FMCW SAR Sparse Imaging Based on Approximated Observation: An Overview on Current Technologies

• Published in: IEEE journal of selected topics in applied earth observations and remote sensing 2020, Vol.13, p.4825-4835
• Main Authors: Bi, Hui, Zhu, Daiyin, Bi, Guoan, Zhang, Bingchen, Hong, Wen, Wu, Yirong
• Format: Article
• Language: English
• Subjects: <inline-formula xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"> <tex-math notation="LaTeX"> L_q</tex-math> </inline-formula>-norm regularization; <inline-formula xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"> <tex-math notation="LaTeX"> L_{2; <inline-formula xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"> <tex-math notation="LaTeX">$L_{2,q}$</tex-math> </inline-formula>-norm regularization; Algorithms; Approximated observation; Approximation; Azimuth; Battlefields; Computational efficiency; Computer applications; Computer simulation; Computing costs; Continuous radiation; Data recovery; Echoes; Frequency dependence; Frequency modulation; frequency modulation continuous wave (FMCW); High resolution; Image resolution; Imaging; Imaging techniques; Military technology; New technology; Operators (mathematics); q}</tex-math> </inline-formula>-norm regularization; Radar; Radar equipment; Radar imaging; Radar polarimetry; Real time; real-time imaging; Real-time systems; Reconstruction; Regularization; Resolution; SAR (radar); Signal processing algorithms; sparse imaging; Sparse matrices; Synthetic aperture radar; synthetic aperture radar (SAR); Warning systems
• ISSN: 1939-1404; 2151-1535
• DOI: 10.1109/JSTARS.2020.3017487

Sparsity-driven synthetic aperture radar (SAR) imaging technique for frequency modulation continuous wave (FMCW) has already shown the superiority in terms of performance improvement in imaging and recovery from down-sampled data. However, restricted by the computational cost, conventional FMCW SAR sparse imaging method based on observation matrix is not able to achieve the large-scale scene reconstruction, not to mention real-time processing. To solve this problem, the FMCW SAR sparse imaging theory based on approximated observation is proposed by using an echo simulation operator to replace typical observation matrix, and recovering the scene via 2-D regularization operation. This new technology can achieve high-resolution sparse imaging of the scene with a computational cost close to that of traditional matched filtering algorithms, which makes several applications, such as early-warning and battlefield monitoring, possible by using FMCW SAR sparse imaging system. In this article, we present the recent research progress on approximated observationbased FMCW SAR sparse imaging to deal with a few key issues for practical radar systems. In particular, we describe: first, L q -norm (0