Compressed sensing, IEEE Transactions on Information Theory, vol.52, issue.4, pp.1289-1306, 2006. ,
DOI : 10.1109/TIT.2006.871582
URL : https://hal.archives-ouvertes.fr/inria-00369486
Compressive Sensing [Lecture Notes], IEEE Signal Processing Magazine, vol.24, issue.4, pp.118-121, 2007. ,
DOI : 10.1109/MSP.2007.4286571
Decoding by Linear Programming, IEEE Transactions on Information Theory, vol.51, issue.12, pp.4203-4215, 2005. ,
DOI : 10.1109/TIT.2005.858979
URL : http://arxiv.org/abs/math/0502327
An Introduction To Compressive Sampling, IEEE Signal Processing Magazine, vol.25, issue.2, pp.21-30, 2008. ,
DOI : 10.1109/MSP.2007.914731
Sampling-50 years after Shannon, Proceedings of the IEEE, vol.88, issue.4, pp.569-587, 2000. ,
DOI : 10.1109/5.843002
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.140.9599
Direction estimation using compressive sampling array processing, 2009 IEEE/SP 15th Workshop on Statistical Signal Processing, pp.626-629, 2009. ,
DOI : 10.1109/SSP.2009.5278497
A sparse signal reconstruction perspective for source localization with sensor arrays, IEEE Transactions on Signal Processing, vol.53, issue.8, pp.3010-3022, 2005. ,
DOI : 10.1109/TSP.2005.850882
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.208.4199
Compressive wireless sensing, Proceedings of the 5th international conference on Information processing in sensor networks, pp.134-142, 2006. ,
A User's Guide to Compressed Sensing for Communications Systems, IEICE Transactions on Communications, vol.96, issue.3, pp.685-712, 2013. ,
DOI : 10.1587/transcom.E96.B.685
Compressive video sampling, Signal Processing Conference 16th European, pp.1-5, 2008. ,
MIMO Radar Using Compressive Sampling, IEEE Journal of Selected Topics in Signal Processing, vol.4, issue.1, pp.146-163, 2010. ,
DOI : 10.1109/JSTSP.2009.2038973
URL : http://arxiv.org/abs/0911.4752
High-Resolution Radar via Compressed Sensing, IEEE Transactions on Signal Processing, vol.57, issue.6, pp.2275-2284, 2009. ,
DOI : 10.1109/TSP.2009.2014277
URL : http://arxiv.org/pdf/0803.2257
Spatial Compressive Sensing for MIMO Radar, IEEE Transactions on Signal Processing, vol.62, issue.2, pp.419-430, 2014. ,
DOI : 10.1109/TSP.2013.2289875
URL : http://arxiv.org/abs/1304.4578
How well can we estimate a sparse vector?, Applied and Computational Harmonic Analysis, vol.34, issue.2, pp.317-323, 2013. ,
DOI : 10.1016/j.acha.2012.08.010
Bayesian bounds for parameter estimation and nonlinear filtering/tracking, AMC, vol.10, p.12, 2007. ,
DOI : 10.1109/9780470544198
Spectral analysis of signals, NJ, 2005. ,
On the Achievability of Cram??r???Rao Bound in Noisy Compressed Sensing, IEEE Transactions on Signal Processing, vol.60, issue.1, pp.518-526, 2012. ,
DOI : 10.1109/TSP.2011.2171953
Asymptotic Achievability of the CramÉr–Rao Bound for Noisy Compressive Sampling, IEEE Transactions on Signal Processing, vol.57, issue.3, pp.1233-1236, 2009. ,
DOI : 10.1109/TSP.2008.2010379
Corrections to ???Asymptotic Achievability of the Cram??r???Rao Bound for Noisy Compressive Sampling???, IEEE Transactions on Signal Processing, vol.65, issue.18, 2017. ,
DOI : 10.1109/TSP.2017.2723352
The Cramér-Rao Bound for Estimating a Sparse Parameter Vector, IEEE Transactions on Signal Processing, vol.58, issue.6, pp.3384-3389, 2010. ,
DOI : 10.1109/TSP.2010.2045423
Linear Regression With Gaussian Model Uncertainty: Algorithms and Bounds, IEEE Transactions on Signal Processing, vol.56, issue.6, pp.2194-2205, 2008. ,
DOI : 10.1109/TSP.2007.914323
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.331.6072
Estimation Performance for the Bayesian Hierarchical Linear Model, IEEE Signal Processing Letters, vol.23, issue.4, pp.488-492, 2016. ,
DOI : 10.1109/LSP.2016.2528579
URL : https://hal.archives-ouvertes.fr/hal-01264666
Cramér-Rao-Type Bounds for Sparse Bayesian Learning, IEEE Transactions on Signal Processing, vol.61, issue.3, pp.622-632, 2013. ,
DOI : 10.1109/TSP.2012.2226165
Large-System Estimation Performance in Noisy Compressed Sensing With Random Support of Known Cardinality???A Bayesian Analysis, IEEE Transactions on Signal Processing, vol.64, issue.21, pp.5525-5535, 2016. ,
DOI : 10.1109/TSP.2016.2591511
Combined modeling of sparse and dense noise improves Bayesian RVM, 22nd European Signal Processing Conference (EUSIPCO), pp.1841-1845, 2014. ,
Exact signal recovery from sparsely corrupted measurements through the Pursuit of Justice, 2009 Conference Record of the Forty-Third Asilomar Conference on Signals, Systems and Computers, pp.1556-1560, 2009. ,
DOI : 10.1109/ACSSC.2009.5470141
Democracy in action: Quantization, saturation, and compressive sensing, Applied and Computational Harmonic Analysis, vol.31, issue.3, pp.429-443, 2011. ,
DOI : 10.1016/j.acha.2011.02.002
URL : http://doi.org/10.1016/j.acha.2011.02.002
Bayesian lower bounds for dense or sparse (Outlier) noise in the RMT framework, 2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM), 2016. ,
DOI : 10.1109/SAM.2016.7569694
URL : https://hal.archives-ouvertes.fr/hal-01315978
Robust Bayesian compressed sensing with outliers, Signal Processing, vol.140, 2017. ,
DOI : 10.1016/j.sigpro.2017.05.017
Stable signal recovery from incomplete and inaccurate measurements, Communications on Pure and Applied Mathematics, vol.7, issue.8, pp.1207-1223, 2006. ,
DOI : 10.1017/CBO9780511804441
Signal Recovery From Incomplete and Inaccurate Measurements Via Regularized Orthogonal Matching Pursuit, IEEE Journal of Selected Topics in Signal Processing, vol.4, issue.2, pp.310-316, 2010. ,
DOI : 10.1109/JSTSP.2010.2042412
URL : http://arxiv.org/abs/0712.1360
Recovery of Sparsely Corrupted Signals, IEEE Transactions on Information Theory, vol.58, issue.5, pp.3115-3130, 2012. ,
DOI : 10.1109/TIT.2011.2179701
URL : http://arxiv.org/abs/1102.1621
Oracle performance estimation of Bernoulli-distributed sparse vectors Available: https, 2016 IEEE International Workshop on Statistical Signal Processing, 2016. ,
DOI : 10.1109/ssp.2016.7551780
Mathematical methods and algorithms for signal processing, 2000. ,
A mathematical introduction to compressive sensing, 2013. ,
DOI : 10.1007/978-0-8176-4948-7
The restricted isometry property and its implications for compressed sensing, Comptes Rendus Mathematique, vol.346, issue.9-10, pp.589-592, 2008. ,
DOI : 10.1016/j.crma.2008.03.014
Signal Processing With Compressive Measurements, IEEE Journal of Selected Topics in Signal Processing, vol.4, issue.2, pp.445-460, 2010. ,
DOI : 10.1109/JSTSP.2009.2039178
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.148.1507
A Simple Proof of the Restricted Isometry Property for Random Matrices, Constructive Approximation, vol.159, issue.2, pp.253-263, 2008. ,
DOI : 10.1007/978-3-642-60932-9
A simple proof that random matrices are democratic, 2009. ,
Parameter estimation problems with singular information matrices, IEEE Transactions on Signal Processing, vol.49, issue.1, pp.87-90, 2001. ,
DOI : 10.1109/78.890346
On the Constrained CramÉr–Rao Bound With a Singular Fisher Information Matrix, IEEE Signal Processing Letters, vol.16, issue.6, pp.453-456, 2009. ,
DOI : 10.1109/LSP.2009.2016831
Lower bounds for parametric estimation with constraints, IEEE Transactions on Information Theory, vol.36, issue.6, pp.1285-1301, 1990. ,
DOI : 10.1109/18.59929
On the Cramer-Rao bound under parametric constraints, IEEE Signal Processing Letters, vol.5, issue.7, pp.177-179, 1998. ,
DOI : 10.1109/97.700921
A Lower Bound on the Bayesian MSE Based on the Optimal Bias Function, IEEE Transactions on Information Theory, vol.55, issue.11, pp.5179-5196, 2009. ,
DOI : 10.1109/TIT.2009.2030451
Tensor CP Decomposition With Structured Factor Matrices: Algorithms and Performance, IEEE Journal of Selected Topics in Signal Processing, vol.10, issue.4, pp.757-769, 2016. ,
DOI : 10.1109/JSTSP.2015.2509907
URL : https://hal.archives-ouvertes.fr/hal-01246855
Random matrix methods for wireless communications, 2011. ,
DOI : 10.1017/CBO9780511994746
URL : https://hal.archives-ouvertes.fr/hal-00658725
Prior distributions for variance parameters in hierarchical models (comment on
article by Browne and Draper), Bayesian Analysis, vol.1, issue.3, pp.515-534, 2006. ,
DOI : 10.1214/06-BA117A
Mimo radar signal processing ,
DOI : 10.1002/9780470391488
Oblique Projections for Direction-of-Arrival Estimation With Prior Knowledge, IEEE Transactions on Signal Processing, vol.56, issue.4, pp.1374-1387, 2008. ,
DOI : 10.1109/TSP.2007.909348
URL : https://hal.archives-ouvertes.fr/hal-00575873
An optimal prior-knowledge-based DOA estimation method, 17th European Signal Processing Conf, pp.869-873, 2009. ,
URL : https://hal.archives-ouvertes.fr/hal-00456900
Signal Processing Tools for Radio Astronomy, Handbook of Signal Processing Systems, pp.421-463, 2013. ,
DOI : 10.1007/978-1-4614-6859-2_14
Relaxed concentrated MLE for robust calibration of radio interferometers Available: https, EUSIPCO, 2016. ,
CoSaMP, Communications of the ACM, vol.53, issue.12, pp.301-321, 2009. ,
DOI : 10.1145/1859204.1859229
Regression shrinkage and selection via the lasso, Journal of the Royal Statistical Society. Series B, pp.267-288, 1996. ,
DOI : 10.1111/j.1467-9868.2011.00771.x
Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit, IEEE Transactions on Information Theory, vol.53, issue.12, pp.4655-4666, 2007. ,
DOI : 10.1109/TIT.2007.909108
URL : http://authors.library.caltech.edu/9490/1/TROieeetit07.pdf
Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition, Proceedings of 27th Asilomar Conference on Signals, Systems and Computers, pp.40-44, 1993. ,
DOI : 10.1109/ACSSC.1993.342465
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.348.5735
Atomic Decomposition by Basis Pursuit, SIAM Journal on Scientific Computing, vol.20, issue.1, pp.33-61, 1998. ,
DOI : 10.1137/S1064827596304010
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.113.7694
Iterative hard thresholding for compressed sensing, Applied and Computational Harmonic Analysis, vol.27, issue.3, pp.265-274, 2009. ,
DOI : 10.1016/j.acha.2009.04.002
URL : http://doi.org/10.1016/j.acha.2009.04.002