Based on sparse Bayesian compressed sensing (BCS) imaging, a multitask Bayesian compressed sensing (MT-BCS) algorithm was proposed for the joint sparse of the target imaging space of stepped frequency continuous wave groundpenetrating radar (SFCW-GPR).For different tasks, a general priori hierarchical Bayesian model is adopted.MT-BCS algorithm can recover the original signal from fewer random samples by utilizing the correlation between each set of observation data under the condition of limited observation data.The algorithm is independent in the reconstruction of each group of tasks.While making full use of the correlation of observation data, it can retain the characteristics of each group of data and realize the information sharing between each group of observation data.The simulation results show that the reconstruction performance of MT-BCS is good, and the reconstruction effect of MT-BCS algorithm is better than that of BCS algorithm under the same condition.