Efficient DOA Estimation Based on Coprime Array Interpolation With Deep Unfolding Network

Coprime arrays increase the degrees of freedom for direction of arrival (DOA) estimation, but virtual-array gaps require costly filling procedures. In this letter, a new DOA estimation method is proposed for coprime arrays based on interpolation and a deep unfolding network. The reconstruction of the interpolated virtual array covariance matrix is formulated as a rank minimization problem and solved using an ADMM-based deep unfolding network with stage-wise learnable parameters and an unsupervised loss inspired by ADMM convergence criteria. Finally, root-MUSIC is employed for DOA estimation. Simulations demonstrate the effectiveness of the proposed method in terms of both computational efficiency and estimation performance.