Grid Evolution Method for DOA Estimation

Wang, Qianli; Zhao, Zhiqin; Chen, Zhuming; Nie, Zaiping

doi:10.1109/tsp.2018.2814998

Cited by 48 publications

(27 citation statements)

References 34 publications

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“…The grid fission process is designed to improve resolution, at the same time maintaining high efficiency. The idea of grid fission is first proposed in [33]. However, the grid fission process in this paper is different from that of [33].…”

Section: Grid Fissionmentioning

confidence: 98%

“…For the former, a linear approximation is usually used to model the off-grid problem [30]. Dynamic grid methods [32,33,39] were proposed in which the grid is considered as the adjustable parameters. For this kind of methods, the computational complexity is significantly reduced, and the modeling error can be almost eliminated.…”

Section: Data Modelmentioning

confidence: 99%

“…The idea of grid fission is first proposed in [33]. However, the grid fission process in this paper is different from that of [33]. The grid point corresponding to the largest element of P, supposed asθ p , is selected as the one to get fission.…”

Section: Grid Fissionmentioning

confidence: 99%

“…The estimated spectrum of GRDOA is shown and compared with RSBL firstly. Then, we compare GRDOA with l 1 -SVD [26], OGSBI [30], RSBL [32], and GEDOA [33] on accuracy and computational complexity. All the numerical simulations were carried out by MATLAB on a PC with an Inter i3 − 7350k CPU and 8GB of RAM.…”

Section: Simulationmentioning

confidence: 99%

“…However, if more than one DOA exists in the same grid interval, RSBL may fail to discriminate these DOAs in the case of coarse grid. GEDOA [33] combines off-grid methods and grid refinement to make the grid nonuniformly evolve from coarse to dense, which can discriminate closely spaced DOAs as well as achieve higher efficiency than RSBL. However, its performance will get worse in the low SNR condition.…”

Section: Introductionmentioning

confidence: 99%

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Grid Reconfiguration Method for Off-Grid DOA Estimation

Ling

Gao

et al. 2019

Electronics

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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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Section: Grid Fissionmentioning

confidence: 98%