Joint Tensor Gradient and Sparsity-driven for Fast Robust Video SAR Imaging

Due to the ability of providing continuous observation, the video synthetic aperture radar(ViSAR) has recently received increasing attention. Particularly focusing on imaging of fields that admit a sparse representation, we propose a tensor sparsity-driven method for video SAR imaging. Joint with high-dimension data processing, the proposed method significantly reduce computing burden. Compared with conventional algorithms, numerical simulations validate the effectiveness, accuracy and robustness of our algorithm.