Compressive Sensing Algorithms for Signal Processing Applications: A Survey

Abo‐Zahhad, Mohammed; Hussein, Aziza I.; Mohamed, Abdelfatah M.

doi:10.4236/ijcns.2015.86021

Cited by 53 publications

(55 citation statements)

References 29 publications

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“…Compressive sensing is an exciting, rapidly growing field which has attracted considerable attention in electrical engineering, applied mathematics, statistics, and computer science [75]. This paradigm has been applied to many signal processing areas such as image processing, communication and networks.…”

Section: Compressive Sensing Applicationsmentioning

confidence: 99%

“…Standard methods for radar imaging actually also use the sparsity assumption, but only at the very end of the signal processing procedure in order to clean up the noise in the resulting image. Using sparsity systematically from the very beginning by exploiting compressive sensing methods is therefore a natural approach [75], [80]. …”

Section: Compressive Radarmentioning

confidence: 99%

“…Due to their limited power supply, energy consumption is a key issue in the design of protocols and algorithms for WSNs. Compressed sensing and distributed compressed sensing have been adopted as potential approaches to provide energy efficient sensing in wireless sensor networks [75]. In another study, CS theory to sensor data gathering for large scale wireless sensor networks (WSNs) was first developed by considering the scenario in which a large number of sensor nodes are densely deployed and sensor readings are spatially correlated.…”

Section: Cs In Wireless Sensor Networkmentioning

confidence: 99%

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Improved reconstruction in compressive sensing of clustered signals

Barzideh

Lundheim

2015

Africon 2015

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This thesis deals with an emerging area of signal processing, called Compressive Sensing (CS), that allows the reconstruction of sparse or compressible signals from fewer measurements than are used in traditional schemes. Like traditional signal representation schemes, CS follows a similar framework: encoding, transmission/storing, and decoding. The encoding part is done using random projection (RP) or random sensing, and the decoding is done via nonlinear reconstruction algorithms from a reduced amount of measurements. The performance of the reconstruction schemes used and the application of such paradigm are the two main focuses of the thesis. It has three parts: the introduction, performance analysis of recovery algorithms in CS and some applications of CS.The introductory part provides the background for the following four chapters. It begins by defining the basic concepts used in CS theory and presents the Bayesian framework. Further, an analytical tool from statistical mechanics for performance analysis of physical systems is introduced applied on a non-noisy CS problem. The Bayesian framework is given ample emphasis in the thesis for two reasons. First, it serves as a bridge between the recovery algorithms used in CS and a tool from the statistical mechanics, called the replica method. Second, it is used as main framework to incorporate different prior signal information, like sparsity and clusteredness. Furthermore, a short description of CS applications is given before the introduction concludes by presenting the scope and the contribution of the thesis.The second part of the thesis deals with the study of the performance of recovery methods in CS systems using the Replica Method. At the beginning, the study of the performance of the recovery algorithms in CS was focused on the ratio of the amount of measurement used, the sparsity level, and how accurate the recovered information is. However, there was a luck of universal performance analysis. The Replica method provides this by considering large size systems. This thesis contributes to such analysis via the Bayesian framework. In this work noisy CS systems are considered and the recovering algorithms are reinterpreted as a maximum a posteriori (MAP) estimator. It, therefore, provides replica analysis including one step replica breaking ansatz for CS systems as an extension of similar analysis done for other systems like multiple input/multiple output (MIMO).The CS application is the third part of the thesis. The theory of CS has been applied in many signal processing fields such as image processing, communication, networks and so on. There are hundreds, if not thousands of articles on this subject at present. In this thesis, there are novel results that contribute to the application of CS theory. The theory is applied to limited feedback in temporally correlated MIMO channels, where the sparsity property was used to reduce feedback overhead significantly while delivering the same performance. Further, including another assumption, i.e., more st...

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Section: Compressive Sensing Applicationsmentioning

confidence: 99%

Section: Compressive Radarmentioning

confidence: 99%

Section: Cs In Wireless Sensor Networkmentioning

confidence: 99%

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Improved reconstruction in compressive sensing of clustered signals

Barzideh

Lundheim

2015

Africon 2015

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“…The former underlines a signal distribution, and the latter introduces one or two variables, which control the sparse signal. Bayesian algorithms achieve a balance of high accuracy and short recovery time [9, 10]. In recent years, some scholars have applied Swarm Intelligence Algorithms to signal recovery in CS, such as Particle Swarm Optimization (PSO) [11] and the Grey Wolf Optimizer Algorithm [12].…”

Section: Introductionmentioning

confidence: 99%

A Bat-Inspired Sparse Recovery Algorithm for Compressed Sensing

Bao

Liu

Huang

et al. 2018

Computational Intelligence and Neuroscience

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Compressed sensing (CS) is an important research area of signal sampling and compression, and the essence of signal recovery in CS is an optimization problem of solving the underdetermined system of equations. Greedy pursuit algorithms are widely used to solve this problem. They have low computational complexity; however, their recovery performance is limited. In this paper, an intelligence recovery algorithm is proposed by combining the Bat Algorithm (BA) and the pruning technique in subspace pursuit. Experimental results illustrate that the proposed algorithm has better recovery performance than greedy pursuit algorithms. Moreover, applied to the microseismic monitoring system, the BA can recover the signal well.

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“…Because of their simplicity, more interest has been paid to random matrices. These matrices are randomly generated with independent and identically distributed (i.i.d) elements such as Gaussian and Bernoulli distributions [5,6]. In general, compressive sensing requires that the sampling matrix satisfies the Restrict Isometry Property (RIP) condition [10].…”

Section: Introductionmentioning

confidence: 99%

Bayesian compressive sensing with circulant matrix for spectrum sensing in cognitive radio networks

Salahdine

Kaabouch

Ghazi

2016

2016 IEEE 7th Annual Ubiquitous Computing, Electronics &Amp; Mobile Communication Conference (UEMCON)

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For wideband spectrum sensing, compressive sensing has been proposed as a solution to speed up the high dimensional signals sensing and reduce the computational complexity. Compressive sensing consists of acquiring the essential information from a sparse signal and recovering it at the receiver based on an efficient sampling matrix and a reconstruction technique. In order to deal with the uncertainty, improve the signal acquisition performance, and reduce the randomness during the sensing and reconstruction processes, compressive sensing requires a robust sampling matrix and an efficient reconstruction technique. In this paper, we propose an approach that combines the advantages of a Circulant matrix with Bayesian models. This approach is implemented, extensively tested, and its results have been compared to those of 1 norm minimization with a Circulant or random matrix based on several metrics. These metrics are Mean Square Error, reconstruction error, correlation, recovery time, sampling time, and processing time. The results show that our technique is faster and more efficient.

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Compressive Sensing Algorithms for Signal Processing Applications: A Survey

Cited by 53 publications

References 29 publications

Improved reconstruction in compressive sensing of clustered signals

Improved reconstruction in compressive sensing of clustered signals

A Bat-Inspired Sparse Recovery Algorithm for Compressed Sensing

Bayesian compressive sensing with circulant matrix for spectrum sensing in cognitive radio networks

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