Gridless Two-Dimensional Doa Estimation With L-Shaped Array Based on the Cross-Covariance Matrix

Xiaohuan Wu; Wei-Ping Zhu; Jun Yan

ICASSP 2018accepted0 citations

Gridless Two-Dimensional Doa Estimation With L-Shaped Array Based on the Cross-Covariance Matrix

Abstract

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.

BibTeX

@inproceedings{icassp2018_gridlesstwodimen,
  title = {Gridless Two-Dimensional Doa Estimation With L-Shaped Array Based on the Cross-Covariance Matrix},
  author = {Xiaohuan Wu and Wei-Ping Zhu and Jun Yan},
  booktitle = {ICASSP 2018},
  year = {2018}
}