Sparse reconstruction technique can be used to provide high-resolution imaging result for through-the-wall radar (TWR) system. Since conventional sparse imaging reconstruction algorithms usually require a tremendous amount of computer memory and computational complexity, it is very difficult to apply in the practical large-scale TWR imaging applications. To solve the above problem, an efficient sparse imaging reconstruction algorithm is proposed in this paper. The proposed imaging method combines the spectral projection gradient L1-norm (SFGL1) algorithm with nonuniform fast Fourier transform (NUFFT) technique to achieve imaging reconstruction. Benefiting from the function handle operation of SPGL1 and computational efficiency of NUFFT, the proposed imaging algorithm can significantly reduce the memory requirement and computation complexity. The simulated and experimental results have shown that the proposed imaging method can significantly reduce the required computer memory and computational cost while providing the similar recovered image quality as the conventional sparse imaging method.
2. Yoon, Y.-S. and M. G. Amin, "Compressed sensing technique for high-resolution radar imaging," Proc. SPIE, Vol. 6968, 69681A-1-69681A-10, 2008.
3. Huang, Q., L. Qu, B.Wu, and G. Fang, "UWB through-wall imaging based on compressive sensing," IEEE Trans. Geosci. Remote Sens., Vol. 48, No. 3, 1408-1415, Mar. 2010.
4. Browne, K. E., R. J. Burkholder, and J. L. Volakis, "Fast optimization of through-wall radar images via the method of Lagrange multipliers," IEEE Trans. Antennas Propag., Vol. 61, No. 1, 320-328, Jan. 2013.
5. Li, G. and R. Burkholder, "Hybrid matching pursuit for distributed through-wall radar imaging," IEEE Trans. Antennas Propag., Vol. 63, No. 4, 1701-1711, Apr. 2015.
6. Zhang, W. and A. Hoofar, "A generalized approach for SAR and MIMO radar imaging of building interior targets with compressive sensing," IEEE Antennas Wireless Propag. Lett., Vol. 14, 1052-1055, 2015.
7. Lagunas, E., M. G. Amin, F. Ahmad, and M. Najar, "Joint wall mitigation and compressive sensing for indoor image reconstruction," IEEE Trans. Geosci. Remote Sens., Vol. 51, No. 2, 891-906, Feb. 2013.
8. Ahmad, F., J. Qian, and M. G. Amin, "Wall clutter mitigation using discrete prolate spheroidal sequences for sparse reconstruction of indoor stationary scenes," IEEE Trans. Geosci. Remote Sens., Vol. 53, No. 3, 1549-1557, Mar. 2015.
9. Leigsnering, M., F. Ahmad, M. Amin, and A. Zoubir, "Parametric dictionary learning for sparistybased TWRI in multipath environments," IEEE Trans. Aerosp. Electron. Syst., Vol. 52, No. 2, 532-547, Apr. 2016.
10. Van den Berg, E. and M. P. Friedlander, "Probing the Pareto frontier for basis pursuit solutions," SIAM J. Sci. Comput., Vol. 31, 890-912, Nov. 2008.
11. Greengard, L. and J.-Y. Lee, "Accelerating the nonuniform fast Fourier transform," SIAM Rev., Vol. 46, No. 3, 443-454, 2004.
12. Ward, R., "Compressed sensing with cross validation," IEEE Trans. Inf. Theory, Vol. 55, No. 12, 5773-5782, Dec. 2009.
13. Van den Berg, E. and M. P. Friedlander, "SPGL1: A solver for large-scale sparse reconstruction,", Jun. 2007, [Online], Available: http://www.cs.ubc.ca/labs/scl/spgl1.
14. Dilsavor, R., et al., "Experiments on wideband through the wall imaging," Proc. SPIE Symp. Defense Security, Algorithms Synthetic Aperture Radar Imagery XII Conf., Vol. 5808, 196-209, 2005.