A new approach is proposed for DOA estimation in nonuniform linear arrays (NLA) based on array interpolation. A Wiener formulation is presented to improve the condition number of the mapping matrix as well as the performance for noisy observations. Noniterative and iterative methods for DOA estimation are proposed. These methods use an initial DOA which is then significantly improved by the subsequent processing. Partially augmentable nonredundant arrays (PANA) and partly filled NLA (PFNLA) are considered and initial DOA is found in a different manner for each of these arrays. PANA are used for noncoherent sources whereas PFNLA are employed when the sources are coherent. Array interpolation is used to map the sample covariance matrix of the NLA to the covariance matrix of a uniform linear array (ULA) with the same aperture size and root-MUSIC is employed as a fast subspace algorithm for DOA estimation. Proposed methods overcome some of the limitations of the conventional array interpolation. DOA estimation problem is considered for correlated and coherent sources. Proposed approaches are compared with two previous methods as well as the spectral MUSIC and root-MUSIC algorithms applied on the NLA and ULA, respectively. It is shown that DOA performance is significantly improved for a wide variety of DOA scenarios.