Off-grid algorithms for direction of arrival (DOA) estimation have become attractive because of their advantages in resolution and efficiency over conventional ones. In this paper, we propose a grid reconfiguration direction of arrival (GRDOA) estimation method based on sparse Bayesian learning. Unlike other off-grid methods, the grid points of GRDOA are treated as dynamic parameters. The number and position of the grid points are varied iteratively via a root method and a fission process. Then, the grid gets reconfigured through some criteria. By iteratively updating the reconfigured grid, DOAs are estimated completely. Since GRDOA has fewer grid points, it has better computational efficiency than the previous methods. Moreover, GRDOA can achieve better resolution and relatively higher accuracy. Numerical simulation results validate the effectiveness of GRDOA.

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grdoa root method grid reconfiguration direction of arrival grdoa estimation method resolution ones computational efficiency points methods fission process algorithms sparse bayesian learning reconfigured offgrid

Yun Ling,Huotao Gao,Guobao Ru,Haitao Chen,Boya Li,Ting Cao,.Grid Reconfiguration Method for Off-Grid DOA Estimation. 8 (11),.

Yun Ling,Huotao Gao,Guobao Ru,Haitao Chen,Boya Li,Ting Cao,"Grid Reconfiguration Method for Off-Grid DOA Estimation" 8

Yun Ling,Huotao Gao,Guobao Ru,Haitao Chen,Boya Li,Ting Cao,"Grid Reconfiguration Method for Off-Grid DOA Estimation"