Common-Innovation Subspace Pursuit for Distributed Compressed Sensing in Wireless Sensor Networks

Liu, Jing; Huang, Kaiyu; Yao, Xianghua

doi:10.1109/jsen.2018.2881056

Cited by 19 publications

(4 citation statements)

References 31 publications

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“…The theory notes that [1]- [3] if a signal is sparse or sparse after a certain transformation, the high-dimensional signal can be projected into a low-dimensional space and reconstructed from a small set of low-dimensional data with high probability. CS theory affects many research fields, including radar image processing [4]- [6], blind source separation [7]- [9], sensor networks [10], [11], and Internet of The associate editor coordinating the review of this manuscript and approving it for publication was Zilong Liu.…”

Section: Introductionmentioning

confidence: 99%

Direction of Arrival Estimation by Matching Pursuit Algorithm With Subspace Information

et al. 2021

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Traditional orthogonal matching pursuit (OMP) algorithms for direction of arrival (DOA) estimation suffer from poor angular resolution and noise suppression. In this paper, we analyze the reason why the OMP algorithm has difficulties in resolving closely separated DOAs and conclude that it lies in the rules of support detection. Moreover, we propose a solution to this problem via developing the connection between the sparse reconstruction class algorithm and the subspace algorithm from the structure of the redundant dictionary. Based on the framework of the matching pursuit (MP) algorithm, the effective information of the signal and noise subspaces is integrated, and a noise subspace reprojection orthogonal matching pursuit (NSRomp) algorithm for DOA estimation is proposed. By adopting signal subspaces to reconstruct the original signal, the proposed NSRomp can reduce both the influence of noise on the selection of the support set and the computing time. By implementing the minimum norm method to optimize the noise subspace into a vector, which corrects the selection rules of the support set during each iteration, the angular resolution of the proposed algorithm is improved. From the simulation results, when the signal to noise ratio (SNR) is lower than or near 0, the angular resolution can be improved by > 15 • using OMP algorithms to by < 5 • using the proposed NSRomp algorithm.

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Section: Introductionmentioning

confidence: 99%

Direction of Arrival Estimation by Matching Pursuit Algorithm With Subspace Information

et al. 2021

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“…For the CS reconstruction, some greedy algorithms are proposed to reduce the amount of computation. The greedy algorithms include the matching pursuit [ 35 ], compressive sampling matching pursuit (CoSaMP) [ 36 ], iteratively reweighted least squares (Irls) [ 37 ], subspace pursuit (SP) [ 38 ], and stage-wise orthogonal matching pursuit [ 39 ] algorithms.…”

Section: Methodsmentioning

confidence: 99%

Performance Analysis of Electromyogram Signal Compression Sampling in a Wireless Body Area Network

Zhang

Chen

et al. 2022

Micromachines

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The rapid growth in demand for portable and intelligent hardware has caused tremendous pressure on signal sampling, transfer, and storage resources. As an emerging signal acquisition technology, compressed sensing (CS) has promising application prospects in low-cost wireless sensor networks. To achieve reduced energy consumption and maintain a longer acquisition duration for high sample rate electromyogram (EMG) signals, this paper comprehensively analyzes the compressed sensing method using EMG. A fair comparison is carried out on the performances of 52 ordinary wavelet sparse bases and five widely applied reconstruction algorithms at different compression levels. The experimental results show that the db2 wavelet basis can sparse EMG signals so that the compressed EMG signals are reconstructed properly, thanks to its low percentage root mean square distortion (PRD) values at most compression ratios. In addition, the basis pursuit (BP) reconstruction algorithm can provide a more efficient reconstruction process and better reconstruction performance by comparison. The experiment records and comparative analysis screen out the suitable sparse bases and reconstruction algorithms for EMG signals, acting as prior experiments for further practical applications and also a benchmark for future academic research.

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“…16); l = z t+1 − g; for Each group L G i in l do Construction dictionary D Gi by y G i using PCA. Update α Gi by computing Equation (8). Estimate b Gi by computing Equation ( 9) and Equation (10)…”

Section: A Solve the Z Sub-problemmentioning

confidence: 99%

“…The machinery not only overcomes Nyquist sampling's constraints but also allows for simultaneous signal sampling and compression, lowering the cost of signal storage, transmission, and processing. Among the applications that have aroused the interest of researchers are single-pixel imaging [5], magnetic resonance imaging [6], radar imaging [7], wireless sensor networks [8], limited data computed tomography [9], optical diffusion tomography [10], ultrasound tomography [11], and electron tomography [12].…”

Section: Introductionmentioning

confidence: 99%

Group-Based Sparse Representation for Compressed Sensing Image Reconstruction with Joint Regularization

et al. 2022

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Achieving high-quality reconstructions of images is the focus of research in image compressed sensing. Group sparse representation improves the quality of reconstructed images by exploiting the non-local similarity of images; however, block-matching and dictionary learning in the image group construction process leads to a long reconstruction time and artifacts in the reconstructed images. To solve the above problems, a joint regularized image reconstruction model based on group sparse representation (GSR-JR) is proposed. A group sparse coefficients regularization term ensures the sparsity of the group coefficients and reduces the complexity of the model. The group sparse residual regularization term introduces the prior information of the image to improve the quality of the reconstructed image. The alternating direction multiplier method and iterative thresholding algorithm are applied to solve the optimization problem. Simulation experiments confirm that the optimized GSR-JR model is superior to other advanced image reconstruction models in reconstructed image quality and visual effects. When the sensing rate is 0.1, compared to the group sparse residual constraint with a nonlocal prior (GSRC-NLR) model, the gain of the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) is up to 4.86 dB and 0.1189, respectively.

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Common-Innovation Subspace Pursuit for Distributed Compressed Sensing in Wireless Sensor Networks

Cited by 19 publications

References 31 publications

Direction of Arrival Estimation by Matching Pursuit Algorithm With Subspace Information

Direction of Arrival Estimation by Matching Pursuit Algorithm With Subspace Information

Performance Analysis of Electromyogram Signal Compression Sampling in a Wireless Body Area Network

Group-Based Sparse Representation for Compressed Sensing Image Reconstruction with Joint Regularization

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