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A Generalized Sparse Bayesian Learning Algorithm for Near-Field Synthetic Aperture Radar Imaging: By Exploiting Impropriety and Noncircularity

Authors: Pan Long, Bi Dongjie, Li Xifeng, Xie Yongle

Abstract:

The near-field synthetic aperture radar (SAR) imaging is an advanced nondestructive testing and evaluation (NDT&E) technique. This paper investigates the complex-valued signal processing related to the near-field SAR imaging system, where the measurement data turns out to be noncircular and improper, meaning that the complex-valued data is correlated to its complex conjugate. Furthermore, we discover that the degree of impropriety of the measurement data and that of the target image can be highly correlated in near-field SAR imaging. Based on these observations, A modified generalized sparse Bayesian learning algorithm is proposed, taking impropriety and noncircularity into account. Numerical results show that the proposed algorithm provides performance gain, with the help of noncircular assumption on the signals.

Keywords: Complex-valued signal processing, synthetic aperture radar (SAR), 2-D radar imaging, compressive sensing, Sparse Bayesian learning.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.3298811

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References:


[1] T¨ulay Adali, Peter J Schreier, and Louis L Scharf. Complex-valued signal processing: The proper way to deal with impropriety. IEEE Transactions on Signal Processing, 59(11):5101–5125, 2011.
[2] Maher Al-Shoukairi, Philip Schniter, and Bhaskar D Rao. A gamp-based low complexity sparse bayesian learning algorithm. IEEE Transactions on Signal Processing, 66(2):294–308, 2018.
[3] Claude Berrou and Alain Glavieux. Near optimum error correcting coding and decoding: Turbo-codes. The best of the best: fifty years of communications and networking research, 45, 2007.
[4] Dongjie Bi, Yongle Xie, Xifeng Li, and Yahong Rosa Zheng. Efficient 2-d synthetic aperture radar image reconstruction from compressed sampling using a parallel operator splitting structure. Digital Signal Processing, 50:171–179, 2016.
[5] Dongjie Bi, Yongle Xie, Lan Ma, Xifeng Li, Xiahan Yang, and Yahong Rosa Zheng. Multifrequency compressed sensing for 2-d near-field synthetic aperture radar image reconstruction. IEEE Transactions on Instrumentation and Measurement, 66(4):777–791, 2017.
[6] Emmanuel J Cand`es and Michael B Wakin. An introduction to compressive sampling
[a sensing/sampling paradigm that goes against the common knowledge in data acquisition]. IEEE signal processing magazine, 25(2):21–30, 2008.
[7] Matteo Carlin, Paolo Rocca, Giacomo Oliveri, Federico Viani, and Andrea Massa. Directions-of-arrival estimation through bayesian compressive sensing strategies. IEEE Transactions on Antennas and Propagation, 61(7):3828–3838, 2013.
[8] M¨ujdat C¸ etin and William Clement Karl. Feature-enhanced synthetic aperture radar image formation based on nonquadratic regularization. IEEE Transactions on Image Processing, 10(4):623–631, 2001.
[9] David L Donoho, Arian Maleki, and Andrea Montanari. Message passing algorithms for compressed sensing: I. motivation and construction. In 2010 IEEE Information Theory Workshop on Information Theory (ITW 2010, Cairo), pages 1–5. IEEE, 2010.
[10] Joachim HG Ender. On compressive sensing applied to radar. Signal Processing, 90(5):1402–1414, 2010.
[11] Hichem Guerboukha, Kathirvel Nallappan, and Maksim Skorobogatiy. Exploiting k-space/frequency duality toward real-time terahertz imaging. Optica, 5(2):109–116, 2018.
[12] Qinghua Guo and Defeng David Huang. A concise representation for the soft-in soft-out lmmse detector. IEEE Communications Letters, 15(5):566–568, 2011.
[13] Gabor Hannak, Alessandro Perelli, Norbert Goertz, Gerald Matz, and Mike E Davies. Performance analysis of approximate message passing for distributed compressed sensing. IEEE Journal of Selected Topics in Signal Processing, 12(5):857–870, 2018.
[14] Mario Huemer, Oliver Lang, and Christian Hofbauer. Component-wise conditionally unbiased widely linear mmse estimation. Signal Processing, 133:227–239, 2017.
[15] Sergey Kharkovsky and Reza Zoughi. Microwave and millimeter wave nondestructive testing and evaluation-overview and recent advances. IEEE Instrumentation & Measurement Magazine, 10(2):26–38, 2007.
[16] Arian Maleki, Laura Anitori, Zai Yang, and Richard G Baraniuk. Asymptotic analysis of complex lasso via complex approximate message passing (camp). IEEE Transactions on Information Theory, 59(7):4290–4308, 2013.
[17] Xiangming Meng, Sheng Wu, Linling Kuang, and Jianhua Lu. Concise derivation of complex bayesian approximate message passing via expectation propagation. arXiv preprint arXiv:1509.08658, 2015.
[18] Xiangming Meng, Sheng Wu, and Jiang Zhu. A unified bayesian inference framework for generalized linear models. IEEE Signal Processing Letters, 25(3):398–402, 2018.
[19] Xiangming Meng and Jiang Zhu. A generalized sparse bayesian learning algorithm for 1-bit doa estimation. IEEE Communications Letters, 22(7):1414–1417, 2018.
[20] Meenu Rani, SB Dhok, and RB Deshmukh. A systematic review of compressive sensing: Concepts, implementations and applications. IEEE Access, 6:4875–4894, 2018.
[21] Peter J Schreier and Louis L Scharf. Statistical signal processing of complex-valued data: the theory of improper and noncircular signals. Cambridge university press, 2010.
[22] David M Sheen, Douglas L McMakin, and Thomas E Hall. Three-dimensional millimeter-wave imaging for concealed weapon detection. IEEE Transactions on microwave theory and techniques, 49(9):1581–1592, 2001.
[23] Qisong Wu, Yimin D Zhang, Moeness G Amin, and Braham Himed. Complex multitask bayesian compressive sensing. In 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 3375–3379. IEEE, 2014.
[24] Zhimin Xu, Wai Lam Chan, Daniel M Mittleman, and Edmund Y Lam. Sparse reconstruction of complex signals in compressed sensing terahertz imaging. In Signal Recovery and Synthesis, page STuA4. Optical Society of America, 2009.
[25] Muhammet Emin Yanik and Murat Torlak. Near-field mimo-sar millimeter-wave imaging with sparsely sampled aperture data. IEEE Access, 7:31801–31819, 2019.
[26] Siwei Yu, A Shaharyar Khwaja, and Jianwei Ma. Compressed sensing of complex-valued data. Signal Processing, 92(2):357–362, 2012.