Through-the-wall radar imaging (TWRI) applications allow accurate target localization and high-resolution imaging. However, multipath propagation generates challenges to the image reconstruction procedure. With distortions in the received radar signals, traditional imaging algorithms are not able to acquire high-resolution images. The unpredictability of the indoor scattering environment makes this even worse. In this paper, a novel block orthogonal matching pursuit (BOMP)-based group-sparsity reconstruction algorithm combined with particle swarm optimization (PSO) is proposed for reliable scene reconstruction. The proposed imaging algorithm can recover the image of targets by exploiting multipath propagation and simultaneously estimating wall parameters with high accuracy. The effectiveness of the proposed imaging method has been further demonstrated via simulation results.
AhmadFAminMG (2008) Multi-location wideband synthetic aperture imaging for urban sensing applications. Journal of the Franklin Institute345: 618–639.
2.
AhmadFQianJAminMG (2015) Wall clutter mitigation using discrete prolate spheroidal sequences for sparse reconstruction of indoor stationary scenes. IEEE Transactions on Geoscience and Remote Sensing53: 1549–1557.
3.
CaiXJCuiZHZengJC (2008) Particle swarm optimization with self-adjusting cognitive selection strategy. International Journal of Innovative Computing, Information and Control14: 943–952.
4.
EldarYKuppingerPBolcskeiH (2010) Block-sparse signals: uncertainty relations and efficient recovery. IEEE Transactions on Signal Processing58: 3042–3054.
5.
GennarelliGSoldovieriF (2013) A linear inverse scattering algorithm for radar imaging in multipath environments. Geoscience and Remote Sensing Letters10: 1085–1089.
6.
GurbuzACMcclellanJHScottWR (2009) A compressive sensing data acquisition and imaging method for stepped frequency GPRs. IEEE Transactions on Signal Processing57: 2640–2650
7.
HenningPKiefelM (2013) Quasi-Newton methods: a new direction. Journal of Machine Learning Research14: 843–865.
8.
LagunasEAminMGAhmadFet al. (2013) Determining building interior structures using compressive sensing. Journal of Electronic Imaging22: 381–388.
9.
LavelyEMZhangYIiiEHH (2008) Theoretical and experimental study of through the wall microwave tomography inverse problems. Journal of the Franklin Institute6: 592–617.
10.
LeigsneringMAminMGAhmadFet al. (2013a) Multipath exploitation and suppression for SAR imaging of building interiors: an overview of recent advances. Signal Processing Magazine31: 110–119.
11.
LeigsneringMAhmadFAminMGet al. (2014) Multipath exploitation in through-the-wall radar imaging using sparse reconstruction. IEEE Transactions on Aerospace and Electronic Systems50: 920–938.
12.
LeigsneringMAhmadFAminMGet al. (2013b) Compressive sensing based specular multipath exploitation for through-the-wall radar imaging. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6004–6008.
13.
LeigsneringMAhmadFAminMGet al. (2013c) CS based wall ringing and reverberation mitigation for through-the-wall radar imaging. In: IEEE Radar Conference on Ultra-Wideband (RadarCon13), pp. 1–5.
14.
LiCYangSNguyenTT (2012) A self-learning particle swarm optimizer for global optimization problems. IEEE Transactions on Systems, Man and Cybernetics42: 627–646.
15.
LiGBurkholderRJ (2015) Hybrid matching pursuit for distributed through-wall radar imaging. IEEE Transactions on Antennas and Propagation63: 1701–1711.
16.
MansourHLiuD (2013) Blind multi-path elimination by sparse inversion in through-the-wall-imaging. In: IEEE Computational Advances Multi-Sensor Adaptive Processing (CAMSAP), pp. 256–259.
17.
MccorkleJW (1998) Method and system for forming image by backprojection. United States Patent 5805098.
18.
ParsopoulosKEVrahatisMN (2004) On the computation of all global minimizers through particle swarm optimization. IEEE Transactions on Evolutionary Computation8: 211–224.
19.
QuLLFangGYYangTH (2011) The application of compressed sensing to stepped-frequency ground penetrating radar migration imaging. Journal of Electronics and Information Technology33: 21–26.
20.
SengCHBouzerdoumAAminMGPhungSL (2012) Two-stage fuzzy fusion with applications to through-the-wall radar imaging. Geoscience and Remote Sensing Letters10: 687–691.
21.
SetlurPAminMGAhmadFet al. (2011) Multipath model and exploitation in through-the-wall and urban radar sensing. IEEE Transactions on Geoscience and Remote Sensing49: 4021–4034.
22.
StojnicMParvareshFHassibiB (2009) On the reconstruction of block-sparse signals with an optimal number of measurements. IEEE Transactions on Signal Processing57: 3075–3085.
23.
TanQSongY (2010) A new method for multipath interference suppression in through-the-wall UWB radar imaging. In: Advanced Computer Control (ICACC), pp. 535–540.
24.
UlanderLMHHellstenH dStenstromG (2003) Synthetic-aperture radar processing using fast factorized back-projection. IEEE Transactions on Aerospace and Electronic Systems39: 760–776.
25.
WangJWangPYLiYHet al. (2013) A multipath suppression technique for through-the-wall radar. In: IEEE International Conference on Ultra-Wideband (ICUWB), pp. 215–220.
26.
WrightSJNowakRDFigueiredoMAT (2009) Sparse reconstruction by separable approximation. IEEE Transactions on Signal Processing57: 2479–2493.
27.
ZhangWAminMGAhmadFet al. (2012) Ultra-wideband impulse radar through-the-wall imaging with compressive sensing. International Journal of Antennas and Propagation2012: 473–475.
