<p>Direction of Arrival (DOA) estimation techniques based on sparse reconstruction have overcome many limitations inherent in traditional subspace-based algorithms, enabling high-accuracy localization even in non-ideal environments including multipath propagation and a limited measurements. However, these methods require the construction of a multidimensional dictionary matrix in the multidimensional parameter estimation case, which can lead to huge computational effort. This study introduces a low-complexity sparse reconstruction framework for polarization-sensitive arrays, aiming to jointly estimate the DOA and polarization. Firstly, the decoupled sparse signal model of polarization-sensitive arrays is established. Then, DOA is estimated by a two-stage source localization strategy. The first stage achieves the interval estimation of the target by constructing an interval overcomplete dictionary matrix, while the second stage performs fine estimation within the identified interval. This coarse-to-fine estimation strategy significantly reduces computational complexity. Subsequently, the polarization parameters are obtained from the reconstructed coefficient matrix. Finally, we conducted experimental comparisons under different conditions to verify the advantages of the proposed method. The results show that the algorithm achieves a good balance between complexity and accuracy. Although it has lower complexity, this is not at the expense of accuracy.</p>