LAUSR

In this letter, a dual formulation of atomic norm minimization (ANM) approach is proposed by exploiting the vectorized covariance data of the signals received by the extended virtual array. Compared with the traditional ANM-based gridless direction of arrival (DOA) estimation algorithms for sparse linear array (SLA), the proposed algorithm not only realizes the parameter continuity and data completion, but also reduces the size of optimization model and the influence of noise on the estimation accuracy, which can significantly improve the performance of the algorithm. In addition, after establishing an ANM model based on complete vectorized covariance data, the corresponding dual problem is formulated to make the subsequent DOA recovery process no longer need the number of sources as a priori information. Numerial simulations demonstrate the superiority of the proposed algorithm over state of the art gridless DOA estimation algorithms.