Online Sparse DOA Estimation Based on Sub–Aperture Recursive LASSO for TDM–MIMO Radar
Luo, Jiawei ; Zhang, Yongwei ; Yang, Jianyu ; Zhang, Donghui ; Zhang, Yongchao ; Zhang, Yin ; Huang, Yulin and Jakobsson, Andreas LU
(2022)
In Remote Sensing
14(9).
- Abstract
The least absolute shrinkage and selection operator (LASSO) algorithm is a promising method for sparse source location in time–division multiplexing (TDM) multiple–input, multiple– output (MIMO) radar systems, with notable performance gains in regard to resolution enhancement and side lobe suppression. However, the current batch LASSO algorithm suffers from high– computational complexity when dealing with massive TDM–MIMO observations, due to high– dimensional matrix operations and the large number of iterations. In this paper, an online LASSO method is proposed for efficient direction–of–arrival (DOA) estimation of the TDM–MIMO radar based on the receiving features of the sub–aperture data blocks. This method recursively refines the... (More)
The least absolute shrinkage and selection operator (LASSO) algorithm is a promising method for sparse source location in time–division multiplexing (TDM) multiple–input, multiple– output (MIMO) radar systems, with notable performance gains in regard to resolution enhancement and side lobe suppression. However, the current batch LASSO algorithm suffers from high– computational complexity when dealing with massive TDM–MIMO observations, due to high– dimensional matrix operations and the large number of iterations. In this paper, an online LASSO method is proposed for efficient direction–of–arrival (DOA) estimation of the TDM–MIMO radar based on the receiving features of the sub–aperture data blocks. This method recursively refines the location parameters for each receive (RX) block observation that becomes available sequentially in time. Compared with the conventional batch LASSO method, the proposed online DOA method makes full use of the TDM–MIMO reception time to improve the real–time performance. Additionally, it allows for much less iterations, avoiding high–dimensional matrix operations, allowing the computational complexity to be reduced from O( K3) to O( K2). Simulated and real–data results demonstrate the superiority and effectiveness of the proposed method.
(Less)
- author
- Luo, Jiawei
; Zhang, Yongwei
; Yang, Jianyu
; Zhang, Donghui
; Zhang, Yongchao
; Zhang, Yin
; Huang, Yulin
and Jakobsson, Andreas
LU
- organization
- publishing date
- 2022-05-01
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- DOA, LASSO, online, TDM–MIMO
- in
- Remote Sensing
- volume
- 14
- issue
- 9
- article number
- 2133
- publisher
- MDPI AG
- external identifiers
-
- scopus:85129899497
- ISSN
- 2072-4292
- DOI
- 10.3390/rs14092133
- language
- English
- LU publication?
- yes
- additional info
- Publisher Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
- id
- f5cd4d27-8f45-4bfc-a0cd-f698cd565e74
- date added to LUP
- 2022-08-19 12:48:20
- date last changed
- 2023-11-21 02:18:08
@article{f5cd4d27-8f45-4bfc-a0cd-f698cd565e74,
abstract = {{<p>The least absolute shrinkage and selection operator (LASSO) algorithm is a promising method for sparse source location in time–division multiplexing (TDM) multiple–input, multiple– output (MIMO) radar systems, with notable performance gains in regard to resolution enhancement and side lobe suppression. However, the current batch LASSO algorithm suffers from high– computational complexity when dealing with massive TDM–MIMO observations, due to high– dimensional matrix operations and the large number of iterations. In this paper, an online LASSO method is proposed for efficient direction–of–arrival (DOA) estimation of the TDM–MIMO radar based on the receiving features of the sub–aperture data blocks. This method recursively refines the location parameters for each receive (RX) block observation that becomes available sequentially in time. Compared with the conventional batch LASSO method, the proposed online DOA method makes full use of the TDM–MIMO reception time to improve the real–time performance. Additionally, it allows for much less iterations, avoiding high–dimensional matrix operations, allowing the computational complexity to be reduced from O<sup>(</sup> K<sup>3)</sup> to O<sup>(</sup> K<sup>2)</sup>. Simulated and real–data results demonstrate the superiority and effectiveness of the proposed method.</p>}},
author = {{Luo, Jiawei and Zhang, Yongwei and Yang, Jianyu and Zhang, Donghui and Zhang, Yongchao and Zhang, Yin and Huang, Yulin and Jakobsson, Andreas}},
issn = {{2072-4292}},
keywords = {{DOA; LASSO; online; TDM–MIMO}},
language = {{eng}},
month = {{05}},
number = {{9}},
publisher = {{MDPI AG}},
series = {{Remote Sensing}},
title = {{Online Sparse DOA Estimation Based on Sub–Aperture Recursive LASSO for TDM–MIMO Radar}},
url = {{http://dx.doi.org/10.3390/rs14092133}},
doi = {{10.3390/rs14092133}},
volume = {{14}},
year = {{2022}},
}