This paper presents a low-rank and jointly-sparse approach for imaging stationary targets using multipolarization through-wall radar (TWR). The proposed approach exploits two important characteristics of multichannel TWR signals: low-rank structure of the wall reflections and jointly-sparse structure of the polarization images. The task of removing wall reflections and reconstructing multichannel images of the same scene behind-the-wall is formulated as a regularized least squares optimization problem, where the low-rank regularization is imposed on the wall returns and the joint-sparsity constraint is enforced on the multichannel images. An iterative algorithm is introduced to solve the optimization problem, yielding multichannel images of the indoor targets. Experimental results on real radar data show that the proposed model enhances multichannel imaging in terms of target-to-clutter ratio and indoor target localization.