Estimating Weak DOA Signals Using Adaptive Grid Selection
Wu, Yanan ; Jakobsson, Andreas LU
and Liu, Lutao
(2023)
22nd IEEE Statistical Signal Processing Workshop, SSP 2023
- Abstract
In this work, we consider the problem of estimating the directions of arrival of far-field sources impinging on a sensor array using a computationally efficient approach. A novel adaptive grid selection technique is employed to reduce the dimensionality of the used dictionary matrix. The method further makes use of a SPICE-inspired dictionary to adaptively select an appropriate regularization parameter and to select the active grid set. An adaptive stepsize selection strategy is also introduced to reduce computation complexity further. The proposed method allows for accurate DOA estimates using even a single snapshot, also from weak sources. Numerical simulations indicate that the method offers preferable performance in comparison to... (More)
In this work, we consider the problem of estimating the directions of arrival of far-field sources impinging on a sensor array using a computationally efficient approach. A novel adaptive grid selection technique is employed to reduce the dimensionality of the used dictionary matrix. The method further makes use of a SPICE-inspired dictionary to adaptively select an appropriate regularization parameter and to select the active grid set. An adaptive stepsize selection strategy is also introduced to reduce computation complexity further. The proposed method allows for accurate DOA estimates using even a single snapshot, also from weak sources. Numerical simulations indicate that the method offers preferable performance in comparison to existing state-of-the-art DOA estimation algorithms.
(Less)
- author
- Wu, Yanan
; Jakobsson, Andreas
LU
and Liu, Lutao
- organization
- publishing date
- 2023
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- adaptive grid selecting technique, adaptive regularization, Direction-of-Arrival (DOA) estimation, sparse reconstruction
- host publication
- Proceedings of the 22nd IEEE Statistical Signal Processing Workshop, SSP 2023
- pages
- 5 pages
- publisher
- IEEE Computer Society
- conference name
- 22nd IEEE Statistical Signal Processing Workshop, SSP 2023
- conference location
- Hanoi, Viet Nam
- conference dates
- 2023-07-02 - 2023-07-05
- external identifiers
-
- scopus:85168881773
- ISBN
- 9781665452458
- DOI
- 10.1109/SSP53291.2023.10208076
- language
- English
- LU publication?
- yes
- id
- 82e217e2-ab14-4ed4-9660-afd3cbfffcbb
- date added to LUP
- 2023-11-10 13:52:05
- date last changed
- 2023-11-21 10:01:26
@inproceedings{82e217e2-ab14-4ed4-9660-afd3cbfffcbb,
abstract = {{<p>In this work, we consider the problem of estimating the directions of arrival of far-field sources impinging on a sensor array using a computationally efficient approach. A novel adaptive grid selection technique is employed to reduce the dimensionality of the used dictionary matrix. The method further makes use of a SPICE-inspired dictionary to adaptively select an appropriate regularization parameter and to select the active grid set. An adaptive stepsize selection strategy is also introduced to reduce computation complexity further. The proposed method allows for accurate DOA estimates using even a single snapshot, also from weak sources. Numerical simulations indicate that the method offers preferable performance in comparison to existing state-of-the-art DOA estimation algorithms.</p>}},
author = {{Wu, Yanan and Jakobsson, Andreas and Liu, Lutao}},
booktitle = {{Proceedings of the 22nd IEEE Statistical Signal Processing Workshop, SSP 2023}},
isbn = {{9781665452458}},
keywords = {{adaptive grid selecting technique; adaptive regularization; Direction-of-Arrival (DOA) estimation; sparse reconstruction}},
language = {{eng}},
publisher = {{IEEE Computer Society}},
title = {{Estimating Weak DOA Signals Using Adaptive Grid Selection}},
url = {{http://dx.doi.org/10.1109/SSP53291.2023.10208076}},
doi = {{10.1109/SSP53291.2023.10208076}},
year = {{2023}},
}