RMP: Reduced-set matching pursuit approach for efficient compressed sensing signal reconstruction

Abdel-Sayed, Michael M.; Khattab, Ahmed; Abu-Elyazeed, Mohamed F.

doi:10.1016/j.jare.2016.08.005

Cited by 14 publications

(16 citation statements)

References 23 publications

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“…The support (non-zero) indices of the sparse signals are expected to have relatively large magnitudes of correlation. A number of highest magnitude components of correlated values are chosen in every iteration, and their indices are added to a set of identified supports [19,32,37]. Generally, greedy algorithm exhibit good performance when the prior information on the sparsity of signal (number of the non-zero components) is known.…”

Section: Sparse Recoverymentioning

confidence: 99%

“…However, the samples obtained at or above the Nyquist rate are not fully needed to represent a signal in light of the fact that, from this tremendous acquired samples, a significant portion is immediately discarded and relatively a few important ones are stored at the compression stage. In order to eliminate the collection of unnecessary samples, the compressive sensing (CS) method is proposed that combines compression with signal acquisition step [17,18,19]. CS technique provides the capability to recover the signal from fewer samples or measurements than the conventional approach of reconstructing a signal from acquired data which is governed by the Shannon-Nyquist sampling theorem [16,19,20,21].…”

Section: Introductionmentioning

confidence: 99%

“…In order to eliminate the collection of unnecessary samples, the compressive sensing (CS) method is proposed that combines compression with signal acquisition step [17,18,19]. CS technique provides the capability to recover the signal from fewer samples or measurements than the conventional approach of reconstructing a signal from acquired data which is governed by the Shannon-Nyquist sampling theorem [16,19,20,21]. Therefore, the primary challenge in CR is spectrum sensing, in which the SUs achieve the awareness about the portion of a licensed spectrum that is not being used by PUs.…”

Section: Introductionmentioning

confidence: 99%

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Compressive sensing based maximum-minimum subband energy detection for cognitive radios

Dagne

Fante

Desta

2020

Heliyon

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To satisfy the growing spectrum demands of emerging wireless applications, cognitive radios have been considered as a viable option. It enables dynamic spectrum access opportunistically using wideband spectrum sensing (WSS) methods to discover the temporarily free frequency bands. WSS requires a high-speed analog-to-digital converter (ADC), which has high power consumption and hardware complexity. Improving the power consumption and hardware complexity of the ADC is one of the existing challenges in energy-constrained applications. To alleviate this problem, we propose compressive sensing (CS) in maximum-minimum subband energy detection method to sense the wideband spectrum by utilizing the sparse nature of spectrum occupancy with the minimal possible number of measurements. The CS method uses Fourier Transform and chaotic sequence in designing the measurement matrix to achieve both determinacy and randomness. The Bayesian method is used to reconstruct the signal from the available measurements. From the reconstructed signal, the maximum-minimum subband energy detection (ED) method is used to decide whether the primary user (PU) is absent or present in a particular frequency band. The simulation results show that the proposed CS-based maximum-minimum subband energy detection approach improves the probability of detection by 7.5% compared to the conventional maximum-minimum subband energy detection method of spectrum sensing. The proposed spectrum sensing method is simple and robust to noise uncertainty and signal strength variations.

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Section: Sparse Recoverymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Compressive sensing based maximum-minimum subband energy detection for cognitive radios

Dagne

Fante

Desta

2020

Heliyon

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“…The core purpose of CS framework is to efficiently recover the raw signal with high accuracy from the compressed data, thus, a variety of methodologies have been proposed in recent years. Roughly, the existing construction algorithms can be divided into four classes: greedy pursuit algorithms such as matching pursuit (MP) [ 11 ], orthogonal matching pursuit (OMP) [ 12 , 13 ], etc. ; convex regularization methods such as the family of L1-norm [ 14 ]; non-convex regularization methods such as the family of nonconvex Lp-norm (0 < p < 1) [ 15 , 16 ]; and sparse low rank matrix (SLRM) approaches such as non-separable SLRM regularization [ 17 , 18 , 19 ], etc.…”

Section: Introductionmentioning

confidence: 99%

“…greedy pursuit algorithms such as matching pursuit (MP) [ 11 ], orthogonal matching pursuit (OMP) [ 12 , 13 ], etc. ;…”

Section: Introductionmentioning

confidence: 99%

Multichannel Signals Reconstruction Based on Tunable Q-Factor Wavelet Transform-Morphological Component Analysis and Sparse Bayesian Iteration for Rotating Machines

Hu²,

Peng³

2018

Entropy

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High-speed remote transmission and large-capacity data storage are difficult issues in signals acquisition of rotating machines condition monitoring. To address these concerns, a novel multichannel signals reconstruction approach based on tunable Q-factor wavelet transform-morphological component analysis (TQWT-MCA) and sparse Bayesian iteration algorithm combined with step-impulse dictionary is proposed under the frame of compressed sensing (CS). To begin with, to prevent the periodical impulses loss and effectively separate periodical impulses from the external noise and additive interference components, the TQWT-MCA method is introduced to divide the raw vibration signal into low-resonance component (LRC, i.e., periodical impulses) and high-resonance component (HRC), thus, the periodical impulses are preserved effectively. Then, according to the amplitude range of generated LRC, the step-impulse dictionary atom is designed to match the physical structure of periodical impulses. Furthermore, the periodical impulses and HRC are reconstructed by the sparse Bayesian iteration combined with step-impulse dictionary, respectively, finally, the final reconstructed raw signals are obtained by adding the LRC and HRC, meanwhile, the fidelity of the final reconstructed signals is tested by the envelop spectrum and error analysis, respectively. In this work, the proposed algorithm is applied to simulated signal and engineering multichannel signals of a gearbox with multiple faults. Experimental results demonstrate that the proposed approach significantly improves the reconstructive accuracy compared with the state-of-the-art methods such as non-convex Lq (q = 0.5) regularization, spatiotemporal sparse Bayesian learning (SSBL) and L1-norm, etc. Additionally, the processing time, i.e., speed of storage and transmission has increased dramatically, more importantly, the fault characteristics of the gearbox with multiple faults are detected and saved, i.e., the bearing outer race fault frequency at 170.7 Hz and its harmonics at 341.3 Hz, ball fault frequency at 7.344 Hz and its harmonics at 15.0 Hz, and the gear fault frequency at 23.36 Hz and its harmonics at 47.42 Hz are identified in the envelope spectrum.

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A new greedy sparse recovery algorithm for fast solving sparse representation

Lahaw

Seddik

2021

Vis Comput

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RMP: Reduced-set matching pursuit approach for efficient compressed sensing signal reconstruction

Abstract: Graphical abstract

Cited by 14 publications

References 23 publications

Compressive sensing based maximum-minimum subband energy detection for cognitive radios

Compressive sensing based maximum-minimum subband energy detection for cognitive radios

Multichannel Signals Reconstruction Based on Tunable Q-Factor Wavelet Transform-Morphological Component Analysis and Sparse Bayesian Iteration for Rotating Machines

A new greedy sparse recovery algorithm for fast solving sparse representation

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