Microwave array 3-D imaging is an emerging technique capable of producing a 3-D map of scattered electric fields. Its all-weather and large scene imaging features make it an attractive powerful tool for target detection and feature extraction. Typically, a microwave array 3-D imaging system based on the classical sampling theory requires a large dense 2-D antenna array, which may suffer from a very high cost. To reduce the number of the antenna array elements, this paper surveys the use of compressed sensing recovery and sparse measurement strategies for microwave array 3-D imaging. Combining with the typical spatial sparsity of the underlying scene, we pose the sparse array microwave 3-D imaging as finding sparse solutions to under-determined linear equations. Further, to reduce the computational of the compressed sensing recovery with the large-scale echoes data, we divide the underlying 3-D scene into a series of equal-range 2-D slices, and deal with these slices separately using the orthogonal matching pursuit (OMP) algorithm. Lastly, the performance of the presented compressed sensing approach is verified by an X-band microwave array 3-D imaging system. The experimental results demonstrate that the compressed sensing approach can produce a better resolution 3-D image of the observed scatterers compared with the conventional method, especially in the case of very sparse activate antenna array.
"Sparse Array Microwave 3-d
Imaging: Compressed Sensing Recovery and Experimental Study," Progress In Electromagnetics Research,
Vol. 135, 161-181, 2013. doi:10.2528/PIER12082305
1. Peng, , X., , W. Tan, Y. Wang, W. Hong, and Y. Wu, "Convolution back-projection imaging algorithm for downward-looking sparse linear array three dimensional synthetic aperture radar," Progress In Electromagnetics Research, Vol. 129, 287-313, 2012. doi: --- Either ISSN or Journal title must be supplied.
2. Mohammadpoor, , M., , R. S. A. Raja Abdullah, A. Ismail, and A. F. Abas, "A circular synthetic aperture radar for on-the-ground object detection ," Progress In Electromagnetics Research, Vol. 122, 269-292, 2012. doi:10.2528/PIER11082201
3. Tan, , W., , W. Hong, Y. Wang, and Y. Wu, "A novel spherical-wave three-dimensional imaging algorithm for microwave cylindrical scanning geometries," Progress In Electromagnetics Research, Vol. 111, 43-70, 2011. doi:10.2528/PIER10100307
4. Huang, , Y., Y. Liu, Q. H. Liu, and J. Zhang, "Improved 3-d GPR detection by NUFFT combined with MPD method," Progress In Electromagnetics Research, Vol. 103, 185-199, 2010. doi:10.2528/PIER10021005
5. Deng, , M. Y. and X. Liu, "Electromagnetic imaging methods for nondestructive evaluation applications," Sensors , Vol. 11, No. 12, 11774-11808, 2011. doi:10.3390/s111211774
6. Zhou, , H., , T. Takenaka, J. Johnson, and T. Tanaka, "A breast imaging model using microwaves and a time domain three dimensional reconstruction method," Progress In Electromagnetics Research, Vol. 93, 57-70, 2009. doi:10.2528/PIER09033001
7. Wei, , H.-Y. and M. Soleimani, "Three dimensional magnetic induction tomography imaging using a matrix free Krylov subspace inversion algorithm," Progress In Electromagnetics Research, Vol. 122, 2945, 2012.
8. Soleimani, , M., , C. N. Mitchell, R. Banasiak, R. Wajman, and A. Adler, "Four-dimensional electrical capacitance tomography imaging using experimental data," Progress In Electromagnetics Research, Vol. 90, 171-186, 2009. doi:10.2528/PIER09010202
9. Ren, , X. Z., L. H. Qiao, and Y. Qin, "A three-dimensional imaging algorithm for tomography SAR based on improved interpolated array transform1 ," Progress In Electromagnetics Research, Vol. 120, 181-193, 2011.
10. Liao, , K. F., X.-L. Zhang, and J. Shi, "Fast 3-d microwave imaging method based on subaperture approximation," Progress In Electromagnetics Research, Vol. 126, 333-353, 2012. doi:10.2528/PIER12011106
11. Yu, , L. and Y. Zhang, "A 3D target imaging algorithm based on two-pass circular SAR observations," Progress In Progress In, Vol. 122, 341-360, 2012.
12. Shi, , J., , X.-L. Zhang, J.-Y. Yang, et al. "APC trajectory design for `One-Active' linear-array three-dimensional imaging SAR," IEEE Transactions on Geoscience and Remote Sensing, Vol. 48, No. 3, 1470-1486, 2010. doi:10.1109/TGRS.2009.2031430
13. Shi, , J., X. L. Zhang, J. Yang, and K. Liao, "Experiment results on one-active LASAR," IEEE Radar Conference, 1-4, 2009.
14. Juan, , L. S. , F. G. Joaquim, and , "3-D radar imaging using range migration techniques," IEEE Transactions on Antennas and Propagation,, Vol. 48, No. 5, 2000.
15. Zhang, D. H. , X. L. Zhang, and , "Downward-looking 3-D linear array SAR imaging based on chirp scaling algorithm," Asian-Pacific Conference on SAR , 1043-1046, 2009.
16. Qi, , Y., , W. Tan, Y. Wang, W. Hong, and Y. Wu, "3D bistatic omega-k imaging algorithm for near range microwave imaging systems with bistatic planar scanning geometry ," Progress In Electromagnetics Research, Vol. 121, 409-431, 2011. doi:10.2528/PIER11090205
17. Li, , S., , B. Ren, H.-J. Sun, W. Hu, and X. Lv, "Modified wavenumber domain algorithm for three-dimensional millimeter-wave imaging," Progress In Electromagnetics Research,, Vol. 124, 35-53, 2012. doi:10.2528/PIER11112406
18. Montefusco, L. B., , D. Lazzaro, S. Papi, and C. Guerrini, "A fast compressed sensing approach to 3D MR image reconstruction," IEEE Transactions on Medical Imaging,, Vol. 30, No. 5, 2011. doi:10.1109/TMI.2010.2068306
19. Berger, , C. R., , S. L. Zhou J. C. Preisig, and P. Willett, "Sparse channel estimation for multicarrier underwater acoustic communication: From subspace methods to compressed sensing ," IEEE Transactions on Signal Processing, Vol. 58, No. 3, 2010. doi:10.1109/TSP.2009.2038424
20. Mahalanobis, A. , R. Muise, and , "Object specfic image reconstruction using a compressive sensing architecture for application in surveillance systems," IEEE Transactions on Aerospace and Electronic System, Vol. 45, No. 3, 2009. doi:10.1109/TAES.2009.5259191
21. Wei, , S.-J., , X.-L. Zhang, J. Shi, and G. Xiang, "Sparse reconstruction for SAR imaging based on compressed sensing," Progress In Electromagnetics Research,, Vol. 109, 63-81, 2010. doi:10.2528/PIER10080805
22. Patel, , V. M., , G. R. Easley, and D. M. Healy, "Compressed synthetic aperture radar," IEEE Journal of Selected Topics in Signal Processing, Vol. 4, No. 2, 244-254, 2010. doi:10.1109/JSTSP.2009.2039181
23. Zhu, X.-X. and R. Bamler, "Tomographic SAR inversion by L1-norm regularization the compressive sensing approach," IEEE Transactions on Geoscience and Remote Sensing, Vol. 48, No. 10, 3839-3846, 2010. doi:10.1109/TGRS.2010.2048117
24. Budillon, , A., , A. Evangelista, and G. Schirinzi, "Three-dimensional SAR focussing from multipass signals using compres-sive sampling ," IEEE Transactions on Geoscience and Remote Sensing, Vol. 49, No. 1, 488-499, 2010. doi:10.1109/TGRS.2010.2054099
25. Chen, , J., , J. Gao, Y. Zhu, W. Yang, and P. Wang, "A novel image formation algorithm for high-resolution wide-swath spaceborne SAR using compressed sensing on azimuth displacement phase center antenna, ," Progress In Electromagnetics Research, Vol. 125, 527-543, 2012. doi:10.2528/PIER11121101
26. Li, J., , S. Zhang, and J. Chang, "Applications of compressed sensing for multiple transmitters multiple azimuth beams SAR imaging," Progress In Electromagnetics Research, Vol. 127, 259-275, 2012. doi:10.2528/PIER12021307
27. Oliveri, , G., , P. Rocca, and A. Massa, "A Bayesian-compressive-sampling-based inversion for imaging sparse scatterers," IEEE Transactions on Geoscience and Remote Sensing, Vol. 49, No. 10, 3993-4006, 2011. doi:10.1109/TGRS.2011.2128329
28. Poli, L., , G. Oliveri, and A. Massa, "Microwave imaging within the first-order born approximation by means of the contrast-field Bayesian compressive sensing," IEEE Transactions on Antennas and Propagation, Vol. 60, No. 6, 2865-2879, 2012. doi:10.1109/TAP.2012.2194676
29. Wei, , S.-J., , X.-L. Zhang, and J. Shi, "Linear array SAR imaging via compressed sensing," Progress In Electromagnetics Research,, Vol. 117, 299-319, 2011.
31. Candes, E. J. and M. Wakin, "An introduction to compressive sampling," IEEE Signal Processing Magazine, 21-30, 2008. doi:10.1109/MSP.2007.914731
32. Candes, , E. J., J. K. Romberg, and T. Tao, "Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information," IEEE Trans. Inf. Theory, Vol. 52, No. 2, 489-509, 2006. doi:10.1109/TIT.2005.862083
33. Tropp, , J. A., "Greed is good: Algrorithm results for sparse approximation ," IEEE Trans. Inf. Theory, Vol. 50, No. 10, 2231-2242, 2004. doi:10.1109/TIT.2004.834793
34. Needell, , D. and R. Vershynin, "Uniform uncertainty principle and signal recovery via regularized orthogonal matching pursuit," Found. Comput. Math., Vol. 9, No. 3, 317-334, 2009. doi:10.1007/s10208-008-9031-3
35. Fornasier, , M. and H. Rauhut, "Compressive sensing," Handbook of Mathematical Methods in Imaging, Vol. 1, 187-229, 2010.
36. Samadi, , S., M. Cetin, and M. A. Masnadi-Shirazi, "Sparse representation-based synthetic aperture radar imaging," IET Radar, Sonar and Navigation, Vol. 5, No. 2, 182-193, 2011. doi:10.1049/iet-rsn.2009.0235