Vol. 96
Latest Volume
All Volumes
PIERC 127 [2022] PIERC 126 [2022] PIERC 125 [2022] PIERC 124 [2022] PIERC 123 [2022] PIERC 122 [2022] PIERC 121 [2022] PIERC 120 [2022] PIERC 119 [2022] PIERC 118 [2022] PIERC 117 [2021] PIERC 116 [2021] PIERC 115 [2021] PIERC 114 [2021] PIERC 113 [2021] PIERC 112 [2021] PIERC 111 [2021] PIERC 110 [2021] PIERC 109 [2021] PIERC 108 [2021] PIERC 107 [2021] PIERC 106 [2020] PIERC 105 [2020] PIERC 104 [2020] PIERC 103 [2020] PIERC 102 [2020] PIERC 101 [2020] PIERC 100 [2020] PIERC 99 [2020] PIERC 98 [2020] PIERC 97 [2019] PIERC 96 [2019] PIERC 95 [2019] PIERC 94 [2019] PIERC 93 [2019] PIERC 92 [2019] PIERC 91 [2019] PIERC 90 [2019] PIERC 89 [2019] PIERC 88 [2018] PIERC 87 [2018] PIERC 86 [2018] PIERC 85 [2018] PIERC 84 [2018] PIERC 83 [2018] PIERC 82 [2018] PIERC 81 [2018] PIERC 80 [2018] PIERC 79 [2017] PIERC 78 [2017] PIERC 77 [2017] PIERC 76 [2017] PIERC 75 [2017] PIERC 74 [2017] PIERC 73 [2017] PIERC 72 [2017] PIERC 71 [2017] PIERC 70 [2016] PIERC 69 [2016] PIERC 68 [2016] PIERC 67 [2016] PIERC 66 [2016] PIERC 65 [2016] PIERC 64 [2016] PIERC 63 [2016] PIERC 62 [2016] PIERC 61 [2016] PIERC 60 [2015] PIERC 59 [2015] PIERC 58 [2015] PIERC 57 [2015] PIERC 56 [2015] PIERC 55 [2014] PIERC 54 [2014] PIERC 53 [2014] PIERC 52 [2014] PIERC 51 [2014] PIERC 50 [2014] PIERC 49 [2014] PIERC 48 [2014] PIERC 47 [2014] PIERC 46 [2014] PIERC 45 [2013] PIERC 44 [2013] PIERC 43 [2013] PIERC 42 [2013] PIERC 41 [2013] PIERC 40 [2013] PIERC 39 [2013] PIERC 38 [2013] PIERC 37 [2013] PIERC 36 [2013] PIERC 35 [2013] PIERC 34 [2013] PIERC 33 [2012] PIERC 32 [2012] PIERC 31 [2012] PIERC 30 [2012] PIERC 29 [2012] PIERC 28 [2012] PIERC 27 [2012] PIERC 26 [2012] PIERC 25 [2012] PIERC 24 [2011] PIERC 23 [2011] PIERC 22 [2011] PIERC 21 [2011] PIERC 20 [2011] PIERC 19 [2011] PIERC 18 [2011] PIERC 17 [2010] PIERC 16 [2010] PIERC 15 [2010] PIERC 14 [2010] PIERC 13 [2010] PIERC 12 [2010] PIERC 11 [2009] PIERC 10 [2009] PIERC 9 [2009] PIERC 8 [2009] PIERC 7 [2009] PIERC 6 [2009] PIERC 5 [2008] PIERC 4 [2008] PIERC 3 [2008] PIERC 2 [2008] PIERC 1 [2008]
2019-11-02
Enhanced TWI Under Wall Parameter Uncertainty with the Parametric Sparse Recovery Method
By
Progress In Electromagnetics Research C, Vol. 96, 193-204, 2019
Abstract
In recent years, through-wall imaging (TWI) has gained much research interest because of urgent needs of civilian, security, and defense applications. TWI based on compressive sensing (CS) method can produce high resolution, assuming that the wall parameters are known in prior. However, it is difficult to know the exact wall parameters in actual scenarios. With unknown wall parameters, the dictionary matrix is not a fixed one. Therefore, CS theory cannot be directly applied in the TWI. This paper presents a parametric sparse recovery method for TWI with unknown wall parameters. The original reconstruction problem is reformulated into a joint optimization one which can be solved with an alternating minimization algorithm. Specifically, the proposed method performs the wall parameter estimation and sparse image reconstruction in an iterative procedure. With the estimated wall parameter which is or close to the true one, the high fidelity and high-resolution image is obtained. Experimental simulations show that the proposed method can obtain an autofocus image and improve the image quality.
Citation
Fang-Fang Wang Huiying Wu Tingting Qin , "Enhanced TWI Under Wall Parameter Uncertainty with the Parametric Sparse Recovery Method," Progress In Electromagnetics Research C, Vol. 96, 193-204, 2019.
doi:10.2528/PIERC19072604
http://www.jpier.org/PIERC/pier.php?paper=19072604
References

1. Frazier, L. M., "Surveillance through walls and other opaque materials," Proceedings of the 1996 IEEE National Radar Conference, 1996, 27-31, IEEE, 1995.

2. Song, L. P., C. Yu, and Q. H. Liu, "Through-wall imaging (TWI) by radar: 2-D tomographic results and analyses," IEEE Transactions on Geoscience & Remote Sensing, Vol. 43, No. 12, 2793-2798, 2005.
doi:10.1109/TGRS.2005.857914

3. Amin, M. G., Through-the-wall Radar Imaging, CRC Press, 2011.

4. Massa, A., P. Rocca, and G. Oliveri, "Compressive sensing in electromagnetics — A review," IEEE Antennas & Propagation Magazine, Vol. 57, No. 1, 224-238, 2015.
doi:10.1109/MAP.2015.2397092

5. Donoho, D. L., "Compressed sensing," IEEE Transactions on Information Theory, Vol. 52, No. 4, 1289-1306, 2006.
doi:10.1109/TIT.2006.871582

6. Herman, M. A. and T. Strohmer, "High-resolution radar via compressed sensing," IEEE Transactions on Signal Processing, Vol. 57, No. 6, 2275-2284, 2009.
doi:10.1109/TSP.2009.2014277

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

8. Yoon, Y. S. and M. G. Amin, "Compressed sensing technique for high-resolution radar imaging," SPIE Defense and Security Symposium, International Society for Optics and Photonics, 69681A-69681A-10, 2008.
doi:10.1117/12.777175

9. Huang, Q., et al., "UWB through-wall imaging based on compressive sensing," IEEE Transactions on Geoscience & Remote Sensing, Vol. 48, No. 3, 1408-1415, 2010.
doi:10.1109/TGRS.2009.2030321

10. Leigsnering, M., et al., "Multipath exploitation in through-the-wall radar imaging using sparse reconstruction," IEEE Transactions on Aerospace & Electronic Systems, Vol. 50, No. 2, 920-939, 2014.
doi:10.1109/TAES.2013.120528

11. Leigsnering, M., et al., "Multipath exploitation and suppression for SAR imaging of building interiors: An overview of recent advances," IEEE Signal Processing Magazine, Vol. 31, No. 4, 110-119, 2014.
doi:10.1109/MSP.2014.2312203

12. Liu, J., et al., "First-order multipath Ghosts’ characteristics and suppression in MIMO through-wall imaging," IEEE Geoscience & Remote Sensing Letters, Vol. 13, No. 9, 1315-1319, 2016.
doi:10.1109/LGRS.2016.2583795

13. Chen, Y. C., et al., "Motion compensation for airborne SAR via parametric sparse representation," IEEE Transactions on Geoscience & Remote Sensing, Vol. 55, No. 1, 551-562, 2016.
doi:10.1109/TGRS.2016.2611522

14. Li, G., et al., "ISAR 2-D imaging of uniformly rotating targets via matching pursuit," IEEE Transactions on Aerospace & Electronic Systems, Vol. 48, No. 2, 1838-1846, 2012.
doi:10.1109/TAES.2012.6178106

15. Rao, W., et al., "Adaptive sparse recovery by parametric weighted l1 minimization for ISAR imaging of uniformly rotating targets," IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, Vol. 6, No. 2, 942-952, 2013.
doi:10.1109/JSTARS.2012.2215915

16. Leigsnering, M., et al., "Parametric dictionary learning for sparsity-based TWRI in multipath environments," IEEE Transactions on Aerospace & Electronic Systems, Vol. 52, No. 2, 532-547, 2016.
doi:10.1109/TAES.2015.140828