Publisher Summary Nonuniform linear arrays (NLAs) have certain advantages and problems for directions of arrival (DOA) estimation. They cover a large array aperture with fewer sensors, and they require fewer matched channels or receivers. Their disadvantage is that coherent sources cannot be easily handled. Furthermore, they need additional computation to compensate for and augment the missing sensor information. The completion of missing sensor data is required to improve accuracy. Array mapping is an effective method of augmenting the NLA covariance matrix. Array-mapping accuracy can be improved significantly if an initial DOA estimate is used and then the estimates are improved iteratively. For this reason, initial DOA estimation is a key problem for NLA, but it can be easily solved for uncorrelated sources. Toeplitz completion can be used directly for this purpose. Initial DOA estimation for coherent sources is not an easy task. A promising approach for coherent signals is to use partly filled NLA. Initial DOA estimates can be obtained via forward-backward spatial smoothing for the ULA part of this array. Then, array mapping can generate a covariance matrix corresponding to a full array with the same aperture. Different array-mapping techniques exist in the literature. Classical array interpolation is well known, but it has certain limitations. Wiener array interpolation performs well, but it produces focusing loss for NLA. It performs better for circular arrays compared to alternative techniques where a large angular sector for array mapping is used.