The depth of traditional Covariance Matrix Taper(CMT) null widening algorithms will become shallower after the null broadening under high dynamic conditions. A novel null widening algorithm based on Laplace distribution is proposed. Firstly, the signal model is reformulated and the two-dimensional Laplace algorithm can be derived. Secondly, the algorithm extracts the interference components by projection transformation from the data, and enhances the interference components by weighting the coefficients. Then, null widening algorithm is adopted to expand interference direction based on the reconstructed covariance matrix. Finally, the Power Inversion(PI) algorithm is utilized to suppress the interference. Simulation shows that the depth of the proposed algorithm is increased on the basis of the null widening algorithm, and the robustness of the anti-jamming algorithm is improved. Being applicable to arbitrary planar arrays, the algorithm possesses great practical significance in engineering.