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With the recent advances in the theory of compressive sensing (CS), spatial sparsity has been applied for device-free localization (DFL) to reduce the number of measurements required by DFL systems while maintaining the high localization accuracy. However, few works considered the problem of basis mismatch in CS-based DFL. In this paper, a new CS-based DFL approach with basis self-calibration (called DFL-SC) is proposed to overcome the problem incurred by basis errors. The novel feature of this method is to simultaneously estimate the perturbations of the basis matrix and the sparse solution based on a joint optimization framework under sparsity constraints. Meanwhile, we add prior information on the support region obtained from last target's location estimation into the sparse reconstruction process for enhancing reconstruction performance. Experimental results verify the effectiveness of the proposed method on the location accuracy.