Pseudo Noise Subspace based DOA Estimation for Unfolded Coprime Linear Arrays

To achieve a better tradeoff between the direction-of-arrival (DOA) estimation resolution and complexity compared to the existing sparse array based estimators, this letter proposes a pseudo noise subspace based DOA estimator for an unfolded coprime linear array (UCLA). On one hand, by unfolding the two subarrays of a general coprime linear array (CLA), the UCLA acquires a larger aperture, giving rise to a higher angular resolution. On the other hand, a mapping relationship is explored to convert the original noise subspace into the pseudo noise subspace (in the Nyquist spatial sampling sense), from which the DOAs can be accurately estimated by root-MUSIC method instead of exhaustive peak searching, thereby reducing the complexity. Numerical results also confirm this superiority over other sparse array based estimators.