Sparse Signal Recovery Based on Compressive Sensing and Exploration Using Multiple Mobile Sensors

Sparse Signal Recovery Based on Compressive Sensing and Exploration Using Multiple Mobile Sensors

[1]  Daniel Hernández-Lobato,et al.  Generalized spike-and-slab priors for Bayesian group feature selection using expectation propagation , 2013, J. Mach. Learn. Res..

[2]  Ting Sun,et al.  Single-pixel imaging via compressive sampling , 2008, IEEE Signal Process. Mag..

[3]  Florent Krzakala,et al.  Approximate message passing with restricted Boltzmann machine priors , 2015, ArXiv.

[4]  Kaushik Mahata,et al.  A Robust Algorithm for Joint-Sparse Recovery , 2009, IEEE Signal Processing Letters.

[5]  Philip Schniter,et al.  Turbo reconstruction of structured sparse signals , 2010, 2010 44th Annual Conference on Information Sciences and Systems (CISS).

[6]  Massimo Fornasier,et al.  Compressive Sensing , 2015, Handbook of Mathematical Methods in Imaging.

[7]  Wynn C. Stirling Multi-Agent Coordinated Decision-Making Using Epistemic Utility Theory , 1992, MAAMAW.

[8]  Y. C. Pati,et al.  Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition , 1993, Proceedings of 27th Asilomar Conference on Signals, Systems and Computers.

[9]  Carl E. Rasmussen,et al.  Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.

[10]  Ahmed Khattab,et al.  RMP: Reduced-set matching pursuit approach for efficient compressed sensing signal reconstruction☆☆☆ , 2016, Journal of advanced research.

[11]  Wei Lu,et al.  Modified-CS: Modifying compressive sensing for problems with partially known support , 2009, 2009 IEEE International Symposium on Information Theory.

[12]  Sung Wook Paek,et al.  Reconfigurable satellite constellations for geo-spatially adaptive Earth observation missions , 2012 .

[13]  Timothy C. Coburn,et al.  Geostatistics for Natural Resources Evaluation , 2000, Technometrics.

[14]  Zhilin Zhang,et al.  Exploiting Correlation in Sparse Signal Recovery Problems: Multiple Measurement Vectors, Block Sparsity, and Time-Varying Sparsity , 2011, ArXiv.

[15]  Jian Wang,et al.  A Sharp Condition for Exact Support Recovery With Orthogonal Matching Pursuit , 2017, IEEE Transactions on Signal Processing.

[16]  Bhaskar D. Rao,et al.  Extension of SBL Algorithms for the Recovery of Block Sparse Signals With Intra-Block Correlation , 2012, IEEE Transactions on Signal Processing.

[17]  M. R. Holdaway,et al.  Spatial modeling and interpolation of monthly temperature using kriging , 2006 .

[18]  Cédric Richard,et al.  Detection of nonlinear mixtures using Gaussian processes: Application to hyperspectral imaging , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[19]  Andrea Montanari,et al.  Message passing algorithms for compressed sensing: I. motivation and construction , 2009, 2010 IEEE Information Theory Workshop on Information Theory (ITW 2010, Cairo).

[20]  Chengbo Li An efficient algorithm for total variation regularization with applications to the single pixel camera and compressive sensing , 2010 .

[21]  Rabab Kreidieh Ward,et al.  Distributed Compressive Sensing: A Deep Learning Approach , 2015, IEEE Transactions on Signal Processing.

[22]  Dominique Courault,et al.  Spatial interpolation of air temperature using environmental context: Application to a crop model , 2001, Environmental and Ecological Statistics.

[23]  Jie Chen,et al.  Theoretical Results on Sparse Representations of Multiple-Measurement Vectors , 2006, IEEE Transactions on Signal Processing.

[24]  Bhaskar D. Rao,et al.  Sparse Signal Recovery With Temporally Correlated Source Vectors Using Sparse Bayesian Learning , 2011, IEEE Journal of Selected Topics in Signal Processing.

[25]  Bhaskar D. Rao,et al.  On the benefits of the block-sparsity structure in sparse signal recovery , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[26]  Albert Wang,et al.  The In-Crowd Algorithm for Fast Basis Pursuit Denoising , 2011, IEEE Transactions on Signal Processing.

[27]  Andrew Gelman,et al.  General methods for monitoring convergence of iterative simulations , 1998 .

[28]  Emmanuel J. Candès,et al.  NESTA: A Fast and Accurate First-Order Method for Sparse Recovery , 2009, SIAM J. Imaging Sci..

[29]  T. Choi,et al.  Gaussian Process Regression Analysis for Functional Data , 2011 .

[30]  Shengli Zhou,et al.  Application of compressive sensing to sparse channel estimation , 2010, IEEE Communications Magazine.

[31]  Yonina C. Eldar,et al.  Block-Sparse Signals: Uncertainty Relations and Efficient Recovery , 2009, IEEE Transactions on Signal Processing.

[32]  David B. Dunson,et al.  Multi-task compressive sensing with Dirichlet process priors , 2008, ICML '08.

[33]  Xiao-Ping Zhang,et al.  Direction-of-arrival estimation using sparse variable projection optimization , 2012, 2012 IEEE International Symposium on Circuits and Systems.

[34]  M. Yuan,et al.  Model selection and estimation in regression with grouped variables , 2006 .

[35]  Shinichi Nakajima,et al.  Bayesian Group-Sparse Modeling and Variational Inference , 2014, IEEE Transactions on Signal Processing.

[36]  Marc Teboulle,et al.  A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems , 2009, SIAM J. Imaging Sci..

[37]  R. Tibshirani,et al.  Sparsity and smoothness via the fused lasso , 2005 .

[38]  Philip Schniter,et al.  Expectation-maximization Bernoulli-Gaussian approximate message passing , 2011, 2011 Conference Record of the Forty Fifth Asilomar Conference on Signals, Systems and Computers (ASILOMAR).

[39]  Éric Marchand,et al.  Active sensor placement for complete scene reconstruction and exploration , 1997, Proceedings of International Conference on Robotics and Automation.

[40]  Joachim H. G. Ender,et al.  On compressive sensing applied to radar , 2010, Signal Process..

[41]  Matti Latva-aho,et al.  Bayesian method for image recovery from block compressive sensing , 2016, 2016 50th Asilomar Conference on Signals, Systems and Computers.

[42]  Ljubiša Stanković,et al.  Adaptive Variable Step Algorithm for Missing Samples Recovery in Sparse Signals , 2013, IET Signal Process..

[43]  Bradley P. Carlin,et al.  Markov Chain Monte Carlo in Practice: A Roundtable Discussion , 1998 .

[44]  Edward Lloyd Snelson,et al.  Flexible and efficient Gaussian process models for machine learning , 2007 .

[45]  Hong Sun,et al.  Compressive sensing for cluster structured sparse signals: variational Bayes approach , 2016, IET Signal Process..

[46]  Kevin P. Murphy,et al.  Machine learning - a probabilistic perspective , 2012, Adaptive computation and machine learning series.

[47]  Todd K. Moon,et al.  On the block-sparse solution of single measurement vectors , 2015, 2015 49th Asilomar Conference on Signals, Systems and Computers.

[48]  Philip Schniter,et al.  Dynamic Compressive Sensing of Time-Varying Signals Via Approximate Message Passing , 2012, IEEE Transactions on Signal Processing.

[49]  D. Donoho,et al.  Sparse MRI: The application of compressed sensing for rapid MR imaging , 2007, Magnetic resonance in medicine.

[50]  Junzhou Huang,et al.  Learning with structured sparsity , 2009, ICML '09.

[51]  Lawrence Carin,et al.  Tree-Structured Compressive Sensing With Variational Bayesian Analysis , 2010, IEEE Signal Processing Letters.

[52]  Yonina C. Eldar,et al.  Reduce and Boost: Recovering Arbitrary Sets of Jointly Sparse Vectors , 2008, IEEE Transactions on Signal Processing.

[53]  Yuri Ulybyshev,et al.  Satellite Constellation Design for Complex Coverage , 2008 .

[54]  Stefania Cornara,et al.  A STUDY OF THREE SATELLITE CONSTELLATION DESIGN ALGORITHMS , 2013 .

[55]  Olivier L. de Weck,et al.  Optimal Reconfiguration of Satellite Constellations with the Auction Algorithm , 2004 .

[56]  D. Donoho,et al.  Basis pursuit , 1994, Proceedings of 1994 28th Asilomar Conference on Signals, Systems and Computers.

[57]  Todd K. Moon,et al.  AMP-B-SBL: An algorithm for clustered sparse signals using approximate message passing , 2016, 2016 IEEE 7th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON).

[58]  Todd K. Moon,et al.  Detection of Amorphously Shaped Objects Using Spatial Information Detection Enhancement (SIDE) , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[59]  E.J. Candes,et al.  An Introduction To Compressive Sampling , 2008, IEEE Signal Processing Magazine.

[60]  Symeon Chatzinotas,et al.  Compressive Sparsity Order Estimation for Wideband Cognitive Radio Receiver , 2014, IEEE Transactions on Signal Processing.

[61]  Philip Schniter,et al.  Efficient High-Dimensional Inference in the Multiple Measurement Vector Problem , 2011, IEEE Transactions on Signal Processing.

[62]  L. Rebollo-Neira,et al.  Optimized orthogonal matching pursuit approach , 2002, IEEE Signal Processing Letters.

[63]  Todd K. Moon,et al.  Hierarchical Bayesian approach for jointly-sparse solution of multiple-measurement vectors , 2014, 2014 48th Asilomar Conference on Signals, Systems and Computers.

[64]  Yonina C. Eldar,et al.  Xampling: Signal Acquisition and Processing in Union of Subspaces , 2009, IEEE Transactions on Signal Processing.

[65]  S. Frick,et al.  Compressed Sensing , 2014, Computer Vision, A Reference Guide.

[66]  Satyajayant Misra,et al.  Applications of Compressed Sensing in Communications Networks , 2013, ArXiv.

[67]  Wynn C. Stirling,et al.  Epistemic Decision Theory Applied to Multiple-Target Tracking , 1994, IEEE Trans. Syst. Man Cybern. Syst..

[68]  Thomas Strohmer,et al.  High-Resolution Radar via Compressed Sensing , 2008, IEEE Transactions on Signal Processing.

[69]  Andreas Krause,et al.  Near-Optimal Sensor Placements in Gaussian Processes: Theory, Efficient Algorithms and Empirical Studies , 2008, J. Mach. Learn. Res..

[70]  Carl E. Rasmussen,et al.  Analysis of Some Methods for Reduced Rank Gaussian Process Regression , 2003, European Summer School on Multi-AgentControl.

[71]  Alexander J. Smola,et al.  Sparse Greedy Gaussian Process Regression , 2000, NIPS.

[72]  Guillermo Sapiro,et al.  Dictionary Learning for Noisy and Incomplete Hyperspectral Images , 2012, SIAM J. Imaging Sci..

[73]  Baoxin Li,et al.  A compressive sensing approach for expression-invariant face recognition , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[74]  Sungyoung Lee,et al.  Compressive sensing: From theory to applications, a survey , 2013, Journal of Communications and Networks.

[75]  Ming-Hsuan Yang,et al.  Online Sparse Gaussian Process Regression and Its Applications , 2011, IEEE Transactions on Image Processing.

[76]  Olivier L. de Weck,et al.  Staged Deployment of Communications Satellite Constellations in Low Earth Orbit , 2004, J. Aerosp. Comput. Inf. Commun..

[77]  Gitta Kutyniok,et al.  Theory and applications of compressed sensing , 2012, 1203.3815.

[78]  Jie Ding,et al.  Robustness of orthogonal matching pursuit for multiple measurement vectors in noisy scenario , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[79]  John Gray Peatman,et al.  Introduction to Applied Statistics , 1964, Univariate, Bivariate, and Multivariate Statistics Using R.

[80]  Alexandros G. Dimakis,et al.  Compressed Sensing using Generative Models , 2017, ICML.

[81]  Jongeun Choi,et al.  Adaptive Sampling for Learning Gaussian Processes Using Mobile Sensor Networks , 2011, Sensors.

[82]  Thomas S. Huang,et al.  Image Super-Resolution Via Sparse Representation , 2010, IEEE Transactions on Image Processing.

[83]  Darryl Morrell,et al.  Convex Bayes decision theory , 1991, IEEE Trans. Syst. Man Cybern..

[84]  Hong Zhang,et al.  Optimal sensor placement , 1992, Proceedings 1992 IEEE International Conference on Robotics and Automation.

[85]  Richard G. Baraniuk,et al.  Bayesian Compressive Sensing Via Belief Propagation , 2008, IEEE Transactions on Signal Processing.

[86]  L. Rider,et al.  Circular polar constellations providing continuous single or multiple coverage above a specified latitude , 1987 .

[87]  Bhaskar D. Rao,et al.  An Empirical Bayesian Strategy for Solving the Simultaneous Sparse Approximation Problem , 2007, IEEE Transactions on Signal Processing.

[88]  Emmanuel J. Candès,et al.  Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information , 2004, IEEE Transactions on Information Theory.

[89]  Ole Winther,et al.  Bayesian Inference for Structured Spike and Slab Priors , 2014, NIPS.

[90]  Michael Elad,et al.  Sparse and Redundant Representations - From Theory to Applications in Signal and Image Processing , 2010 .

[91]  J G Walker,et al.  Continuous Whole-Earth Coverage by Circular-Orbit Satellite Patterns , 1977 .

[92]  Farzad Kamalabadi,et al.  Optimal Sensor Array Configuration in Remote Image Formation , 2008, IEEE Transactions on Image Processing.

[93]  Paco López-Dekker,et al.  A Novel Strategy for Radar Imaging Based on Compressive Sensing , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[94]  Roman Garnett,et al.  Bayesian optimization for sensor set selection , 2010, IPSN '10.

[95]  Zongxu Pan,et al.  Super-Resolution Based on Compressive Sensing and Structural Self-Similarity for Remote Sensing Images , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[96]  K. V. S. Hari,et al.  Fusion of Greedy Pursuits for compressed sensing signal reconstruction , 2012, 2012 Proceedings of the 20th European Signal Processing Conference (EUSIPCO).

[97]  Jong Chul Ye,et al.  Dynamic sparse support tracking with multiple measurement vectors using compressive MUSIC , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[98]  Shutao Li,et al.  Remote Sensing Image Fusion via Sparse Representations Over Learned Dictionaries , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[99]  Lawrence Carin,et al.  Bayesian Compressive Sensing , 2008, IEEE Transactions on Signal Processing.

[100]  Yonina C. Eldar,et al.  Blind Multiband Signal Reconstruction: Compressed Sensing for Analog Signals , 2007, IEEE Transactions on Signal Processing.

[101]  Baoxin Li,et al.  Compressive Sensing Reconstruction of Correlated Images Using Joint Regularization , 2016, IEEE Signal Processing Letters.

[102]  Sheetal Kalyani,et al.  Tuning Free Orthogonal Matching Pursuit , 2017, ArXiv.

[103]  Todd K. Moon,et al.  Sparse Bayesian learning using variational Bayes inference based on a greedy criterion , 2017, 2017 51st Asilomar Conference on Signals, Systems, and Computers.

[104]  Igal Bilik,et al.  Spatial Compressive Sensing for Direction-of-Arrival Estimation of Multiple Sources using Dynamic Sensor Arrays , 2011, IEEE Transactions on Aerospace and Electronic Systems.

[105]  Lawrence Carin,et al.  Exploiting Structure in Wavelet-Based Bayesian Compressive Sensing , 2009, IEEE Transactions on Signal Processing.

[106]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[107]  Yonina C. Eldar,et al.  Spectrum Sharing Radar: Coexistence via Xampling , 2016, IEEE Transactions on Aerospace and Electronic Systems.

[108]  Sundeep Rangan,et al.  Orthogonal Matching Pursuit: A Brownian Motion Analysis , 2011, IEEE Transactions on Signal Processing.

[109]  Robert D. Nowak,et al.  Compressed Channel Sensing: A New Approach to Estimating Sparse Multipath Channels , 2010, Proceedings of the IEEE.

[110]  Chris Bailey-Kellogg,et al.  Gaussian Processes for Active Data Mining of Spatial Aggregates , 2005, SDM.

[111]  Zengchang Qin,et al.  An application of compressive sensing for image fusion , 2010, CIVR '10.

[112]  Marc Toussaint,et al.  Efficient sparsification for Gaussian process regression , 2016, Neurocomputing.

[113]  Matthew J. Beal Variational algorithms for approximate Bayesian inference , 2003 .

[114]  Hongbin Li,et al.  Pattern-Coupled Sparse Bayesian Learning for Recovery of Block-Sparse Signals , 2013, IEEE Transactions on Signal Processing.

[115]  D. Dunson,et al.  Efficient Gaussian process regression for large datasets. , 2011, Biometrika.

[116]  Wynn C. Stirling,et al.  Making value-laden decisions under conflict , 1994, Proceedings of IEEE International Conference on Systems, Man and Cybernetics.

[117]  Bhaskar D. Rao,et al.  Sparse solutions to linear inverse problems with multiple measurement vectors , 2005, IEEE Transactions on Signal Processing.

[118]  Lie Wang,et al.  Orthogonal Matching Pursuit for Sparse Signal Recovery With Noise , 2011, IEEE Transactions on Information Theory.

[119]  Chun-Liang Li,et al.  One Network to Solve Them All — Solving Linear Inverse Problems Using Deep Projection Models , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[120]  Stephen J. Roberts,et al.  A tutorial on variational Bayesian inference , 2012, Artificial Intelligence Review.

[121]  Aggelos K. Katsaggelos,et al.  Bayesian Compressive Sensing Using Laplace Priors , 2010, IEEE Transactions on Image Processing.

[122]  Philip Schniter,et al.  Expectation-Maximization Gaussian-Mixture Approximate Message Passing , 2012, IEEE Transactions on Signal Processing.

[123]  I. A. Antonov,et al.  An economic method of computing LPτ-sequences , 1979 .

[124]  Jun Fang,et al.  Two-Dimensional Pattern-Coupled Sparse Bayesian Learning via Generalized Approximate Message Passing , 2015, IEEE Transactions on Image Processing.

[125]  W. F. Caselton,et al.  Optimal monitoring network designs , 1984 .

[126]  Stéphane Mallat,et al.  Matching pursuits with time-frequency dictionaries , 1993, IEEE Trans. Signal Process..

[127]  Wynn C. Stirling,et al.  A theory of satisficing decisions and control , 1998, IEEE Trans. Syst. Man Cybern. Part A.

[128]  Todd K. Moon,et al.  Exploration vs. Data Refinement via Multiple Mobile Sensors , 2019, Entropy.

[129]  D.G. Tzikas,et al.  The variational approximation for Bayesian inference , 2008, IEEE Signal Processing Magazine.

[130]  Trac D. Tran,et al.  Iterative Convex Refinement for Sparse Recovery , 2015, IEEE Signal Processing Letters.

[131]  Tingting Wu,et al.  Spatial interpolation of temperature in the United States using residual kriging , 2013 .

[132]  Miles E. Lopes Estimating Unknown Sparsity in Compressed Sensing , 2013 .

[133]  Todd K. Moon,et al.  On the block-sparsity of multiple-measurement vectors , 2015, 2015 IEEE Signal Processing and Signal Processing Education Workshop (SP/SPE).

[134]  Joel A. Tropp,et al.  Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit , 2007, IEEE Transactions on Information Theory.

[135]  Bernard Ghanem,et al.  ISTA-Net: Iterative Shrinkage-Thresholding Algorithm Inspired Deep Network for Image Compressive Sensing , 2017, ArXiv.

[136]  Thomas J. Lang Symmetric circular orbit satellite constellations for continuous global coverage , 1988 .

[137]  Sundeep Rangan,et al.  Orthogonal Matching Pursuit From Noisy Random Measurements: A New Analysis , 2009, NIPS.

[138]  Bhaskar D. Rao,et al.  A GAMP-Based Low Complexity Sparse Bayesian Learning Algorithm , 2017, IEEE Transactions on Signal Processing.

[139]  Zhi Chen,et al.  Support knowledge-aided sparse Bayesian learning for compressed sensing , 2015, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[140]  Vishal Monga,et al.  Adaptive matching pursuit for sparse signal recovery , 2016, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[141]  G. Shafer The Enterprise of Knowledge: An Essay on Knowledge, Credal Probability, and Chance , 1982 .

[142]  Bhaskar D. Rao,et al.  Recovery of block sparse signals using the framework of block sparse Bayesian learning , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[143]  D. Rubin,et al.  Inference from Iterative Simulation Using Multiple Sequences , 1992 .

[144]  Lawrence Carin,et al.  Bayesian Robust Principal Component Analysis , 2011, IEEE Transactions on Image Processing.

[145]  S. Unnikrishna Pillai,et al.  An algorithm for near-optimal placement of sensor elements , 1990, IEEE Trans. Inf. Theory.

[146]  Thomas F. La Porta,et al.  Mobile Sensor Deployment in Unknown Fields , 2010, 2010 Proceedings IEEE INFOCOM.

[147]  Lei Han,et al.  Study on satellite orbit design for boundary environment monitoring , 2011, 2011 International Conference on Remote Sensing, Environment and Transportation Engineering.

[148]  Hong Sun,et al.  Bayesian compressive sensing for cluster structured sparse signals , 2012, Signal Process..

[149]  Ricardo Píriz,et al.  The Galileo Constellation Design: A Systematic Approach , 2005 .

[150]  Michael Elad,et al.  A Plurality of Sparse Representations Is Better Than the Sparsest One Alone , 2009, IEEE Transactions on Information Theory.

[151]  David B. Dunson,et al.  Multitask Compressive Sensing , 2009, IEEE Transactions on Signal Processing.

[152]  A. Storkey Truncated covariance matrices and Toeplitz methods in Gaussian processes , 1999 .

[153]  D. Beste Design of Satellite Constellations for Optimal Continuous Coverage , 1978, IEEE Transactions on Aerospace and Electronic Systems.