A reducing iteration orthogonal matching pursuit algorithm for compressive sensing

Wang, Rui; Zhang, Jinglei; Ren, Suli; Li, Qingjuan

doi:10.1109/tst.2016.7399284

Cited by 30 publications

(14 citation statements)

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“…This problem becomes a convex optimization problem. The typical reconstruction algorithm can be the greed tracking method [24], convex relaxation method [25], or combination method [26].…”

Section: Basic Theory Of Compressive Sensingmentioning

confidence: 99%

Direct Under-Sampling Compressive Sensing Method for Underwater Echo Signals and Physical Implementation

Sun

Blondel

2019

Applied Sciences

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Compressive sensing can guarantee the recovery accuracy of suitably constrained signals by using sampling rates much lower than the Nyquist limit. This is a leap from signal sampling to information sampling. The measurement matrix is key to implementation but limited in the acquisition systems. This article presents the critical elements of the direct under-sampling—compressive sensing (DUS–CS) method, constructing the under-sampling measurement matrix, combined with a priori information sparse representation and reconstruction, and we show how it can be physically implemented using dedicated hardware. To go beyond the Nyquist constraints, we show how to design and adjust the sampling time of the A/D circuit and how to achieve low-speed random non-uniform direct under-sampling. We applied our method to data measured with different compression ratios (volume ratios of collected data to original data). It is shown that DUS-CS works well when the SNR is 3 dB, 0 dB, −3 dB, and −5 dB and the compression ratio is 50%, 20%, and 10%, and this is validated with both simulation and actual measurements. The method we propose provides an effective way for compressed sensing theory to move toward practical field applications that use underwater echo signals.

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“…This problem becomes a convex optimization problem. The typical reconstruction algorithm can be the greed tracking method [24], convex relaxation method [25], or combination method [26].…”

Section: Basic Theory Of Compressive Sensingmentioning

confidence: 99%

Direct Under-Sampling Compressive Sensing Method for Underwater Echo Signals and Physical Implementation

Sun

Blondel

2019

Applied Sciences

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“…In the CS framework, recovering the original signal from the compressed data represents a significant challenge. In the literature, many sparse signal reconstruction methods have already been presented [12,13]. They can be classified into three major categories: convex relaxation, non-convex relaxation, and greedy algorithms.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Particle Swarm Based Compressive Sensing Technique

Mostafa

Nahas

Mekky

2022

Menoufia Journal of Electronic Engineering Research

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Compressive sensing (CS) has recently gained a lot of attention in the domains of applied mathematics, computer science, and electrical engineering by offering compression of data below the Nyquist rate. The particle swarm optimization (PSO) reconstruction algorithm is considered one of the most widely used evolutionary optimization techniques in CS. The self-tuned PSO parameters control can greatly improve its performance. In this paper, we propose a self-tuned PSO parameter control based on a sigmoid function in the CS framework. In the proposed approach, PSO parameters are adjusted by the evaluation at each iteration. The proposed self-tuned PSO parameter control approach involves two PSO parameters. First, acceleration coefficients, which are considered very effective parameters in enhancing the performance of the algorithm, second, inertia weight, which is used to accelerate the movement of particles towards the optimum point or slow down the particles so that they converge to the optimum. In contrast to conventional PSO, the proposed self-tuned PSO parameters control algorithm governs the convergence rate, resulting in a fast convergence to an optimal solution and very precise recovery of the original signal. A simulation study validates the effectiveness of the proposed method as compared to the conventional PSO algorithm.

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“…It is based on the principle that a sparse signal can be reconstructed from far fewer samples than those required by the classical Shannon-Nyquist sampling theorem by finding the sparsest solution to the underdetermined linear systems. The two essential conditions under the signal reconstruction are sparsity and incoherence [7] . The compressibility and computational asymmetry of the CS theory have made data collection of resource-constrained WSNs very practicable.…”

Section: Introductionmentioning

confidence: 99%

An energy-efficient data collection scheme using denoising autoencoder in wireless sensor networks

Li¹,

Peng²,

Wang³

et al. 2019

Tinshhua Sci. Technol.

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As one of the key operations in Wireless Sensor Networks (WSNs), the energy-efficient data collection schemes have been actively explored in the literature. However, the transform basis for sparsifing the sensed data is usually chosen empirically, and the transformed results are not always the sparsest. In this paper, we propose a Data Collection scheme based on Denoising Autoencoder (DCDA) to solve the above problem. In the data training phase, a Denoising AutoEncoder (DAE) is trained to compute the data measurement matrix and the data reconstruction matrix using the historical sensed data. Then, in the data collection phase, the sensed data of whole network are collected along a data collection tree. The data measurement matrix is utilized to compress the sensed data in each sensor node, and the data reconstruction matrix is utilized to reconstruct the original data in the sink. Finally, the data communication performance and data reconstruction performance of the proposed scheme are evaluated and compared with those of existing schemes using real-world sensed data. The experimental results show that compared to its counterparts, the proposed scheme results in a higher data compression rate, lower energy consumption, more accurate data reconstruction, and faster data reconstruction speed.

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A reducing iteration orthogonal matching pursuit algorithm for compressive sensing

Cited by 30 publications

References 0 publications

Direct Under-Sampling Compressive Sensing Method for Underwater Echo Signals and Physical Implementation

Direct Under-Sampling Compressive Sensing Method for Underwater Echo Signals and Physical Implementation

An Adaptive Particle Swarm Based Compressive Sensing Technique

An energy-efficient data collection scheme using denoising autoencoder in wireless sensor networks

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