Vol. 53
Latest Volume
All Volumes
PIERB 108 [2024] PIERB 107 [2024] PIERB 106 [2024] PIERB 105 [2024] PIERB 104 [2024] PIERB 103 [2023] PIERB 102 [2023] PIERB 101 [2023] PIERB 100 [2023] PIERB 99 [2023] PIERB 98 [2023] PIERB 97 [2022] PIERB 96 [2022] PIERB 95 [2022] PIERB 94 [2021] PIERB 93 [2021] PIERB 92 [2021] PIERB 91 [2021] PIERB 90 [2021] PIERB 89 [2020] PIERB 88 [2020] PIERB 87 [2020] PIERB 86 [2020] PIERB 85 [2019] PIERB 84 [2019] PIERB 83 [2019] PIERB 82 [2018] PIERB 81 [2018] PIERB 80 [2018] PIERB 79 [2017] PIERB 78 [2017] PIERB 77 [2017] PIERB 76 [2017] PIERB 75 [2017] PIERB 74 [2017] PIERB 73 [2017] PIERB 72 [2017] PIERB 71 [2016] PIERB 70 [2016] PIERB 69 [2016] PIERB 68 [2016] PIERB 67 [2016] PIERB 66 [2016] PIERB 65 [2016] PIERB 64 [2015] PIERB 63 [2015] PIERB 62 [2015] PIERB 61 [2014] PIERB 60 [2014] PIERB 59 [2014] PIERB 58 [2014] PIERB 57 [2014] PIERB 56 [2013] PIERB 55 [2013] PIERB 54 [2013] PIERB 53 [2013] PIERB 52 [2013] PIERB 51 [2013] PIERB 50 [2013] PIERB 49 [2013] PIERB 48 [2013] PIERB 47 [2013] PIERB 46 [2013] PIERB 45 [2012] PIERB 44 [2012] PIERB 43 [2012] PIERB 42 [2012] PIERB 41 [2012] PIERB 40 [2012] PIERB 39 [2012] PIERB 38 [2012] PIERB 37 [2012] PIERB 36 [2012] PIERB 35 [2011] PIERB 34 [2011] PIERB 33 [2011] PIERB 32 [2011] PIERB 31 [2011] PIERB 30 [2011] PIERB 29 [2011] PIERB 28 [2011] PIERB 27 [2011] PIERB 26 [2010] PIERB 25 [2010] PIERB 24 [2010] PIERB 23 [2010] PIERB 22 [2010] PIERB 21 [2010] PIERB 20 [2010] PIERB 19 [2010] PIERB 18 [2009] PIERB 17 [2009] PIERB 16 [2009] PIERB 15 [2009] PIERB 14 [2009] PIERB 13 [2009] PIERB 12 [2009] PIERB 11 [2009] PIERB 10 [2008] PIERB 9 [2008] PIERB 8 [2008] PIERB 7 [2008] PIERB 6 [2008] PIERB 5 [2008] PIERB 4 [2008] PIERB 3 [2008] PIERB 2 [2008] PIERB 1 [2008]
2013-08-13
Target Detection from Microwave Imaging Based on Random Sparse Array and Compressed Sensing
By
Progress In Electromagnetics Research B, Vol. 53, 333-354, 2013
Abstract
This paper proposes an imaging scheme using a random sparse array (RSA) structure for radar target detection using compressed sensing (CS). The array collects sparse measurements with less collection time and data storage. Two schemes of the RSA are considered, random SAR mode and random array mode. Performances of both static and moving target detections are investigated. Performance of RSA with CS is compared with that using full SAR data with conventional back-projection (BP) method for static target detection and full uniform linear array (ULA) data with conventional beamforming (CBF) method for moving target detection. Simulation and real experimental tests are provided to verify the proposed target imaging scheme. Results show that RSA imaging with CS can perform better than normal SAR and ULA with conventional imaging methods. However, when environment is complicated and background too noisy, CS may have degraded performance.
Citation
Ling Huang, and Yi-Long Lu, "Target Detection from Microwave Imaging Based on Random Sparse Array and Compressed Sensing," Progress In Electromagnetics Research B, Vol. 53, 333-354, 2013.
doi:10.2528/PIERB13051701
References

1. Candes, E., J. Romberg, and T. Tao, "Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information," IEEE Trans. Inf. Theory, Vol. 52, 489-509, Feb. 2006.
doi:10.1109/TIT.2005.862083

2. Candes, E., J. Romberg, and T. Tao, "Stable signal recovery from incomplete and inaccurate measurements," Comm. Pure Appl. Math., Vol. 59, 1207-1223, Mar. 2006.
doi:10.1002/cpa.20124

3. Donoho, D., "Compressed sensing," IEEE Trans. Inf. Theory, Vol. 52, 1289-1306, Apr. 2006.
doi:10.1109/TIT.2006.871582

4. Baraniuk, R. and P. Steeghs, "Compressive radar imaging," Proc. 2007 IEEE Radar Conference, 128-133, 2007.

5. Patel, V. M., G. R. Easley, D. M. Healy, R. Chellappa, and Jr., "Compressed synthetic aperture radar," IEEE J. Sel. Topics Signal Process., Vol. 4, No. 2, 244-254, Apr. 2010.
doi:10.1109/JSTSP.2009.2039181

6. Wei, S. J., X. L. Zhang, J. Shi, and G. Xiang, "Sparse reconstruction for SAR imaging based on compressed sensing," Progress In Electromagnetics Research, Vol. 109, 63-81, 2010.
doi:10.2528/PIER10080805

7. Alonso, M. T., P. López-Dekker, and J. J. Mallorquí, "A novel strategy for radar imaging based on compressive sensing," IEEE Trans. Geosci. Remote Sens., Vol. 48, No. 12, 4285-4295, Dec. 2010.
doi:10.1109/TGRS.2010.2051231

8. Wei, S. J., X. L. Zhang, and J. Shi, "Linear array SAR imaging via compressed sensing," Progress In Electromagnetics Research, Vol. 117, 299-319, 2011.

9. Li, J., S. Zhang, and J. Chang, "Applications of compressed sensing for multiple transmitters multiple azimuth beams SAR imaging," Progress In Electromagnetics Research, Vol. 127, 259-275, 2012.
doi:10.2528/PIER12021307

10. Yang, J. G., J. Thompson, X. T. Huang, T. Jin, and Z. M. Zhou, "Random-frequency SAR imaging based on compressed sensing," IEEE Trans. Geosci. Remote Sens., 1-12, 2012.

11. Kurtz, J. L. and R. J. Tan, "Sparse array of RF sensors for sensing through the wall," Proc. SPIE, Vol. 6547, 5470B1-11, 2007.

12. Ao, Z. J., L. J. Kong, Y. Jia, and J. Y. Yang, "Multi-channel target cooperative detection in through-the-wall-radar imaging based on random sparse array," IEEE CIE International Conference on Radar, Vol. 1, 176-178, 2011.

13. Carin, L., "On the relationship between compressive sensing and random sensor arrays ," IEEE Antennas Propagat. Mag., Vol. 51, 72-81, 2009.
doi:10.1109/MAP.2009.5432044

14. Carin, L., L. Dehong, and G. Bin, "Coherence, compressive sensing, and random sensor arrays," IEEE Antennas Propagat. Mag., Vol. 53, 28-39, 2011.
doi:10.1109/MAP.2011.6097283

15. Soumekh, M., Synthetic Aperture Radar Signal Processing with MATLAB Algorithms, Wiley, 1999.

16. Marcelo, A., P. Pau, and S. Rolf, "Applications of time-domain back-projection SAR processing in the airborne case," Proc. 7th European Conference on Synthetic Aperture Radar, 1-4, Jun. 2008.

17. Donoho, D., M. Elad, and V. N. Temlyakov, "Stable recovery of sparse overcomplete representations in the presence of noise," IEEE Trans. Inf. Theory, Vol. 52, No. 1, 6-18, Jan. 2006.
doi:10.1109/TIT.2005.860430

18. Candes, E. and J. Romberg, "Sparsity and incoherence in compressed sensing," Inverse Problems, Vol. 23, 2006.

19. Goldstein, T. and S. Osher, "The split Bregman method for L1-regularized problems," SIAM Journal on Imaging Sciences, Vol. 2, 285-322, 2009.

20. Romberg, J., L1-magic, Aug. 2008, available: http://www.acm.caltech.edu/l1magic/.

21. Mallat, S., A Wavelet Tour of Signal Processing, Academic Press, 1999.

22. Needell, D. and R. Vershynin, "Uniform uncertainty principle and signal recovery by regularization orthogonal matching pursuit," Journal Foundations of Computational Mathematics, Vol. 9, Apr. 2009.

23. Needell, D. and R. Vershynin, "Greedy signal recovery and uncertainty principles," Proc. SPIE, 68140J, Bellingham, WA, 2008.

24. Donoho, D. L., I. Drori, Y. Tsaig, and J. L. Starck, "Sparse solution of underdetermined systems of linear equations by stagewise orthogonal matching pursuit," IEEE Trans. Inf. Theory, Vol. 58, 1094-1121, Feb. 2012.
doi:10.1109/TIT.2011.2173241

25. Dissanayake, G., S. Sukkarieh, E. Nebot, and H. Durrant-Whyte, "The aiding of a low-cost strapdown inertial measurement unit using vehicle model constraints for land vehicle applications," IEEE Trans. Robot. Automat., Vol. 17, 731-747, 2001.
doi:10.1109/70.964672

26. Cao, F. X., D. K. Yang, A. G. Xu, J. Ma, W. D. Xiao, C. L. Law, K. V. Ling, and H. C. Chua, "Low cost SINS/GPS integration for and vehicle navigation," Proc. ITSC 2002, 910-913, 2002.

27. Huang, L. and Y. L. Lu, "Radar imaging with compressed sensing for detecting moving targets behind walls," IET International Conference on Radar Systems, 2012.

28. Yoon, Y.-S. and M. G. Amin, "Compressed sensing technique for high-resolution radar imaging," Proc. SPIE, Vol. 6968, 696 81A.1-696 81A.10, May 2008.

29. Amin, M., F. Ahmad, and W. J. Zhang, "A compressive sensing approach to moving target indication for urban sensing," Proc. 2011 IEEE Radar Conference, 509-512, 2011.