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Distributed Compressive Sensing Based Spectrum Sensing Method
2018For multi-antenna system, the difficulties of preforming spectrum sensing are high sampling rate and hardware cost. To alleviate these problems, we propose a novel utilization of distributed compressive sensing for the multi-antenna case. The multi-antenna signals first are sampled in terms of distributed compressive sensing, and then the time-domain ...
Yanping Chen 0007 +2 more
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Noncoherent compressive sensing with application to distributed radar
2011 45th Annual Conference on Information Sciences and Systems, 2011We consider a multi-static radar scenario with spatially dislocated receivers that can individually extract delay information only. Furthermore, we assume that the receivers are not phase-synchronized, so the measurements across receivers can only be combined noncoherently.
Christian R. Berger, José M. F. Moura
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Distributed compressed sensing for despeckling of SAR images
Digital Signal Processing, 2018Abstract Speckle noise is one of the critical disturbances that present in the radar imagery. This noise degrades the quality of synthetic aperture radar (SAR) images and needs to be reduced before using SAR images. This paper investigates a novel method for despeckling of SAR images in the distributed compressed sensing (DCS) framework.
Ahmad Shafiei +2 more
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Distributed compressive sensing in heterogeneous sensor network
Signal Processing, 2016In this paper, we apply distributed compressive sensing (DCS) in heterogeneous sensor network (HSN). Combining different types of measurement matrices and different numbers of measurements, we firstly investigate three different scenarios in which HSN is used for signal acquisition.
Jing Liang 0002, Chengchen Mao
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Perceptual-Based Distributed Compressed Video Sensing
2015 Data Compression Conference, 2015This paper proposes an approach of compressed sensing (CS) of video in which distributed video coding DVC and CS are integrated as in [1], and the sensing matrix is modulated in suit of [2] but with proposed fixed weighting strategy to certain DCT coefficients in an effort to improve the visual quality of reconstruction.
Sawsan Abdellatif Abdelsalam Elsayed +1 more
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Perceptually-aware distributed compressive video sensing
2015 Visual Communications and Image Processing (VCIP), 2015By combining the advantages of distributed video coding (DVC) and compressive sensing (CS), distributed compressive video sensing (DCVS) poses itself as a very promising low-complexity video coding framework for distributed applications. In order to improve the rate-distortion performance of DCVS, much research efforts have been focused on exploring ...
Jin Xu 0005 +3 more
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A greedy pursuit algorithm for distributed compressed sensing
2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2012We develop a greedy pursuit algorithm for solving the distributed compressed sensing problem in a connected network. This algorithm is based on subspace pursuit and uses the mixed support-set signal model. Through experimental evaluation, we show that the distributed algorithm performs significantly better than the standalone (disconnected) solution ...
Dennis Sundman +2 more
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A Decentralized Reconstruction Algorithm for Distributed Compressed Sensing
Wireless Personal Communications, 2017This paper considers the distributed compressed sensing (DCS), where each node has a common component and an innovation component. Most existing reconstruction methods for this DCS model are actually centralized, where the measurements of each signal are utilized together at a certain node.
Wenbo Xu 0003 +3 more
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A joint recovery algorithm for distributed compressed sensing
Transactions on Emerging Telecommunications Technologies, 2012ABSTRACTDistributed compressed sensing exploits the correlation among multiple signals to reduce the number of measurements required for recovery. In this paper, we propose a recovery algorithm for a type of joint sparsity model, where all signals share a common sparse component and each individual signal contains a sparse innovation component.
Wenbo Xu 0003 +3 more
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Distributed Outlier Detection using Compressive Sensing
Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, 2015Computing outliers and related statistical aggregation functions from large-scale big data sources is a critical operation in many cloud computing scenarios, e.g. service quality assurance, fraud detection, or novelty discovery. Such problems commonly have to be solved in a distributed environment where each node only has a local slice of the entirety ...
Ying Yan 0006 +6 more
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