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2018-10-18
FDTD Based Dictionary Matrix for Sparsity-Based through -Wall Radar Imaging
By
Progress In Electromagnetics Research M, Vol. 75, 21-28, 2018
Abstract
Compressive sensing for through-wall radar imaging (TWRI) is a promising method to obtain a high-resolution image with limited number of measurements. The capability of the existing method in the framework of CS is limited due to the model error stemmed from the approximated signal model which does not consider multipath returns or only consider first-order interior wall multipath returns. In order to exploit various multipath returns, finite-difference time domain (FDTD) technique is used to obtain the scattered signal for each assumed target position and then to construct the exact forward scattering model. Then, sparse reconstruction is used to solve this linear inverse problem. Numerical results demonstrate that the proposed approach performs better at ghost suppression in the same condition of the signal-to-noise ratio (SNR).
Citation
Fang-Fang Wang, Sichao Zhong, Huiying Wu, Tingting Qin, and Wei Hong, "FDTD Based Dictionary Matrix for Sparsity-Based through -Wall Radar Imaging," Progress In Electromagnetics Research M, Vol. 75, 21-28, 2018.
doi:10.2528/PIERM18082202
References

1. Amin, M. and K. Sarabandi, "Special issue on remote sensing of building interior," IEEE Geoscience & Remote Sensing Letters, Vol. 5, 118-118, 2008.
doi:10.1109/LGRS.2008.915679

2. Amin, M. G., Through-the-Wall Radar Imaging, CRC Press, 2010.

3. Yektakhah, B. and K. Sarabandi, "All-directions through-the-wall radar imaging using a small number of moving transceivers," IEEE Transactions on Geoscience & Remote Sensing, Vol. 54, 6415-6428, 2016.
doi:10.1109/TGRS.2016.2585112

4. Herman, M. A. and T. Strohmer, "High-resolution radar via compressed sensing," IEEE Transactions on Signal Processing, Vol. 57, 2275-2284, 2009.
doi:10.1109/TSP.2009.2014277

5. Donoho, D. L., "Compressed sensing," IEEE Transactions on Information Theory, Vol. 52, 1289-1306, Apr. 2006.
doi:10.1109/TIT.2006.871582

6. Candes, E. J., J. Romberg, and T. Tao, "Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information," IEEE Transactions on Information Theory, Vol. 52, 489-509, 2004.
doi:10.1109/TIT.2005.862083

7. Huang, Q., L. Qu, B.Wu, and G. Fang, "UWB through-wall imaging based on compressive sensing," IEEE Transactions on Geoscience & Remote Sensing, Vol. 48, 1408-1415, 2010.
doi:10.1109/TGRS.2009.2030321

8. Wu, Q., Y. D. Zhang, F. Ahmad, and M. G. Amin, "Compressive-sensing-based high-resolution polarimetric through-the-wall radar imaging exploiting target characteristics," IEEE Antennas & Wireless Propagation Letters, Vol. 14, 1043-1047, 2015.
doi:10.1109/LAWP.2014.2380787

9. Zhang, W. and A. Hoorfar, "A generalized approach for SAR and MIMO radar imaging of building interior targets with compressive sensing," IEEE Antennas & Wireless Propagation Letters, Vol. 14, 1052-1055, 2015.
doi:10.1109/LAWP.2015.2394746

10. Wang, X., G. Li, Y. Liu, and M. G. Amin, "Two-level block matching pursuit for polarimetric through-wall radar imaging," IEEE Transactions on Geoscience & Remote Sensing, Vol. 99, 1-13, 2018.

11. Burkholder, R. J., "Electromagnetic models for exploiting multi-path propagation in through-wall radar imaging," International Conference on Electromagnetics in Advanced Applications, 572-575, 2009.

12. Gennarelli, G. and F. Soldovieri, "Multipath ghosts in radar imaging: Physical insight and mitigation strategies," IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, Vol. 8, 1078-1086, 2015.

13. Liu, J., L. Kong, X. Yang, and Q. H. Liu, "First-order multipath ghosts’ characteristics and suppression in MIMO through-wall imaging," IEEE Geoscience & Remote Sensing Letters, Vol. 13, 1315-1319, 2016.
doi:10.1109/LGRS.2016.2583795

14. Setlur, P., M. Amin, and F. Ahmad, "Multipath model and exploitation in through-the-wall and urban radar sensing," IEEE Transactions on Geoscience & Remote Sensing, Vol. 49, 4021-4034, 2011.
doi:10.1109/TGRS.2011.2128331

15. Setlur, P., G. Alli, and L. Nuzzo, "Multipath exploitation in through-wall radar imaging via point spread functions," IEEE Transactions on Image Processing, Vol. 22, 4571, 2013.
doi:10.1109/TIP.2013.2256916

16. Leigsnering, M., M. Amin, F. Ahmad, and A. M. Zoubir, "Multipath exploitation and suppression for SAR imaging of building interiors: An overview of recent advances," IEEE Signal Processing Magazine, Vol. 31, 110-119, 2014.
doi:10.1109/MSP.2014.2312203

17. Leigsnering, M., F. Ahmad, M. Amin, and A. Zoubir, "Multipath exploitation in through-the-wall radar imaging using sparse reconstruction," IEEE Transactions on Aerospace & Electronic Systems, Vol. 50, 920-939, 2014.
doi:10.1109/TAES.2013.120528

18. Leigsnering, M., F. Ahmad, M. G. Amin, and A. M. Zoubir, "Parametric dictionary learning for sparsity-based TWRI in multipath environments," IEEE Transactions on Aerospace & Electronic Systems, Vol. 52, 532-547, 2016.
doi:10.1109/TAES.2015.140828

19. Dogaru, T. and C. Le, "SAR images of rooms and buildings based on FDTD computer models," IEEE Transactions on Geoscience & Remote Sensing, Vol. 47, 1388-1401, 2009.
doi:10.1109/TGRS.2009.2013841

20. Lagunas, E., M. G. Amin, F. Ahmad, and M. Najar, "Joint wall mitigation and compressive sensing for indoor image reconstruction," IEEE Transactions on Geoscience and Remote Sensing, Vol. 51, 891-906, Feb. 2013.
doi:10.1109/TGRS.2012.2203824