期刊
IEEE SIGNAL PROCESSING LETTERS
卷 29, 期 -, 页码 1052-1056出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LSP.2022.3164850
关键词
Covariance matrices; Estimation; MIMO communication; Direction-of-arrival estimation; US Department of Defense; Redundancy; Manganese; DOD and DOA estimation; MIMO systems; redundancy reduction representation; transformation matrix construction
资金
- U.S. National Science Foundation (NSF) [1527396, 1939553, 2003211, 2128596, 2136202]
- NSF of China (NSFC) [61871218, 61801211]
- Fundamental Research Funds for the Central University [3082017NP2017421]
- Virginia Research Investment Fund CCI [223996]
- Open Research Fund of the Key Lab of Radar Imaging and Microwave Photonics [NJ20210001]
- ASPIRE Project under OTP program of NWO-TTW [14926]
- Direct For Computer & Info Scie & Enginr
- Division Of Computer and Network Systems [2003211] Funding Source: National Science Foundation
- Direct For Computer & Info Scie & Enginr
- Division of Computing and Communication Foundations [1527396, 1939553, 2136202] Funding Source: National Science Foundation
- Directorate For Engineering
- Div Of Electrical, Commun & Cyber Sys [2128596] Funding Source: National Science Foundation
This paper proposes an efficient method for estimating DOD and DOA in MIMO systems. By reducing redundancy, the covariance matrix is transformed into a smaller one without losing useful angle information. This method achieves efficient estimation on a reduced-size problem.
This paper proposes an efficient direction of departure (DOD) and direction of arrival (DOA) estimation method for multi-input multi-output (MIMO) systems. For uncorrelated scenarios, the redundancy of the covariance matrix is first exploited by establishing its concise representation through redundancy reduction, which transforms the original large-size covariance matrix into a smaller-size matrix without loss of useful angle information. Then, the resulting transformed matrix, which retains a salient structure, permits efficient two-dimensional (2D) angle estimators working on a reduced-size problem for DOD and DOA estimation. Compared with conventional subspace-based methods, the proposed method incorporating an appropriate 2D angle estimator is more computationally efficient and can achieve higher estimation accuracy for small numbers of snapshots and low signal-to-noise ratios, which are verified by simulation results.
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