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.
"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
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., L. Qu, B. Wu, 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., F. Ahmad, M. Amin, 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., M. Amin, F. Ahmad, 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., L. Kong, X. Yang, 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., G. Li, Q. Zhang, 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., H. Zhang, X. Wang, 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., G. Li, X. Wang, 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., F. Ahmad, M. G. Amin, 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