691 research outputs found
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-of-arrival (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 efficiency. Firstly, by exploiting the
cross-covariance matrix an ANM-based model has been proposed. We then prove
that this model can be efficiently solved as a semi-definite programming (SDP).
Secondly, a modified 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 efficient.
Simulations results are provided to demonstrate the advantage of our methods
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