  The problem of direction of arrival (DOA) estimation has been studied for
decades as an essential technology in enabling radar, wireless communications,
and array signal processing related applications. In this paper, the DOA
estimation problem in the scenario with gain-phase errors is considered, and a
sparse model is formulated by exploiting the signal sparsity in the spatial
domain. By proposing a new atomic norm, named as GP-ANM, an optimization method
is formulated via deriving a dual norm of GP-ANM. Then, the corresponding
semidefinite program (SDP) is given to estimate the DOA efficiently, where the
SDP is obtained based on the Schur complement. Moreover, a regularization
parameter is obtained theoretically in the convex optimization problem.
Simulation results show that the proposed method outperforms the existing
methods, including the subspace-based and sparse-based methods in the scenario
with gain-phase errors.
