Sparsity-based three-dimensional image reconstruction for near-field MIMO radar imaging

Near-field multiple-input multiple-output (MIMO) radar imaging systems are of interest in diverse fieldssuch as medicine, through-wall imaging, airport security, concealed weapon detection, and surveillance. The successfuloperation of these radar imaging systems highly depends on the quality of the images reconstructed from radar data.Since the underlying scenes can be typically represented sparsely in some transform domain, sparsity priors can effectivelyregularize the image formation problem and hence enable high-quality reconstructions. In this paper, we developan efficient three-dimensional image reconstruction method that exploits sparsity in near-field MIMO radar imaging.Sparsity is enforced using total variation regularization, and the reflectivity distribution is reconstructed iterativelywithout requiring computation with huge matrices. The performance of the developed algorithm is illustrated throughnumerical simulations. The results demonstrate the effectiveness of the sparsity-based method compared to a classicalimage reconstruction method in terms of image quality.

PDF

___

[1] Yarovoy AG, Savelyev TG, Aubry PJ, Lys PE, Ligthart LP. UWB array-based sensor for near-field imaging. IEEE Trans on Microwave Theory and Techniques 2007; 55 (6): 1288–1295.
[2] Zhuge X, Yarovoy AG. A sparse aperture MIMO-SAR-based UWB imaging system for concealed weapon detection. IEEE Transactions on Geoscience and Remote Sensing 2011; 49 (1): 509-518.
[3] Ahmed SS, Schiessl A, Gumbmann F, Tiebout M, Methfessel S et al. Advanced microwave imaging. IEEE Microwave Magazine 2012; 13 (6): 26-43.
[4] Zhou Y, Yu J, Xu Z, Wang Y, Cao Q. Fast modeling methods for estimating imaging performance of whole body screening. IEEE Antennas and Wireless Propagation Letters 2019; 18 (1): 39-43.
[5] Sheen DM, McMakin DL, Hall TE. Three-dimensional millimeter-wave imaging for concealed weapon detection. IEEE Transactions on Microwave Theory and Techniques 2001; 49 (9): 1581-1592.
[6] Li S, Zhao G, Li H, Ren B, Hu W et al. Near-field radar imaging via compressive sensing. IEEE Transactions on Antennas and Propagation 2015; 63 (2): 828-833.
[7] Wei SJ, Zhang XL, Shi J, Liao KF. Sparse array microwave 3-D imaging: compressed sensing recovery and experimental study. Progress in Electromagnetics Research 2013; 135: 161-181.
[8] Yiğit E. Compressed sensing for millimeter-wave ground based SAR/ISAR imaging. Journal of Infrared, Millimeter, and Terahertz Waves 2014; 35 (11): 932-948.
[9] Zhao G, Li S, Ren B, Qiu Q, Sun H. Cylindrical three-dimensional millimeter-wave imaging via compressive sensing. International Journal of Antennas and Propagation 2015; 2015: 218751.
[10] Ahmed SS, Schiessl A, Schmidt LP. A novel fully electronic active real-time imager based on a planar multistatic sparse array. IEEE Transactions on Microwave Theory and Techniques 2011; 59 (12): 3567-3576.
[11] Zhuge X, Yarovoy AG. Sparse multiple-input multiple-output arrays for high-resolution near-field ultra-wideband imaging. IET Microwaves, Antennas & Propagation 2011; 5 (13): 1552-1562.
[12] Tan K, Wu S, Wang Y, Ye S, Chen J et al. A novel two-dimensional sparse MIMO array topology for UWB short-range imaging. IEEE Antennas and Wireless Propagation Letters 2015; 15: 702-705.
[13] Yanik ME, Torlak M. Near-field MIMO-SAR millimeter-wave imaging with sparsely sampled aperture data. IEEE Access 2019; 7: 31801-31819.
[14] Anadol E, Seker I, Camlica S, Topbas TO, Koc S et al. UWB 3D near-field imaging with a sparse MIMO antenna array for concealed weapon detection. In: SPIE Radar Sensor Technology XXII; Orlando, FL, USA; 2018. p. 106331D.
[15] Zhuge X, Yarovoy AG, Savelyev T, Ligthart L. Modified Kirchhoff migration for UWB MIMO array-based radar imaging. IEEE Transactions on Geoscience and Remote Sensing 2010; 48 (6): 2692-2703.
[16] Zhuge X, Yarovoy AG. Three-dimensional near-field MIMO array imaging using range migration techniques. IEEE Transactions on Image Processing 2012; 21 (6): 3026-3033.
[17] Lopez-Sanchez JM, Fortuny-Guasch J. 3-D radar imaging using range migration techniques. IEEE Transactions on Antennas and Propagation 2000; 48 (5): 728-737.
[18] Sakamoto T, Sato T, Aubry PJ, Yarovoy AG. Ultra-wideband radar imaging using a hybrid of Kirchhoff migration and Stolt FK migration with an inverse boundary scattering transform. IEEE Transactions on Antennas and Propagation 2015; 63 (8): 3502-3512.
[19] Tan K, Wu S, Liu X, Fang G. Omega-k algorithm for near-field 3-D image reconstruction based on planar SIMO/MIMO array. IEEE Transactions on Geoscience and Remote Sensing 2018; 57 (4): 2381-2394.
[20] Tan K, Wu S, Liu X, Fang G. A modified omega-k algorithm for near-field MIMO array-based 3-D reconstruction. IEEE Geoscience and Remote Sensing Letters 2018; 18 (99): 1-5.
[21] Álvarez Y, Rodriguez-Vaqueiro Y, Gonzalez-Valdes B, Las-Heras F, García-Pino A. Fourier-based imaging for subsampled multistatic arrays. IEEE Transactions on Antennas and Propagation 2016; 64 (6): 2557-2562.
[22] Candès EJ, Wakin MB. An introduction to compressive sampling. IEEE Signal Processing Magazine 2008; 25 (2): 21-30.
[23] Potter LC, Ertin E, Parker JT, Cetin M. Sparsity and compressed sensing in radar imaging. Proceedings of the IEEE 2010; 98 (6): 1006-1020.
[24] Ender JHG. On compressive sensing applied to radar. Signal Processing 2010; 90 (5): 1402-1414.
[25] Stojanovic I, Çetin M, Karl WC. Compressed sensing of monostatic and multistatic SAR. IEEE Geoscience and Remote Sensing Letters 2013; 10 (6): 1444-1448.
[26] Cheng Q, Alomainy A, Hao Y. Near-field millimeter-wave phased array imaging with compressive sensing. IEEE Access 2017; 5: 18975-18986.
[27] Tropp JA, Wright SJ. Computational methods for sparse solution of linear inverse problems. Proceedings of the IEEE 2010; 98 (6): 948-958.
[28] Samadi S, Çetin M, Masnadi-Shirazi MA. Sparse representation-based synthetic aperture radar imaging. IET Radar, Sonar & Navigation 2011; 5 (2): 182-193.
[29] Yang Z, Zheng YR. A comparative study of compressed sensing approaches for 3-D synthetic aperture radar image reconstruction. Digital Signal Processing 2014; 32: 24-33.
[30] Guo J, Zhang J, Yang K, Zhang B, Hong W et al. Information capacity and sampling ratios for compressed sensingbased SAR imaging. IEEE Geoscience and Remote Sensing Letters 2015; 12 (4): 900-904.
[31] Ma C, Yeo TS, Zhao Y, Feng J. MIMO radar 3D imaging based on combined amplitude and total variation cost function with sequential order one negative exponential form. IEEE Transactions on Image Processing 2014; 23 (5): 2168-2183.
[32] Guo L, Abbosh AM. Microwave stepped frequency head imaging using compressive sensing with limited number of frequency steps. IEEE Antennas and Wireless Propagation Letters 2015; 14: 1133-1136.
[33] Hu X, Tong N, Song B, Ding S, Zhao X. Joint sparsity-driven three-dimensional imaging method for multiple-input multiple-output radar with sparse antenna array. IET Radar, Sonar & Navigation.2016; 11 (5): 709-720.
[34] Ma C, Yeo TS, Ng BP. Multiple input multiple output radar imaging based on multidimensional linear equations and sparse signal recovery. IET Radar, Sonar & Navigation 2017; 12 (1): 3-10.
[35] Jiao Z, Ding C, Liang X, Chen L, Zhang F. Sparse Bayesian learning based three-dimensional imaging algorithm for off-grid air targets in MIMO radar array. Remote Sensing 2018; 10 (3): 369.
[36] Huang P, Li X, Wang H. Tensor-based match pursuit algorithm for MIMO radar imaging. Radio Engineering 2018; 27 (2): 581.
[37] Chen J, Li Y, Wang J, Li Y, Zhang Y. An accurate imaging algorithm for millimeter wave synthetic aperture imaging radiometer in near-field. Progress In Electromagnetics Research 2013; 141: 517-535.
[38] Gurbuz AC, McClellan JH, Scott WR Jr. Compressive sensing for subsurface imaging using ground penetrating radar. Signal Processing 2009; 89 (10): 1959-1972.
[39] Zhang W, Hoorfar A. A generalized approach for SAR and MIMO radar imaging of building interior targets with compressive sensing. IEEE Antennas and Wireless Propagation Letters 2015; 14: 1052-1055.
[40] Geman D, Yang C. Nonlinear image recovery with half-quadratic regularization. IEEE Transactions on Image Processing 1995; 4 (7): 932-946.
[41] Vogel CR, Oman ME. Fast, robust total variation-based reconstruction of noisy, blurred images. IEEE Transactions on Image Processing 1998; 7 (6): 813-824.
[42] Kocamis MB, Oktem FS. Optimal design of sparse MIMO arrays for near-field ultrawideband imaging. In: 2017 25th European Signal Processing Conference; Kos, Greece; 2017. pp. 1952-1956.
[43] Beck A, Teboulle M. Gradient-based algorithms with applications to signal recovery. In: Palomar DP, Eldar YC (editors). Convex Optimization in Signal Processing and Communications. New York, NY, USA: Cambridge University Press, 2010, pp. 42-88.
[44] Oktem FS, Gao L, Kamalabadi F. Computational spectral and ultrafast imaging via convex optimization. In: Monga V (editor). Handbook of Convex Optimization Methods in Imaging Science. Cham, Switzerland: Springer, 2018, pp. 105-127.