted function is proposed; and (3) a mixed conjugate gradient (MCG) method is proposed. In this algorithm, the reweighted function and the new smoothed function are combined as the sparsity promoting objective, and the constraint condition y-Φx22 is taken as a deviation term. Both of them constitute an unconstrained optimization problem under the Tikhonov regularization criterion and the MCG method constructed is used to optimize the problem and realize high-precision reconstruction of sparse signals under noise conditions. Sparse signal recovery experiments on both the simulated and real data show the proposed RRSL0 algorithm performs better than other popular approaches and achieves state-of-the-art performances in signal and image processing.

Underdetermined DOA Estimation for Wideband Signals via Joint Sparse Signal Reconstruction

2019 ◽  
Vol 26 (10) ◽  
pp. 1541-1545 ◽  
Author(s):  
Yunmei Shi ◽  
Xing-Peng Mao ◽  
Chunlei Zhao ◽  
Yong-Tan Liu
Keyword(s):  
Signal Reconstruction ◽  
Doa Estimation ◽  
Sparse Signal ◽  
Sparse Signal Reconstruction ◽  
Wideband Signals

Distributed Compressed Sensing Based Channel Parameter Estimation for MIMO-FBMC Communications

2021 ◽  
Author(s):  
Han Wang ◽  
Xianpeng Wang
Keyword(s):  
Channel Estimation ◽  
Compressed Sensing ◽  
Signal Reconstruction ◽  
Estimation Method ◽  
Sparse Signal ◽  
Weak Selection ◽  
Sparse Signal Reconstruction ◽  
Distributed Compressed Sensing ◽  
Time Frequency ◽  
Selection Threshold

Abstract For the sparse correlation between channels in multiple input multiple output filter bank multicarrier with offset quadrature amplitude modulation (MIMO-FBMC/OQAM) systems, the distributed compressed sensing (DCS)-based channel estimation approach is studied. A sparse adaptive distributed sparse channel estimation method based on weak selection threshold is proposed. Firstly, the correlation between MIMO channels is utilized to represent a joint sparse model, and channel estimation is transformed into a joint sparse signal reconstruction problem. Then, the number of correlation atoms for inner product operation is optimized by weak selection threshold, and sparse signal reconstruction is realized by sparse adaptation. The experiment results show that proposed DCS-based method not only estimates the multipath channel components accurately but also achieves higher channel estimation performance than classical orthogonal matching pursuit (OMP) method and other traditional DCS methods in the time-frequency dual selective channels.

A Differential Evolution Algorithm for Multi-objective Sparse Reconstruction

2021 ◽  
Vol 01 ◽  
Author(s):  
Xiaopei Zhu ◽  
Li Yan ◽  
Boyang Qu ◽  
Pengwei Wen ◽  
Zhao Li
Keyword(s):  
Differential Evolution ◽  
Signal Reconstruction ◽  
Differential Evolution Algorithm ◽  
Magnetic Resonance Images ◽  
Sparse Signal ◽  
Sparse Reconstruction ◽  
Reconstruction Problem ◽  
Sparse Signal Reconstruction ◽  
Multi Objective ◽  
Evolution Algorithm

Aims: This paper proposes a differential evolution algorithm to solve the multi-objective sparse reconstruction problem (DEMOSR). Background: The traditional method is to introduce the regularization coefficient and solve this problem through a regularization framework. But in fact, the sparse reconstruction problem can be regarded as a multi-objective optimization problem about sparsity and measurement error (two contradictory objectives). Objective: A differential evolution algorithm to solve multi-objective sparse reconstruction problem (DEMOSR) in sparse signal reconstruction and the practical application. Methods: First of all, new individuals are generated through tournament selection mechanism and differential evolution. Secondly, the iterative half thresholding algorithm is used for local search to increase the sparsity of the solution. To increase the diversity of solutions, a polynomial mutation strategy is introduced. Results: In sparse signal reconstruction, the performance of DEMOSR is better than MOEA/D-ihalf and StEMO. In addition, it can verify the effectiveness of DEMOSR in practical applications for sparse reconstruction of magnetic resonance images. Conclusions: According to the experimental results of DEMOSR in sparse signal reconstruction and the practical application of reconstructing magnetic resonance images, it can be proved that DEMOSR is effective in sparse signal and image reconstruction.

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