Compressive Direction Finding Based on Amplitude Comparison
Abstract
Keywords
References
[1] Skolnik, M., “Introduction to Radar Systems,” New York: McGraw-Hill, 2001.
[2] S. S. Abeysekera, “An efficient Hilbert transform interpolation algorithm for peak position estimation,”Proceedings of the 11th IEEE Signal Processing Workshop on Statistical Signal Processing, 6-8 Aug. 2001, pp.417- 420
doi:10.1109/SSP.2001.955311
[3] Chu-Xiong Ding, Jing Bai, “Peak position estimation algorithms for cross-correlation function in elastography,”Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 29 Oct.-1 Nov. 1998, pp.866 – 868
[4] A. C. Gilbert, J. A. Tropp. “Applications of sparse approximations in communications,” in Proc. of IEEE Int. Symp. Inf. Theory, 2005.
[5] Candes, E.J.; Wakin, M.B. “An introduction to compressive sampling”, IEEE Signal Processing Magazine, Volume 25, Issue 2, pp. 21-30, March 2008
doi:10.1109/MSP.2007.914731
[6] D. Donoho. Compressed sensing. IEEE Trans. Inform. Theory, Vol. 52, No. 4: 1289-1306, Apr. 2006.
doi:10.1109/TIT.2006.871582
[7] E. Candès. Compressive sampling. in Proceedings of Int. Congress of Mathematics, Madrid, Spain, 2006.
[8] E.J.Candes. “The restricted isometry property and its implication for compressive sensing,” C.R.Math. Acad. Sci. Paris, Seris I, 346:589, 2008
[9] S. G. Mallat and Z. Zhang.: ‘Matching pursuits with time-frequency dictionaries’, IEEE Tran. on ASSP, Dec. 1993, vol. 41, no. 12, pp. 3397-3415
doi:10.1109/78.258082
[10] Tropp J,Gilbert A. “Signal recovery from random measurements via orthogonal matching Pursuit”, Transacting on Information Theory, 2007, 53(12):4655-4666.
doi:10.1109/TIT.2007.909108
[11] Emmanuel Candes, and Terence Tao, “The Dantzig selector: statistical estimation when p is much larger than n”, Annals of Statistics 2007. Volume 35, Number 6, pp. 2313-2351
doi:10.1214/009053606000001523
[12] Yipeng Liu, Qun Wan, Xiaoli Chu, “A Robust Beamformer Based on Weighted Sparse Constraint” CoRR abs/1005.4200, 2010, [online] accessiable: http://arxiv.org/abs/1005.4200
[13] M. Grant and S. Boyd, CVX: Matlab Software for Disciplined Convex Programming. Online accessiable : http://stanford.edu/~boyd/cvx
[14] Ying Zhang,Qun Wan, Anmin Huang,”Localization of narrow band source in the present of mutual coupling via sparse solution finding,” Progress in Electromagnetics Research-PIER, 2008, vol.86, pp.243-257
doi:10.2528/PIER08090703
[15] Yipeng Liu, Qun Wan, “Sparse Support Recovery with Phase-Only Measurements” CoRR abs/1005.1801, 2010, [online] accessiable: http://arxiv.org/abs/1005.1801
[16] M.I.Snolnik. Radar Handbook, Second Edition, New York: McGraw-Hill Publishing Company, 1980.
[17] Peyton Z, Peables Radars Principles, John wiley and Sons Inc, 1998.
[18] H.Griffiths, A Tutorial on Synetheic Aperture Radar. IEEE National Conferrance, 1997.
[19] Deanna Needell,Topics in Compressed Sensing,Submitted in partial satisfaction of the requirements for the degree of doctor of philosophy in mathematics in the office of graduate studies of the university of California DAVIS.
[20] D. Omidiran and M. J. Wainwright. High-dimensional subset recovery in noise: Sparsified measurements without loss of statistical efficiency. Technical report, Department of Statistics,UC Berkeley, April 2008. Short version presented at Int. Symp. Info. Theory, July 2008.
Full Text: PDF