期刊
IEEE SIGNAL PROCESSING LETTERS
卷 24, 期 5, 页码 703-706出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LSP.2017.2690601
关键词
Coarrays; maximum-likelihood direction-of-arrival (DOA) estimation; nested and coprime arrays; nuclear norm minimization; superresolution
资金
- NSF [1544798]
- University of California, San Diego
- Direct For Computer & Info Scie & Enginr
- Division Of Computer and Network Systems [1544798] Funding Source: National Science Foundation
- Division Of Computer and Network Systems
- Direct For Computer & Info Scie & Enginr [1702394] Funding Source: National Science Foundation
This letter offers several new insights into the maximum-likelihood direction-of-arrival (DOA) estimation problem, when the number of sources exceeds the number of sensors. Twomaximum-likelihood problems are studied: one for estimating the Toeplitz-structured coarray covariance matrix from the measurements, and the other for estimating the DOAs directly from the measurements. We establish the equivalence of both problems when the number of sources is assumed to be unknown and can potentially exceed the number of sensors. Additionally, it is shown that when the source waveforms satisfy certain orthogonality conditions, the Toeplitz-constrained maximum-likelihood covariance estimation framework provably produces the true DOAs without requiring to know the number of sources. When the number of sources exceeds the number of sensors, the maximum-likelihood algorithms studied in this letter outperform other recently studied methods, as demonstrated through numerical experiments.
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