shown in Figure 14. The quantitative evaluations of the RMSE and CORR are consistent with the imaging results shown in Figure 13. EXPERIMENTS ON MULTICHANNEL IMAGING In this section, experiments using measured three-channel InISAR data of a civil airplane are performed to illustrate the sparse multichannel imaging performance. The parameters of this dataset can be found in [27]. First, the sparse apertures of CRS and CGS are applied with a sparse sampling ratio of 1/2. Then, the single-channel independent sparsity and joint multichannel sparsity algorithms [27] are used to realize 2D image reconstruction. Figures 15 and 16 show the imaging results in the cases of CRS and CGS, respectively. In comparison with independent single-channel processing, the joint multichannel sparsity algorithm has better performance on suppressing the side- and grating lobes due to the sparse aperture. Figures 15(d) and 16(d) show the 3D geometry reconstruction of the civil airplane using the joint multichannel sparsity algorithm. It can be seen that the joint multichannel sparsity algorithm can provide excellent 3D reconstruction with complete target shape representation, where the head and wing of the airplane can be clearly distinguished. MOTION ESTIMATION AND PARAMETER INVERSION As has been introduced in the " Nonideal Factor and Model Mismatch Correction " section, there are inevitable model errors, including MTRC and phase error modulation, during