Direction-of-Arrival Estimation With ULA: A Spatial Annihilating Filter Reconstruction Perspective

Direction-of-arrival (DOA) estimation under low signal-to-noise ratio (SNR) and snapshot deficient scenarios has practical application value. Since the array output of the uniform linear array (ULA) can be viewed as a linear combination of the equal interval sampling complex exponentials, it can be...

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Bibliographic Details
Published in:IEEE access 2018-01, Vol.6, p.23172-23179
Main Authors: Pan, Yujian, Luo, Guo Qing, Jin, Huayan, Cao, Wenhui
Format: Article
Language:English
Subjects:
annihilating filter
Convolution
Direction of arrival
Direction-of-arrival (DOA) estimation
Direction-of-arrival estimation
Fast Fourier transformations
Fourier transforms
Linear arrays
low SNR and snapshot deficient scenarios
Maximum likelihood estimation
Maximum likelihood estimators
Signal resolution
Signal to noise ratio
Spatial resolution
structured total least norm (STLN)
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Summary:Direction-of-arrival (DOA) estimation under low signal-to-noise ratio (SNR) and snapshot deficient scenarios has practical application value. Since the array output of the uniform linear array (ULA) can be viewed as a linear combination of the equal interval sampling complex exponentials, it can be annihilated by a spatial annihilating filter and the DOAs are able to be obtained from the filter coefficients. To reconstruct the spatial annihilating filter, a multiple measurement vectors structured total least norm approach is used under the structured total least squares framework, along with a spatial fast Fourier transform based initialization method. It is proved that the new method is equivalent to the deterministic maximum likelihood estimator of DOA. It can resolve up to M-1 sources using M -element ULA and is capable of both incoherent and coherent signals. Numerical results show that the proposed method surpasses the representative methods largely in resolution and estimation performance under the low SNR, snapshot deficient, and closely spaced signals scenarios.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2018.2828799