LAUSR

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 observation-based FMCW SAR sparse imaging to deal with a few key issues for practical radar systems. In particular, we describe: first, $L_q$-norm $(0 < q \leq 1)$ regularization-based imaging technique that makes sparse reconstruction of large-scale scene possible; second, $L_{2,q}$-norm regularization-based imaging technique that minimizes the azimuth ambiguities in high-resolution sparse imaging; and third, a sparse imaging technique that supports real-time applications of FMCW SAR imaging.