Joint Multichannel Sparsity for TomoSAR Imaging Under Interrupted SAR Conditions

Currently, ultra-high-resolution SAR can capture detailed structures of urban buildings. As a 3D imaging technology, tomographic SAR(TomoSAR) applies the concept of synthetic aperture in the elevation direction, utilizing SAR images obtained from different perspectives to reconstruct fine structures in three-dimensional space. However, when acquiring SAR images with airborne platforms, safety factors or wind disturbances may cause azimuthal data gaps, which can disrupt the coherence phase of the SAR data, resulting in severe artifacts and noise in the SAR images. The compressed sensing(CS) algorithm can suppress noise to a certain extent, but it does not take into account the prior information between SAR images. In this paper, we make use of the structural sparsity, incorporate the prior information among multi-channel SAR images as a penalty term, and construct a multi-channel SAR reconstruction model based on joint sparsity, so as to enhance the correlation and coherence among different images. Moreover, in order to improve the sparsity of SAR images, we apply the reweighted theory for joint solution and propose a reweighted joint sparse reconstruction algorithm within the framework of the Alternating Direction Method of Multipliers (ADMM). Simulations and tests using real measured data demonstrate that the proposed model and reconstruction algorithm can effectively suppress artifacts, maintain the structural integrity of buildings, and achieve high precision and high integrity.