Gridless Two-dimensional DOA Estimation With L-shaped Array Based on the Cross-covariance Matrix

摘要

The atomic norm minimization (ANM) has been successfully incorporated intothe two-dimensional (2-D) direction-of-arrival (DOA) estimation problem forsuper-resolution. However, its computational workload might be unaffordablewhen the number of snapshots is large. In this paper, we propose two gridlessmethods for 2-D DOA estimation with L-shaped array based on the atomic norm toimprove the computational efficiency. Firstly, by exploiting thecross-covariance matrix an ANM-based model has been proposed. We then provethat this model can be efficiently solved as a semi-definite programming (SDP).Secondly, a modified model has been presented to improve the estimationaccuracy. It is shown that our proposed methods can be applied to both uniformand sparse L-shaped arrays and do not require any knowledge of the number ofsources. Furthermore, since our methods greatly reduce the model size ascompared to the conventional ANM method, and thus are much more efficient.Simulations results are provided to demonstrate the advantage of our methods.