Joint DOD and DOA Estimation for NLOS Target Using IRS-Aided Bistatic MIMO Radar

作者：Fangqing Wen, Junpeng Shi, Guan Gui, Chau Yuen, Hikmet Sari, Fumiyuki Adachi · 发表于：IEEE Transactions on Vehicular Technology · 年份：2024 · DOI：10.1109/tvt.2024.3408813 · 被引用次数：77 · 研究领域：Computer Science

Intelligent Reflecting Surface (IRS) offers new insight into Multiple-Input Multiple-Output (MIMO) radar systems, since it enables a MIMO radar to positioning targets from Non-Line-of-Sight (NLOS) directions. This paper investigates the Direction-of-departure (DOD) and Direction-of-Arrival (DOA) estimation in a bistatic MIMO radar, in which a backward IRS is exploited to receive the echoes reflected by the targets from NLOS viewpoint. A Reduced-Dimension Multiple Signal Classification (MUSIC) estimator is developed. Compared with the state-of-the-art MUSIC and iteratively approximation algorithm, the proposed method RD-MUSIC algorithm is computationally much more efficient. Theoretical analyses are given and numerical results corroborate our analysis.