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Robust reconstruction algorithm for compressed sensing in Gaussian noise environment using orthogonal matching pursuit with partially known support and random subsampling

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EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING 2012年第1期2012卷 1-21页

作者： Sermwuthisarn, Parichat Auethavekiat, Supatana Gansawat, Duangrat Patanavijit, Vorapoj Chulalongkorn Univ Dept Elect Engn Bangkok 10330 Thailand Natl Elect & Comp Technol Ctr Pathum Thani Thailand Assumption Univ Dept Elect Engn Bangkok 10240 Thailand

The compressed signal in compressed sensing (CS) may be corrupted by noise during transmission. The effect of Gaussian noise can be reduced by averaging, hence a robust reconstruction method using compressed signal ensemble from one compressed signal is proposed. The compressed signal is subsampled for L times to create the ensemble of L compressed signals. Orthogonal matching pursuit with partially known support (OMP-PKS) is applied to each signal in the ensemble to reconstruct L noisy outputs. The L noisy outputs are then averaged for denoising. The proposed method in this article is designed for CS reconstruction of image signal. The performance of our proposed method was compared with basis pursuit denoising, Lorentzian-based iterative hard thresholding, OMP-PKS and distributed compressed sensing using simultaneously orthogonal matching pursuit. The experimental results of 42 standard test images showed that our proposed method yielded higher peak signal-to-noise ratio at low measurement rate and better visual quality in all cases.

关键词： compressed sensing (CS) orthogonal matching pursuit (OMP) distributed compressed sensing model-based method

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The compressed sensing of Wireless Sensor Networks Based on Internet of Things

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IEEE SENSORS JOURNAL 2021年第22期21卷 25267-25273页

作者： Wei, Pengcheng He, Fangcheng Chongqing Univ Educ Sch Math & Informat Engn Chongqing 400065 Peoples R China Chongqing Univ Educ Coll Foreign Languages Literature Chongqing 400065 Peoples R China

In the application of wireless sensor networks, poor computing power, limited storage space, and short duration have severely restricted the development of wireless sensor networks. compressed sensing technology is an emerging signal front-end processing technology, which breaks the original use restrictions, saves storage space and energy consumption, and has become a research hotspot in the field of electronic technology. Based on the wireless network, this paper combines the big data P2P direct connection perception idea to expand the compression algorithm spatially. First, locate the data on different nodes, then perform task allocation on the data compression calculation in the cloud, and finally wait for the node to compress the data to be compressed. The results show that compressed sensing technology can accurately locate objects, overcome node defects in wireless sensor networks, reduce node energy consumption, improve data processing capabilities and calculation speed, and track targets. Specific references for more accurate investigation and positioning.

关键词： Wireless sensor networks Sensors compressed sensing Wireless communication Energy consumption Sensor systems Internet of Things compressed sensing distributed compressed sensing Internet of Things wireless sensor network

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Cooperative Radio Source Positioning and Power Map Reconstruction: A Sparse Bayesian Learning Approach

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2015年第6期64卷 2318-2332页

作者： Huang, Din-Hwa Wu, Sau-Hsuan Wu, Wen-Rong Wang, Peng-Hua Natl Chiao Tung Univ Dept Elect & Comp Engn Hsinchu 300 Taiwan Natl Taipei Univ Dept Commun Engn Taipei 237 Taiwan

It is known that in addition to spectrum sparsity, spatial sparsity can also be used to further enhance spectral utilization in cognitive radio systems. To achieve that, secondary users (SUs) must know the locations and signal strength distributions (SSDs) of primary users' base stations (PUBSs). Recently, a group sparse total least squares method was developed to cooperatively sense the PUBSs' signal strength and estimate their locations. It approximates PUBSs' power decay with a path loss model (PLM), assumes PUBSs' locations on some grid points, and then accomplishes the estimation tasks. However, the parameters of the PLM have to be known in advance, and the accuracy of the location estimation is bounded by the resolution of the grid points, which limit its practical applications. In this paper, we propose a sparse Bayesian learning method to solve the problems. We use a Laplacian function to model the power decay of a PUBS and then derive learning rules to estimate corresponding parameters. The distinct features of the proposed method are that most parameters are adaptively estimated, and little prior information is needed. To further enhance the performance, we incorporate source number detection methods in the proposed algorithm such that the number of the PUBSs can be precisely detected, facilitating the estimation of PUBSs' locations and SSDs. Moreover, the proposed algorithm is modified into a recursive mode to adapt to SUs' mobility and time-variant observations. Simulations show that the proposed algorithm has good performance, even when the spatial measurement rate is low.

关键词： Cognitive radio distributed compressed sensing localization sparse Bayesian learning spatial sparsity spectrum sensing

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distributed Multi-View Sparse Vector Recovery

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2023年 71卷 1448-1463页

作者： Tian, Zhuojun Zhang, Zhaoyang Hanzo, Lajos Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310007 Peoples R China Zhejiang Univ Zhejiang Prov Key Lab Informat Proc Commun & Netwo Hangzhou 310007 Peoples R China Zhejiang Univ Zhejiang Prov Key Lab Collaborat Sensing & Autonom Hangzhou Peoples R China Univ Southampton Dept Elect & Comp Sci Southampton SO17 1BF England

In this paper, we consider a multi-view compressed sensing problem, where each sensor can only obtain a partial view of the global sparse vector. Here the partial view means that some arbitrary and unknown indices of the global vector are unobservable to that sensor and do not contribute to the measurement outputs. The sensors aim to collaboratively recover the global state vector in a decentralized manner. We formulate this recovery problem as a bilinear optimization problem relying on a factored joint sparsity model (FJSM), in which the variables are factorized into a node-specific sparse local masking vector and the desired common sparse global vector. We first theoretically analyze the general conditions guaranteeing the global vector's successful recovery. Then we propose a novel in-network algorithm based on the powerful distributed alternating direction method of multipliers (ADMM), which can reconstruct the vectors and achieve consensus among nodes concerning the estimation of the global vector. Specifically, each node alternately updates the common global vector and its local masking vector, and then it transfers the estimated global vector to its neighboring nodes for further updates. To avoid potential divergence of the iterative algorithm, we propose an early stopping rule for the estimation of the local masking vectors and further conceive an estimation error-mitigation algorithm. The convergence of the proposed algorithms is theoretically proved. Finally, extensive simulations validate their excellent performance both in terms of the convergence and recovery accuracy.

关键词： Sensors Convergence Optimization Convex functions compressed sensing Cloud computing Estimation Sensor network multi-view sparse vector recovery distributed compressed sensing distributed optimization alternating direction method of multipliers (ADMM)

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Efficient and Robust distributed Digital Codec Framework for Jointly Sparse Correlated Signals

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IEEE ACCESS 2019年 7卷 77374-77386页

作者： Chen, Xuechen Li, Fan Liu, Xingcheng Sun Yat Sen Univ Sch Elect & Informat Technol Guangzhou 510006 Guangdong Peoples R China

In this paper, we propose a novel distributed digital transmission framework for two jointly sparse correlated signals. First, the non-zero coefficients of each signal are quantized by a standard quantizer or a novel distributed quantizer, as appropriate. Then, these quantized values are mapped to the elements of a finite field, except 0, while the zero coefficients are mapped to 0. Subsequently, compressed sensing over finite fields is applied to obtain measurements. We name such an order first quantization then compressed sensing. The two measurement signals are then converted to bit sequences, modulated, and transmitted through separate additive white Gaussian noise (AWGN) channels. At the central receiver, which has access to both channels, following demodulation, an innovative joint belief propagation (JBP) algorithm is performed for joint recovery. In this algorithm, we introduce a new type of constraint node, i.e., correlation constraint nodes, which connect two factor graphs that separately represent the CS encoding matrix of each signal. The experimental results prove that under the same framework the proposed scheme provides significant performance improvements compared to the scheme that ignores the correlated information between jointly sparse signals, especially when the correlation coefficient is high. Then, to answer the question of which order is better, we construct the first compressed sensing, then quantization framework, for fairness, two cutting edge jointly greedy pursuit algorithms are separately adopted at the joint decoder. Through simulations, we validate that the proposed framework provides more effective and robust transmissions.

关键词： Jointly sparse signals joint belief propagation distributed quantizer distributed compressed sensing LDPC matrix

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distributed greedy pursuit algorithms

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SIGNAL PROCESSING 2014年 105卷 298-315页

作者： Sundman, Dennis Chatterjee, Saikat Skoglund, Mikael KTH Royal Inst Technol Sch Elect Engn S-10044 Stockholm Sweden KTH Royal Inst Technol ACCESS Linneaus Ctr S-10044 Stockholm Sweden

For compressed sensing over arbitrarily connected networks, we consider the problem of estimating underlying sparse signals in a distributed manner. We introduce a new signal model that helps to describe inter-signal correlation among connected nodes. Based on this signal model along with a brief survey of existing greedy algorithms, we develop distributed greedy algorithms with low communication overhead. Incorporating appropriate modifications, we design two new distributed algorithms where the local algorithms are based on appropriately modified existing orthogonal matching pursuit and subspace pursuit. Further, by combining advantages of these two local algorithms, we design a new greedy algorithm that is well suited for a distributed scenario. By extensive simulations we demonstrate that the new algorithms in a sparsely connected network provide good performance, close to the performance of a centralized greedy solution. (c) 2014 Elsevier B.V. All rights reserved.

关键词： Greedy algorithms compressed sensing distributed compressed sensing

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Exploiting joint sparsity for far-field microphone array sound source localization

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APPLIED ACOUSTICS 2020年 159卷 107100-000页

作者： Zheng, Siyuan Tong, F. Huang, Huixiang Guo, Qiuhan Xiamen Univ Minister Educ Key Lab Underwater Acoust Commun & Marine Informa Xiamen Fujian Peoples R China

The presence of far-field noise and reverberation poses significant challenges to the conventional microphone array sound source localization approaches. Consider the sparsity contained in the source direction vector, source localization can be transformed into a compressed sensing (CS) problem by constructing the redundancy frequency domain room impulse response (RIR) matrix as CS measurement matrix. In this paper a new sparse recovery model is derived by decomposing the RIR into delay response term and reverberation response term to facilitate reverberation mitigation via frequency domain accumulation. Furthermore, as the source direction vector of adjacent speech frames tends to exhibit similar sparse pattern, namely, the direction of source can be assumed to keep static within this short period, thus there exists substantial correlation of spatial sparsity among adjacent speech frames. In this paper, under the framework of distributed compressed sensing (DCS), multiple source direction vectors are treated as sparse solutions with common spatial support to derive a joint sparse recovery algorithm for far-field source localization. The experimental results obtained in the context of a uniform circle array (UCA) show that the proposed algorithm is capable of yielding better estimation performance compared with the traditional algorithms. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Microphone array distributed compressed sensing Sound source localization

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Off-Grid Direction of Arrival Estimation Based on Joint Spatial Sparsity for distributed Sparse Linear Arrays

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SENSORS 2014年第11期14卷 21981-22000页

作者： Liang, Yujie Ying, Rendong Lu, Zhenqi Liu, Peilin Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China

In the design phase of sensor arrays during array signal processing, the estimation performance and system cost are largely determined by array aperture size. In this article, we address the problem of joint direction-of-arrival (DOA) estimation with distributed sparse linear arrays (SLAs) and propose an off-grid synchronous approach based on distributed compressed sensing to obtain larger array aperture. We focus on the complex source distribution in the practical applications and classify the sources into common and innovation parts according to whether a signal of source can impinge on all the SLAs or a specific one. For each SLA, we construct a corresponding virtual uniform linear array (ULA) to create the relationship of random linear map between the signals respectively observed by these two arrays. The signal ensembles including the common/innovation sources for different SLAs are abstracted as a joint spatial sparsity model. And we use the minimization of concatenated atomic norm via semidefinite programming to solve the problem of joint DOA estimation. Joint calculation of the signals observed by all the SLAs exploits their redundancy caused by the common sources and decreases the requirement of array size. The numerical results illustrate the advantages of the proposed approach.

关键词： off-grid joint spatial sparsity distributed sparse linear arrays direction of arrival estimation concatenated atomic norm semidefine program distributed compressed sensing

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Sparse Recovery Optimization in Wireless Sensor Networks with a Sub-Nyquist Sampling Rate

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SENSORS 2015年第7期15卷 16654-16673页

作者： Brunelli, Davide Caione, Carlo Univ Trento I-38122 Trento Italy Univ Bologna I-40136 Bologna Italy

Compressive sensing (CS) is a new technology in digital signal processing capable of high-resolution capture of physical signals from few measurements, which promises impressive improvements in the field of wireless sensor networks (WSNs). In this work, we extensively investigate the effectiveness of compressive sensing (CS) when real COTSresource-constrained sensor nodes are used for compression, evaluating how the different parameters can affect the energy consumption and the lifetime of the device. Using data from a real dataset, we compare an implementation of CS using dense encoding matrices, where samples are gathered at a Nyquist rate, with the reconstruction of signals sampled at a sub-Nyquist rate. The quality of recovery is addressed, and several algorithms are used for reconstruction exploiting the intra- and inter-signal correlation structures. We finally define an optimal under-sampling ratio and reconstruction algorithm capable of achieving the best reconstruction at the minimum energy spent for the compression. The results are verified against a set of different kinds of sensors on several nodes used for environmental monitoring.

关键词： compressed sensing wireless sensor networks distributed compressed sensing embedded software low-power electronics

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Joint-Sparse Signal Reconstruction Based on Common Support Set Refinement

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IEEE SIGNAL PROCESSING LETTERS 2019年第9期26卷 1373-1377页

作者： Li, Fulin Hon, Shaohua Gu, Yujie Wang, Lin Xiamen Univ Dept Commun Engn Xiamen 361005 Fujian Peoples R China Temple Univ Dept Elect & Comp Engn Philadelphia PA 19122 USA

Joint-sparse signal reconstruction is a key issue in distributed compressed sensing based on the mixed support set model. In this letter, a novel joint-sparse signal reconstruction algorithm is proposed based on the common support set refinement. The common support set is first roughly estimated by greedy pursuit algorithms. The roughly estimated common support set is then refined by pruning the incorrect elements. With the refined common support set, greedy pursuit is utilized to reconstruct the joint-sparse signals. The complexity analysis and simulation results indicate that the proposed algorithm achieves better estimates of the support sets and finally reduces the reconstruction error for the joint-sparse signals with a moderate complexity compared with the state-of-the-art algorithms.

关键词： Common support set refinement distributed compressed sensing greedy pursuit joint-sparse signals mixed support set model

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