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NoncovANM: Gridless DOA Estimation for LPDF System

delete2023-12-01
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OA
AI
Y
Yangying Zhao
陈朋 封面图
陈朋 (Peng Chen) *
Z
Zhenxin Cao
X
Xianbin Wang
DOI:10.1109/TVT.2023.3293189delete
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摘要

摘要

En 中文
Direction of arrival (DOA) estimation is an important research in the area of array signal processing, and has been studied for decades. High resolution DOA estimation requires large array aperture, which leads to the increase of hardware cost. Besides, high accuracy DOA estimation methods usually have high computational complexity. In this article, the problem of decreasing the hardware cost and algorithm complexity is addressed. First, considering the ability of flexible controlling the electromagnetic waves and low-cost, an intelligent reconfigurable surface (IRS)-aided low-cost passive direction finding (LPDF) system is developed, where only one fully functional receiving channel is adopted. Then, the sparsity of targets direction in the spatial domain is exploited by formulating an atomic norm minimization (ANM) problem to estimate the DOA. Traditionally, solving ANM problem is complex and cannot be realized efficiently. Hence, a novel nonconvex-based ANM (NC-ANM) method is proposed by gradient threshold iteration, where a perturbation is introduced to avoid falling into saddle points. The theoretical analysis for the convergence of the NC-ANM method is also given. Moreover, the corresponding Cramer-Rao lower bound (CRLB) in the LPDF system is derived, and taken as the referred bound of the DOA estimation. Simulation results show that the proposed method outperforms the compared methods in the DOA estimation with lower computational complexity in the LPDF system.
Keyword:
Atomic norm minimization (ANM)
direction of arrival (DOA) estimation
intelligent reconfigurable surface (IRS)
low-cost passive direction finding (LPDF) system
non-convex

期刊

IEEE Transactions on Vehicular Technology 封面图
IEEE Transactions on Vehicular Technology
IF:
7.1
论文数:
1.8W
被引数:
6.6W

机构

W
western university (university of western ontario)
学者数:
2.9W
论文数: 2.7W
被引数: 33
S
southeast university - china
学者数:
5.3W
论文数: 4.9W
被引数: 57
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