Compressed Sensing Recovery using Modified Newton Gradient Pursuit Algorithm and its Application to ECG with Denoising

Thomas, Thomas James; Arun, Anupama; Sheeba, Rani J

doi:10.1109/spcom.2018.8724490

Cited by 2 publications

(2 citation statements)

References 11 publications

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“…In environments characterized by the presence of noise, the robust recovery algorithm proposed by V Meena and G Abhilash, which improves upon the OMP algorithm, has demonstrated the effectiveness of compressed sensing under conditions of high signal-to-noise ratios [45]. Similarly, TJ Thomas and colleagues developed a new algorithm that not only enhances the recovery capabilities of OMP but also offers a new direction for the denoising of ECG signals [46].…”

Section: Contribution(y Amentioning

confidence: 99%

An Improved OMP Algorithm for Enhancing the Anti-Interference Performance of Array Antennas

Gao,

Zhang,

et al. 2024

Sensors

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The demand for precise positioning in noisy environments has propelled the development of research on array antenna radar systems. Although the orthogonal matching pursuit (OMP) algorithm demonstrates superior performance in signal reconstruction, its application efficacy in noisy settings faces challenges. Consequently, this paper introduces an innovative OMP algorithm, DTM_OMP_ICA (a dual-threshold mask OMP algorithm based on independent component analysis), which optimizes the OMP signal reconstruction framework by utilizing two different observation bases in conjunction with independent component analysis (ICA). By implementing a mean mask strategy, it effectively denoises signals received by array antennas in noisy environments. Simulation results reveal that compared to traditional OMP algorithms, the DTM_OMP_ICA algorithm shows significant advantages in noise suppression capability and algorithm stability. Under optimal conditions, this algorithm achieves a noise suppression rate of up to 96.8%, with its stability also reaching as high as 99%. Furthermore, DTM_OMP_ICA surpasses traditional denoising algorithms in practical denoising applications, proving its effectiveness in reconstructing array antenna signals in noisy settings. This presents an efficient method for accurately reconstructing array antenna signals against a noisy backdrop.

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Section: Contribution(y Amentioning

confidence: 99%

An Improved OMP Algorithm for Enhancing the Anti-Interference Performance of Array Antennas

Gao,

Zhang,

et al. 2024

Sensors

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“…Moreover, it has been shown that the greedy algorithms adopted for the sparse signal recovery such as the MP, 22 orthogonal MP, 23 regularized OMP, 24 compressive sampling MP, 25 sparsity adaptive MP, 26 subspace pursuit, 27 and iterative hard thresholding (IHT), 28 although they significantly reduce computational complexity, they do not always guarantee optimal reconstruction. As a promising solution for efficient signal recovery, the Newton‐like method 29 is introduced in some of the greedy algorithms previously cited as refinement step 30–32 …”

Section: Introductionmentioning

confidence: 99%

Iterative Newton hard thresholding‐based approach for cosparse recovery of mmWave massive MIMO channels

Hajjaj

2022

Int J Communication

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Summary Millimeter wave (mmWave) massive multiple‐input multiple‐output (MIMO) channels are cosparse in nature. These cosparse channels share common cosparsity properties, which is interesting when it comes to estimating a large number of mmWave mMIMO channels. Therefore, the channel estimation (CE) problem can be solved by exploiting the cosparsity and common cosupport properties. Specifically, the CE problem can be formulated as a cosparse signal recovery problem, which can be solved using an analysis operator (AO) and the iterative hard thresholding (IHT) algorithm. Prior analysis‐based compressive sensing (ACS) CE techniques adopt an overcomplete AO, which leads usually to a suboptimal solution that cannot guarantee CE accuracy. In this paper, we firstly propose a projected subgradient (PSG) scheme for AO learning. Then, using the previously learned AO, we propose an iterative Newton hard thresholding (INHT)‐based ACS‐CE algorithm. The effectiveness of the proposal has been proven by computational complexity analysis and numerical simulations.

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Compressed Sensing Recovery using Modified Newton Gradient Pursuit Algorithm and its Application to ECG with Denoising

Cited by 2 publications

References 11 publications

An Improved OMP Algorithm for Enhancing the Anti-Interference Performance of Array Antennas

An Improved OMP Algorithm for Enhancing the Anti-Interference Performance of Array Antennas

Iterative Newton hard thresholding‐based approach for cosparse recovery of mmWave massive MIMO channels

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