GRIDLESS TWO-DIMENSIONAL DOA ESTIMATION WITH L-SHAPED ARRAY BASED ON THE CROSS-COVARIANCE MATRIX

The atomic norm minimization (ANM) has been successfully incorporated into the two-dimensional (2-D) direction-ofarrival (DOA) estimation problem for super-resolution. However, its computational workload might be unaffordable when the number of snapshots is large. In this paper, we propose two gridless methods for 2-D DOA estimation with L-shaped array based on the atomic norm to improve the computational ef fi ciency. Firstly, by exploiting the cross-covariance matrix an ANM-based model has been proposed. We then prove that this model can be ef fi ciently solved as a semi-de fi nite programming (SDP). Secondly, a modi fi ed model has been presented to improve the estimation accuracy. It is shown that our proposed methods can be applied to both uniform and sparse L-shaped arrays and do not require any knowledge of the number of sources. Furthermore, since our methods greatly reduce the model size as compared to the conventional ANM method, and thus are much more ef fi cient. Simulations results are provided to demonstrate the advantage of our methods.