Super-resolution Direction of Arrival Estimation Using a Minimum Mean-Square Error Framework
Wu, Yanan LU ; Jakobsson, Andreas LU
and Liu, Lutao
(2023)
In Signal Processing
212.
- Abstract
This paper develops a novel sparse direction-of-arrival (DOA) estimation technique that avoids the common requirement of hyperparameters, which are typically difficult to set suitably in practice. Using the presented minimum mean-square error (MMSE) estimation framework, we propose a computationally efficient super-resolution DOA estimator that is implemented using an alternate updating of the spatial power distribution of the signals and of the dictionary matrix using a ridge regression algorithm. The regularization parameter determining the sparsity of the solution is formed from the previous spatial power distribution estimates using a SPICE-based criteria. The method employs an adaptive gridding strategy to avoid the grid mismatch... (More)
This paper develops a novel sparse direction-of-arrival (DOA) estimation technique that avoids the common requirement of hyperparameters, which are typically difficult to set suitably in practice. Using the presented minimum mean-square error (MMSE) estimation framework, we propose a computationally efficient super-resolution DOA estimator that is implemented using an alternate updating of the spatial power distribution of the signals and of the dictionary matrix using a ridge regression algorithm. The regularization parameter determining the sparsity of the solution is formed from the previous spatial power distribution estimates using a SPICE-based criteria. The method employs an adaptive gridding strategy to avoid the grid mismatch problem. The computational complexity is further reduced by the use of an integrated wideband dictionary, determing the active dictionary in an iterative manner. The method offers high resolution estimates of closely spaced sources even for very low sample support (single snapshot) without assuming prior knowledge of the number of sources. Our evaluations illustrate the preferable performance of the proposed estimator as compared to various state-of-the art estimators, also indicating that the method's performance approaches the Cramér-Rao lower bound (CRB) for the examined problem as the signal-to-noise ratio (SNR) increases.
(Less)
- author
- Wu, Yanan
LU
; Jakobsson, Andreas
LU
and Liu, Lutao
- organization
- publishing date
- 2023
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Adaptive gridding strategy, Direction-of-arrival (DOA) estimation, Minimum mean-square error (MMSE) estimation framework, SPICE, Super-resolution, Wideband dictionary
- in
- Signal Processing
- volume
- 212
- article number
- 109164
- publisher
- Elsevier
- external identifiers
-
- scopus:85164245410
- ISSN
- 0165-1684
- DOI
- 10.1016/j.sigpro.2023.109164
- language
- English
- LU publication?
- yes
- id
- 16bce401-a592-43d6-94c5-d51b37680101
- date added to LUP
- 2023-08-28 15:40:16
- date last changed
- 2023-11-22 22:42:12
@article{16bce401-a592-43d6-94c5-d51b37680101,
abstract = {{<p>This paper develops a novel sparse direction-of-arrival (DOA) estimation technique that avoids the common requirement of hyperparameters, which are typically difficult to set suitably in practice. Using the presented minimum mean-square error (MMSE) estimation framework, we propose a computationally efficient super-resolution DOA estimator that is implemented using an alternate updating of the spatial power distribution of the signals and of the dictionary matrix using a ridge regression algorithm. The regularization parameter determining the sparsity of the solution is formed from the previous spatial power distribution estimates using a SPICE-based criteria. The method employs an adaptive gridding strategy to avoid the grid mismatch problem. The computational complexity is further reduced by the use of an integrated wideband dictionary, determing the active dictionary in an iterative manner. The method offers high resolution estimates of closely spaced sources even for very low sample support (single snapshot) without assuming prior knowledge of the number of sources. Our evaluations illustrate the preferable performance of the proposed estimator as compared to various state-of-the art estimators, also indicating that the method's performance approaches the Cramér-Rao lower bound (CRB) for the examined problem as the signal-to-noise ratio (SNR) increases.</p>}},
author = {{Wu, Yanan and Jakobsson, Andreas and Liu, Lutao}},
issn = {{0165-1684}},
keywords = {{Adaptive gridding strategy; Direction-of-arrival (DOA) estimation; Minimum mean-square error (MMSE) estimation framework; SPICE; Super-resolution; Wideband dictionary}},
language = {{eng}},
publisher = {{Elsevier}},
series = {{Signal Processing}},
title = {{Super-resolution Direction of Arrival Estimation Using a Minimum Mean-Square Error Framework}},
url = {{http://dx.doi.org/10.1016/j.sigpro.2023.109164}},
doi = {{10.1016/j.sigpro.2023.109164}},
volume = {{212}},
year = {{2023}},
}