Principles of Modern Radar - Volume 2 1891121537

202 CHAPTER 5 Radar Applications of Sparse Reconstruction[96] Elad, M. and Yavneh, I., “A Plurality of Sparse Representations Is Better than the SparsestOne Alone,” IEEE Transactions on Information Theory, vol. 55, no. 10, pp. 4701–4714, 2009.[97] Schniter, P., Potter, L., and Ziniel, J., “Fast Bayesian Matching Pursuit,” in Proceedings ofthe Workshop on Information Theory and Applications, La Jolla, CA, January 2008.[98] Baron, D., Sarvotham, S., and Baraniuk, R., “Bayesian Compressive Sensing via BeliefPropagation,” IEEE Transactions on Signal Processing, vol. 58, pp. 269–280, January 2010.[99] Rangan, S., “Estimation with Random Linear Mixing, Belief Propagation and CompressedSensing,” Arxiv preprint arXiv:1001.2228, 2010.[100] Frey, B., Graphical Models for Machine Learning and Digital Communication, MIT Press,Cambridge, MA, 1998.[101] Montanari, A., “Graphical Models Concepts in Compressed Sensing,” Submitted, 2010.[102] Donoho, D. and Tanner, J., “Observed Universality of Phase Transitions in High-DimensionalGeometry, with Implications for Modern Data Analysis and Signal Processing,” PhilosophicalTransactions of the Royal Society A: Mathematical, Physical and Engineering Sciences,vol. 367, no. 1906, p. 4273, 2009.[103] Donoho, D., Maleki, A., and Montanari, A., “The Noise-Sensitivity Phase Transition inCompressed Sensing,” Arxiv preprint arXiv:1004.1218, 2010.[104] Donoho, D., Maleki, A., and Montanari, A., “Message Passing Algorithms for CompressedSensing: I—Motivation and Construction,” in Proceedings of Information Theory Workshop,2010.[105] Donoho, D., Maleki, A., and Montanari, A., “Message Passing Algorithms for CompressedSensing: II—Analysis and Validation,” in Proceedings of Information Theory Workshop,2010.[106] Maleki, A. and Montanari, A., “Analysis of Approximate Message Passing Algorithm,” inProceedings of the 44th Annual Conference on Information Sciences and Systems, pp. 1–7,2010.[107] Bayati, M. and Montanari, A., “The Dynamics of Message Passing on Dense Graphs, withApplications to Compressed Sensing,” Arxiv preprint arXiv:1001.3448, 2010.[108] Rangan, S., “Generalized Approximate Message Passing for Estimation with RandomLinear Mixing,” Arxiv preprint arXiv:1010.5141, 2010.[109] Proakis, J. and Salehi, M., Digital Communications, McGraw-Hill, New York, 1995.[110] Amin Khajehnejad, M., Xu, W., Salman Avestimehr, A., and Hassibi, B., “Weighted l 1minimization for sparse recovery with prior information,” in Proceedings of the IEEEInternational Symposium on Information Theory, pp. 483–487, 2009.[111] Cevher, V., Duarte, M., Hegde, C., and Baraniuk, R., “Sparse Signal Recovery UsingMarkov Random Fields,” in Proceedings of the Workshop on Neural Information ProcessingSystems, 2008.[112] Baraniuk, R.G., Cevher, V., Duarte, M.F., and Hegde, C., “Model-Based CompressiveSensing,” IEEE Transactions on Information Theory, vol. 56, no. 4, pp. 1982–2001, 2010.[113] Cevher, V., Indyk, P., Carin, L., and Baraniuk, R.G., “Sparse Signal Recovery and Acquisitionwith Graphical Models,” IEEE Signal Processing Magazine, vol. 27, no. 6, pp. 92–103, 2010.[114] Eldar, Y., Kuppinger, P., and Bölcskei, H., “Compressed Sensing of Block-Sparse Signals:Uncertainty Relations and Efficient Recovery,” Preprint arXiv: 0906.3173, 2009.[115] Ramakrishnan, N., Ertin, E., and Moses, R.L., “Joint Enhancement of Multichannel SARData,” in Algorithms for Synthetic Aperture Radar Imagery XIV, vol. 6568, SPIE, April 2007.