Reweighted Tensor Factorization Method for SAR Narrowband and Wideband Interference Mitigation Using Smoothing Multiview Tensor Model

Abstract

For the interference suppression problem on synthetic aperture radar (SAR) systems, traditional methods have focused on how to remove one kind of interference through nonparametric methods and parametric methods. However, complicated interferences, including both narrowband interferences (NBIs) and wideband interferences (WBIs), severely affect SAR imaging in practical scenarios. Also, the spectra of the complicated interferences can be continuously distributed, which are even harder to mitigate from the received signal. Hence, in this article, we propose a smoothing multiview (SMV) tensor model in range-azimuth-space domain to represent the intrinsically unified characteristics of the NBIs and the WBIs for SAR systems, reserving more azimuth degrees-of-freedom (DOFs) than the previous MV tensor model. The proposed SMV tensor model can enhance the potential low-rank property of the complicated interferences, even though the interferences may be continuously distributed in low-dimensional domains. Moreover, due to the larger scale of the SMV model than those of the traditional models, a complex reweighted tensor factorization (CRTF) algorithm is proposed to factorize the large-scale tensor into the product of two small-scale tensors, achieving both better computational efficiency and better low-rank approximation of complicated interferences. Finally, the measured SAR data with different kinds of simulated complicated interferences are employed to demonstrate the effectiveness and efficiency of the newly designed SMV model and the proposed method compared with the MV model and the complex tensor robust principal component analysis (CT-RPCA) method.