Search search
submit Submit
Publication Date
to
Vol. 142
Latest Volume
All Volumes
PIER 179 [2024] PIER 178 [2023] PIER 177 [2023] PIER 176 [2023] PIER 175 [2022] PIER 174 [2022] PIER 173 [2022] PIER 172 [2021] PIER 171 [2021] PIER 170 [2021] PIER 169 [2020] PIER 168 [2020] PIER 167 [2020] PIER 166 [2019] PIER 165 [2019] PIER 164 [2019] PIER 163 [2018] PIER 162 [2018] PIER 161 [2018] PIER 160 [2017] PIER 159 [2017] PIER 158 [2017] PIER 157 [2016] PIER 156 [2016] PIER 155 [2016] PIER 154 [2015] PIER 153 [2015] PIER 152 [2015] PIER 151 [2015] PIER 150 [2015] PIER 149 [2014] PIER 148 [2014] PIER 147 [2014] PIER 146 [2014] PIER 145 [2014] PIER 144 [2014] PIER 143 [2013] PIER 142 [2013] PIER 141 [2013] PIER 140 [2013] PIER 139 [2013] PIER 138 [2013] PIER 137 [2013] PIER 136 [2013] PIER 135 [2013] PIER 134 [2013] PIER 133 [2013] PIER 132 [2012] PIER 131 [2012] PIER 130 [2012] PIER 129 [2012] PIER 128 [2012] PIER 127 [2012] PIER 126 [2012] PIER 125 [2012] PIER 124 [2012] PIER 123 [2012] PIER 122 [2012] PIER 121 [2011] PIER 120 [2011] PIER 119 [2011] PIER 118 [2011] PIER 117 [2011] PIER 116 [2011] PIER 115 [2011] PIER 114 [2011] PIER 113 [2011] PIER 112 [2011] PIER 111 [2011] PIER 110 [2010] PIER 109 [2010] PIER 108 [2010] PIER 107 [2010] PIER 106 [2010] PIER 105 [2010] PIER 104 [2010] PIER 103 [2010] PIER 102 [2010] PIER 101 [2010] PIER 100 [2010] PIER 99 [2009] PIER 98 [2009] PIER 97 [2009] PIER 96 [2009] PIER 95 [2009] PIER 94 [2009] PIER 93 [2009] PIER 92 [2009] PIER 91 [2009] PIER 90 [2009] PIER 89 [2009] PIER 88 [2008] PIER 87 [2008] PIER 86 [2008] PIER 85 [2008] PIER 84 [2008] PIER 83 [2008] PIER 82 [2008] PIER 81 [2008] PIER 80 [2008] PIER 79 [2008] PIER 78 [2008] PIER 77 [2007] PIER 76 [2007] PIER 75 [2007] PIER 74 [2007] PIER 73 [2007] PIER 72 [2007] PIER 71 [2007] PIER 70 [2007] PIER 69 [2007] PIER 68 [2007] PIER 67 [2007] PIER 66 [2006] PIER 65 [2006] PIER 64 [2006] PIER 63 [2006] PIER 62 [2006] PIER 61 [2006] PIER 60 [2006] PIER 59 [2006] PIER 58 [2006] PIER 57 [2006] PIER 56 [2006] PIER 55 [2005] PIER 54 [2005] PIER 53 [2005] PIER 52 [2005] PIER 51 [2005] PIER 50 [2005] PIER 49 [2004] PIER 48 [2004] PIER 47 [2004] PIER 46 [2004] PIER 45 [2004] PIER 44 [2004] PIER 43 [2003] PIER 42 [2003] PIER 41 [2003] PIER 40 [2003] PIER 39 [2003] PIER 38 [2002] PIER 37 [2002] PIER 36 [2002] PIER 35 [2002] PIER 34 [2001] PIER 33 [2001] PIER 32 [2001] PIER 31 [2001] PIER 30 [2001] PIER 29 [2000] PIER 28 [2000] PIER 27 [2000] PIER 26 [2000] PIER 25 [2000] PIER 24 [1999] PIER 23 [1999] PIER 22 [1999] PIER 21 [1999] PIER 20 [1998] PIER 19 [1998] PIER 18 [1998] PIER 17 [1997] PIER 16 [1997] PIER 15 [1997] PIER 14 [1996] PIER 13 [1996] PIER 12 [1996] PIER 11 [1995] PIER 10 [1995] PIER 09 [1994] PIER 08 [1994] PIER 07 [1993] PIER 06 [1992] PIER 05 [1991] PIER 04 [1991] PIER 03 [1990] PIER 02 [1990] PIER 01 [1989]
All Journals
PIER PIER B PIER C PIER M PIER Letters
2013-09-22
Lasso Based Performance Evaluation for Sparse One-Dimensional Radar Problem Under Random Sub-Sampling and Gaussian Noise
By
Yin Xiang,

    Dr. Yin Xiang

    Institute of Electronics, Chinese Academy of Sciences

    China

    Homepage

Bingchen Zhang,

    Dr. Bingchen Zhang

    Institute of Electronics

    Chinese Academy of Sciences

    China

    Homepage

Wen Hong

    Dr. Wen Hong

    Institute of Electronics

    Chinese Academy of Sciences

    China

    Homepage

Progress In Electromagnetics Research, Vol. 142, 559-578, 2013
doi:10.2528/PIER13031402
Abstract
Sparse microwave imaging is the combination of microwave imaging and sparse signal processing, which aims to extract physical and geometry information of sparse or transformed sparse scene from least number of radar measurements. As a primary investigation on its performance, this paper focuses on the performance guarantee for a one-dimensional radar, which detects delays of several point targets located at a sparse scene via randomly sub-sampling of radar returns. Based on the Lasso framework, the quantity relationship among three important factors is discussed, including the sub-sampling ratio ρM, sparse ratio ρK and signal-to-noise ratio (SNR), where ρM is the ratio of number of random sub-sampling to that of Nyquist's sampling, and ρK is the ratio of sparsity to the number of unknowns. In particular, to ensure correct delay detection and accurate back scattering coefficient reconstruction for each target, one needs ρM to be greater than C(ρK)ρKlogN and the input SNR be of order logN, where N is the number of range cells in scene.
Citation
Yin Xiang, Bingchen Zhang, and Wen Hong, "Lasso Based Performance Evaluation for Sparse One-Dimensional Radar Problem Under Random Sub-Sampling and Gaussian Noise," Progress In Electromagnetics Research, Vol. 142, 559-578, 2013.
doi:10.2528/PIER13031402
References

1. The Institute of Electronics, Chinese Academy of Sciences, "Statement tasks and project plan of 973 program: Studies on theory, system, and methodology of sparse microwave imaging," , 2009.
doi:10.1109/TIT.2006.871582

2. Donoho, D. L., "Compressive sensing," IEEE Transactions on Information Theory, Vol. 52, No. 4, 1289-1306, 2006.
doi:10.1109/MSP.2007.914731

3. Candes, E. J. and M. B. Wakin, "An introduction to compressive sampling," IEEE Signal Processing Magazine, Vol. 25, No. 2, 21-30, 2008.

4. Wu, Y., B. Zhang, and W. Hong, "Sparse microwave imaging: Principles and applications," China Science (F), Vol. 55, 1722-1754, 2012.

5. Baraniuk, R. and P. Steeghs, "Compressive radar imaging," IEEE Radar Conference, 128-133, Waltham, Massachusetts, 2007.
doi:10.2528/PIER10080805

6. Wei, S. J., X.-L. Zhang, J. Shi, and G. Xiang, "Sparse reconstruction for SAR imaging based on compressive sensing," Progress In Electromagnetics Research, Vol. 109, 63-81, 2010.
doi:10.2528/PIERB11121212

7. Ren, X. Z., Y. F. Li, and R. Yang, "Four-dimensional SAR imaging scheme based on compressive sensing," Progress In Electromagnetics Research B, Vol. 39, 225-239, 2012.

8. Luo, Y., Q. Zhang, Y.-Q. Bai, and Y.-L. Duan, "High-resolution ISAR imaging with sparse-spectrum OFDM-LFM waveform," PIERS Proceedings, 230-234, Kuala Lumpur, Malaysia, Mar. 27-30, 2012.

9. Liu, J., X. Li, S. Xu, and Z. Zhuang, "ISAR imaging of non-uniform rotation targets with limited pulse via compressed sensing," Progress In Electromagnetics Research B, Vol. 41, 285-305, 2012.
doi:10.1016/j.sigpro.2009.03.030

10. Gurbuz, A., J. McClellan, and W. Scott, Jr., "Compressive sensing for subsurface imaging using ground penetrating radar," Signal Processing, Vol. 89, No. 10, 1959-1972, 2009.

11. Stojanovic, I., M. Cetin, and W. Karl, "Joint space aspect reconstruction of wide-angle sar exploiting sparsity," SPIE, Vol. 6970, 2008.
doi:10.1109/ACSSC.2008.5074356

12. Chen "Compressive sensing in MIMO radar," 2008 42nd Asilomar Conference on Signals, Systems and Computers, 41-44, IEEE, 2008.
doi:10.1016/j.sigpro.2009.11.009

13. Ender, J., "On compressive sensing applied to radar," Signal Processing, Vol. 90, No. 5, 1402-1414, 2010.
doi:10.1109/JSTSP.2009.2039181

14. Patel, V., G. Easley, D. Healy, and R. Chellappa, "Compressed synthetic aperture radar," IEEE Journal of Selected Topics, in Signal Processing, Vol. 4, No. 2, 244-254, 2010.
doi:10.1109/TSP.2010.2052460

15. Ben-Haim, Z., Y. Eldar, and M. Elad, "Coherence-based performance guarantees for estimating a sparse vector under random noise," IEEE Trans. on Signal Processing, Vol. 58, No. 10, 5030-5043, 2010.
doi:10.1002/cpa.20124

16. Candes, E., J. Romberg, and T. Tao, "Stable signal recovery from incomplete and inaccurate measurements," Communications on Pure and Applied Mathematics, Vol. 59, No. 8, 1207-1223, 2006.
doi:10.1109/TIT.2005.858979

17. Candes, E. and T. Tao, "Decoding by linear programming," IEEE Trans. on Information Theory, Vol. 51, No. 12, 4203-4215, 2005.
doi:10.1109/TIT.2004.834793

18. Tropp, J., "Greed is good: Algorithmic results for sparse approximation," IEEE Trans. on Information Theory, Vol. 50, No. 10, 2231-2242, 2004.

19. Candes, E. J. and C. Fernandez-Granda, "Towards a mathematical theory of super-resolution," Communications on Pure and Applied Mathematics, 2013.

20. Eaves, J. L. and E. K. Reedy, Principles of Modern Radar, Van Nostrand Reinhold Company, New York, 1987.
doi:10.1109/TIT.2005.858979

21. Candes, E. J. and T. Tao, "Decoding by linear programming," IEEE Trans. on Information Theory, Vol. 51, No. 13, 4203-4215, 2005.

22. Golub, G. H. and C. F. Van Loan, Matrix Computations, Johns Hopkins University Press, Baltimore, 1996.

23. Mackey, L., M. I. Jordan, R. Y. Chen, B. Farrell, and J. A. Tropp, "Matrix concentration inequalities via the method of exchangeable Pairs," , 29 pages, 2012.

25. Bioucas-Dias, J. and M. Figueiredo, "Two-step algorithms for linear inverse problems with non-quadratic regularization," IEEE International Conference on Image Processing, ICIP 2007, Vol. 1, I-105-I-108, IEEE, 2007.

Download Full Article (200) View Full Article volume
The EM Academy piers.org jpier.org Who's Who in EM
Copyright © 2022 The Electromagnetics Academy. All Rights Reserved.