By combining the data of different spatially distributed sensors, multistatic inverse synthetic aperture radar (ISAR) can provide more stable imaging results compared with monostatic ISAR. In most previous studies of multistatic ISAR imaging, the scatterers on the target are modeled as isotropic points and a conventional monostatic ISAR imaging method is directly applied after rearranging the data of multiple sensors, which will result in the degradation of image quality, especially when the measurements are limited. In practice, the complex amplitude of the scatterer is usually strongly angle dependent; therefore, the echo of different sensors cannot be assumed to be coherent. We consider the fluctuation of the radar cross section and propose a method based on compressed sensing (CS) for multistatic ISAR imaging. By utilizing the block orthogonal matching pursuit (BOMP) method to reconstruct the target image, the requirement of coherence between different sensors can be eliminated. Moreover, in order to apply the CS method, a two-step target motion estimation approach is also presented. A coarse motion estimation method is first applied by tracking the target trajectory with the distance-sum measurement. Then, associating the gradient-based optimization algorithm with BOMP, a parametric block-sparse reconstruction method is developed to jointly correct the residual position error and recover the target image with incoherent echo. Simulation results show the effectiveness of the proposed method.