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Online Sparse Reconstruction for Scanning Radar Using Beam-Updating q-SPICE

Zhang, Yongchao ; Li, Jie ; Li, Minghui ; Zhang, Yin ; Luo, Jiawei ; Huang, Yulin ; Yang, Jianyu and Jakobsson, Andreas LU orcid (2022) In IEEE Geoscience and Remote Sensing Letters 19.
Abstract

The generalized sparse iterative covariance-based estimation ( $q$ -SPICE) algorithm was recently introduced for scanning radar applications, resulting in substantial improvements in the angular resolution and quality of the processed images. Regrettably, the computational complexity and storage cost are high and quickly increase with growing data size, limiting the applicability of the estimator. In this letter, we strive to alleviate this problem, deriving a beam-updating $q$ -SPICE algorithm, allowing for efficiently updating of the sparse reconstruction result for each online radar measurement along the scanned beam. The resulting method is a regularized extension of the current online $q$ -SPICE implementation, which not only... (More)

The generalized sparse iterative covariance-based estimation ( $q$ -SPICE) algorithm was recently introduced for scanning radar applications, resulting in substantial improvements in the angular resolution and quality of the processed images. Regrettably, the computational complexity and storage cost are high and quickly increase with growing data size, limiting the applicability of the estimator. In this letter, we strive to alleviate this problem, deriving a beam-updating $q$ -SPICE algorithm, allowing for efficiently updating of the sparse reconstruction result for each online radar measurement along the scanned beam. The resulting method is a regularized extension of the current online $q$ -SPICE implementation, which not only offers constant computational and storage cost, independent of the data size, but also provides enhanced robustness over the current online $q$ -SPICE. Our experimental assessment, conducted using both simulated and real data, demonstrates the advantage of the beam-updating $q$ -SPICE method in the task of sparse reconstruction for scanning radar.

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author
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organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Batch processing, beam-updating q-SPICE, online sparse reconstruction, scanning radar, sparse iterative covariance-based estimation (SPICE)
in
IEEE Geoscience and Remote Sensing Letters
volume
19
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
external identifiers
  • scopus:85101741058
ISSN
1545-598X
DOI
10.1109/LGRS.2021.3058404
language
English
LU publication?
yes
id
c1115316-26be-4345-95cd-f97bf301244a
date added to LUP
2022-02-28 17:12:53
date last changed
2025-04-04 14:09:17
@article{c1115316-26be-4345-95cd-f97bf301244a,
  abstract     = {{<p>The generalized sparse iterative covariance-based estimation ( $q$ -SPICE) algorithm was recently introduced for scanning radar applications, resulting in substantial improvements in the angular resolution and quality of the processed images. Regrettably, the computational complexity and storage cost are high and quickly increase with growing data size, limiting the applicability of the estimator. In this letter, we strive to alleviate this problem, deriving a beam-updating $q$ -SPICE algorithm, allowing for efficiently updating of the sparse reconstruction result for each online radar measurement along the scanned beam. The resulting method is a regularized extension of the current online $q$ -SPICE implementation, which not only offers constant computational and storage cost, independent of the data size, but also provides enhanced robustness over the current online $q$ -SPICE. Our experimental assessment, conducted using both simulated and real data, demonstrates the advantage of the beam-updating $q$ -SPICE method in the task of sparse reconstruction for scanning radar. </p>}},
  author       = {{Zhang, Yongchao and Li, Jie and Li, Minghui and Zhang, Yin and Luo, Jiawei and Huang, Yulin and Yang, Jianyu and Jakobsson, Andreas}},
  issn         = {{1545-598X}},
  keywords     = {{Batch processing; beam-updating q-SPICE; online sparse reconstruction; scanning radar; sparse iterative covariance-based estimation (SPICE)}},
  language     = {{eng}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  series       = {{IEEE Geoscience and Remote Sensing Letters}},
  title        = {{Online Sparse Reconstruction for Scanning Radar Using Beam-Updating q-SPICE}},
  url          = {{http://dx.doi.org/10.1109/LGRS.2021.3058404}},
  doi          = {{10.1109/LGRS.2021.3058404}},
  volume       = {{19}},
  year         = {{2022}},
}