A novel sparse reconstruction method is developed for direction of arrival (DOA) estimation in the presence of unknown mutual coupling of uniform linear arrays. In the proposed method, a sparse representation for single measurement vector (SMV) is first derived. Then, it is shown that the problem size can be reduced by a linear transformation to eliminate the redundant components in the SMV. Finally, by taking advantage of the banded symmetric Toeplitz structure of the mutual coupling matrix, a reweighted ℓ₁-norm minimization subject to an error-constrained ℓ₂-norm is introduced to determine the DOA estimates without mutual coupling compensation, further enhancing the sparsity and providing a robustness against the noise. Simulation results demonstrate the superiority of the proposed method over its state-of-the-art counterparts.