Principles of Modern Radar - Volume 2 1891121537

200 CHAPTER 5 Radar Applications of Sparse Reconstruction[56] Herman, M.A. and Strohmer, T., “High-Resolution Radar via Compressed Sensing,” IEEETransactions on Signal Processing, vol. 57, pp. 2275–2284, June 2009.[57] Levanon, N. and Mozeson, E., Radar Signals, John Wiley & Sons, Interscience Division,New York, 2004.[58] Cociorva, P., “Preconditioners for Sparse Linear Inversion,” Master’s thesis, The Ohio StateUniversity, 2006.[59] Duarte, M. and Baraniuk, R., “Spectral Compressive Sensing,” Preprint, 2010.[60] Becker, S., “List of Sparse and Low-Rank Recovery Algorithms.” Available athttp://www.ugcs.caltech.edu/∼srbecker/algorithms.shtml.[61] Becker, S., Bobin, J., and Candés, E., “NESTA: A Fast and Accurate First-Order Methodfor Sparse Recovery,” SIAM Journal on Imaging Sciences, vol. 4, p. 1, 2011.[62] Burns, J.W., Subotic, N.S., and Pandelis, D., “Adaptive Decomposition in Electromagnetics,”in Antennas and Propagation Society International Symposium, pp. 1984– 1987, July 1997.[63] van den Berg, E. and Friedlander, M., “Probing the Pareto Frontier for Basis PursuitSolutions,” SIAM Journal on Scientific Computing, vol. 31, no. 2, pp. 890–912, 2008.[64] Tibshirani, R., “Regression Shrinkage and Selection via the LASSO,” Journal of the RoyalStatistical Society, Series B (Methodological), pp. 267–288, 1996.[65] Candès, E. and Tao, T., “The Dantzig Selector: Statistical Estimation When p Is MuchLarger than n,” Annals of Statistics, vol. 35, no. 6, pp. 2313–2351, 2007.[66] Donoho, D., Maleki, A., and Montanari, A., “Message-Passing Algorithms for CompressedSensing,” Proceedings of the National Academy of Sciences, vol. 106, no. 45,pp. 18914–18919, 2009.[67] Osborne, M., Presnell, B., and Turlach, B., “On the LASSO and Its Dual,” Journal ofComputational and Graphical Statistics, vol. 9, no. 2, pp. 319–337, 2000.[68] Lawson, C.L. and Hanson, R.J., Solving Least Squares Problems, Prentice-Hall, EnglewoodCliffs, NJ, 1974.[69] Hansen, P.C., “Analysis of Discrete Ill-Posed Problems by Means of the L-Curve,” SIAMReview, vol. 34, no. 4, pp. 561–580, 1992.[70] Chen, S., Donoho, D., and Saunders, M., “Atomic Decomposition by Basis Pursuit,” SIAMReview, vol. 43, no. 1, pp. 129–159, 2001.[71] Delaney, A.H. and Bresler, Y., “Globally Convergent Edge-Preserving Regularized Reconstruction:An Application to Limited-Angle Tomography,” IEEE Transactions on ImageProcessing, vol. 7, no. 2, pp. 204–221, 1998.[72] Lustig, M., Donoho, D., and Pauly, J., “Sparse MRI: The Application of Compressed Sensingfor Rapid MR Imaging,” Magnetic Resonance in Medicine, vol. 58, no. 6, p. 1182, 2007.[73] Smith, L.M., Keegan, M.S., Wittman, T., Mohler, G.O., and Bertozzi, A.L., “ImprovingDensity Estimation by Incorporating Spatial Information,” EURASIP Journal on Advancesin Signal Processing, 2010.[74] van den Berg, E. and Friedlander, M., “Sparse Optimization with Least-Squares Constraints,”Technical Report TR-2010-02, Department of Computer Science, University of BritishColumbia, 2010.[75] Cetin, M. and Karl, W.C., “Feature-Enhanced Synthetic Aperture Radar Image FormationBased on Nonquadratic Regularization,” IEEE Transactions on Image Processing, vol. 10,pp. 623–631, April 2001.[76] Rudin, L., Osher, S., and Fatemi, E., “Nonlinear Total Variation Based Noise RemovalAlgorithms,” Physica D: Nonlinear Phenomena, vol. 60, nos. 1–4, pp. 259–268, 1992.