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Weighted incoherent signal subspace method for DOA estimation on wideband colored signals

Weighted incoherent signal subspace method for DOA estimation on wideband colored signals
Weighted incoherent signal subspace method for DOA estimation on wideband colored signals
Wideband direction-of-arrival (DOA) estimation is a key part in array signal processing. Existing algorithms for the wideband DOA estimation are often studied in the situation of uniformly distributed energy. And all the frequency bins are weighted equally in these algorithms. However, these algorithms perform unsatisfactorily when encountering wideband colored signals with nonuniform energy spectrum. To improve the performance of DOA estimation for wideband colored signals, we proposed two weighting methods, which are based on the perturbed subspace theory and random matrix theory respectively. The two methods weight the space spectrum from all the frequency bins according to the mean square error (MSE) of DOA estimation in each frequency bin. Numerical results show that the random matrix theory based method performs well, due to the inference premise that the dimensions of matrices
increase at the same rate. The perturbed subspace based method, which is concise in calculating the weights, shows high accuracy only at high signal to noise ratio (SNR) and with adequate snapshots. The effectiveness of the two algorithms are also demonstrated by comparing them to various existing algorithms and the Cramér-Rao bound.
Direction-of-arrival estimation, random matrix theory, signal subspace method, wideband signal
2169-3536
1-10
Bai, Yechao
1036da17-2a37-423d-8a3e-3dd7de64b1a9
Li, Jianghui
9c589194-00fa-4d42-abaf-53a32789cc5e
Wu, Yu
647d5b21-ecd7-4d58-a848-0240c4f818ad
Wang, Qiong
48823dfd-1776-4346-8722-e1fda08ee077
Zhang, Xinggan
2c82d0d2-3d39-4608-9547-b0397afa0ee5
Bai, Yechao
1036da17-2a37-423d-8a3e-3dd7de64b1a9
Li, Jianghui
9c589194-00fa-4d42-abaf-53a32789cc5e
Wu, Yu
647d5b21-ecd7-4d58-a848-0240c4f818ad
Wang, Qiong
48823dfd-1776-4346-8722-e1fda08ee077
Zhang, Xinggan
2c82d0d2-3d39-4608-9547-b0397afa0ee5

Bai, Yechao, Li, Jianghui, Wu, Yu, Wang, Qiong and Zhang, Xinggan (2018) Weighted incoherent signal subspace method for DOA estimation on wideband colored signals. IEEE Access, 1-10. (doi:10.1109/ACCESS.2018.2886250).

Record type: Article

Abstract

Wideband direction-of-arrival (DOA) estimation is a key part in array signal processing. Existing algorithms for the wideband DOA estimation are often studied in the situation of uniformly distributed energy. And all the frequency bins are weighted equally in these algorithms. However, these algorithms perform unsatisfactorily when encountering wideband colored signals with nonuniform energy spectrum. To improve the performance of DOA estimation for wideband colored signals, we proposed two weighting methods, which are based on the perturbed subspace theory and random matrix theory respectively. The two methods weight the space spectrum from all the frequency bins according to the mean square error (MSE) of DOA estimation in each frequency bin. Numerical results show that the random matrix theory based method performs well, due to the inference premise that the dimensions of matrices
increase at the same rate. The perturbed subspace based method, which is concise in calculating the weights, shows high accuracy only at high signal to noise ratio (SNR) and with adequate snapshots. The effectiveness of the two algorithms are also demonstrated by comparing them to various existing algorithms and the Cramér-Rao bound.

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Weighted Incoherent Signal Subspace Method for DOA Estimation on Wideband Colored Signals - Accepted Manuscript
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Accepted/In Press date: 6 December 2018
e-pub ahead of print date: 12 December 2018
Published date: 14 December 2018
Keywords: Direction-of-arrival estimation, random matrix theory, signal subspace method, wideband signal

Identifiers

Local EPrints ID: 426662
URI: https://eprints.soton.ac.uk/id/eprint/426662
ISSN: 2169-3536
PURE UUID: e5bfa60d-b965-4527-8a46-ccabcdc3795c

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Date deposited: 10 Dec 2018 17:30
Last modified: 14 Mar 2019 05:01

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