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Improved MIMO-SAR Echo Separation Scheme with Constrained/Generalized LASSO Regression : New Insights and Applications

Wang, Yu ; Jin, Guodong ; Jiang, Penghui ; Jakobsson, Andreas LU orcid ; Shi, Tianyue ; Wang, Qinglu ; Zheng, Yangcheng ; Wu, Di LU and Zhu, Daiyin (2024) In IEEE Transactions on Geoscience and Remote Sensing 62.
Abstract

The separation of multiple transmit waveforms with time and frequency synchronization constitutes a considerable challenge for multiple-input multiple-output (MIMO) synthetic aperture radar (SAR) systems. It is well-known that aliased signal returns may be separable by digital beamforming (DBF) on receive in elevation. However, the current orthogonal-waveform beamforming schemes significantly increase the hardware complexity. Moreover, the direction of arrival (DOA) mismatch issue caused by topographical variations significantly increases the complexity of the DBF process. To alleviate these issues, we here introduce a multiple-subpulse separation and weighting synthesis (MSS-WS) echo separation framework, which is formed using... (More)

The separation of multiple transmit waveforms with time and frequency synchronization constitutes a considerable challenge for multiple-input multiple-output (MIMO) synthetic aperture radar (SAR) systems. It is well-known that aliased signal returns may be separable by digital beamforming (DBF) on receive in elevation. However, the current orthogonal-waveform beamforming schemes significantly increase the hardware complexity. Moreover, the direction of arrival (DOA) mismatch issue caused by topographical variations significantly increases the complexity of the DBF process. To alleviate these issues, we here introduce a multiple-subpulse separation and weighting synthesis (MSS-WS) echo separation framework, which is formed using segmented phase coding (SPC) waveforms. The proposed MSS-WS scheme can halve the number of interferences from far arrival angles, allowing for a reduction of the system complexity. In addition, constrained/generalized least absolute shrinkage and selection operator (LASSO) regression is exploited to form the beamformer with relatively high robustness in terms of dealing with the presence of topographical variations. The so-called LASSO-based dynamic beam response (LASSO-DBR) technique introduced here contains two parts: The source localization and the beamforming based on the designed constraint matrices. In this respect, the proposed LASSO-DBR beamformer can produce a distortionless response to the desired signal and still yield wide nulls for the unwanted interferences. Using numerical simulations, we illustrate the feasibility and performance of the proposed MSS-WS framework using the LASSO-DBR beamforming technique.

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author
; ; ; ; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Digital beamforming (DBF), echo separation, least absolute shrinkage and selection operator (LASSO), multiple-input multiple-output (MIMO) synthetic aperture radar (SAR), multiple-subpulse separation and weighting synthesis (MSS-WS) regression
in
IEEE Transactions on Geoscience and Remote Sensing
volume
62
article number
5223818
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
external identifiers
  • scopus:85204974020
ISSN
0196-2892
DOI
10.1109/TGRS.2024.3464541
language
English
LU publication?
yes
id
3458dc97-4a1c-4edd-b653-72983c3ac136
date added to LUP
2024-12-20 09:39:31
date last changed
2025-04-04 14:04:59
@article{3458dc97-4a1c-4edd-b653-72983c3ac136,
  abstract     = {{<p>The separation of multiple transmit waveforms with time and frequency synchronization constitutes a considerable challenge for multiple-input multiple-output (MIMO) synthetic aperture radar (SAR) systems. It is well-known that aliased signal returns may be separable by digital beamforming (DBF) on receive in elevation. However, the current orthogonal-waveform beamforming schemes significantly increase the hardware complexity. Moreover, the direction of arrival (DOA) mismatch issue caused by topographical variations significantly increases the complexity of the DBF process. To alleviate these issues, we here introduce a multiple-subpulse separation and weighting synthesis (MSS-WS) echo separation framework, which is formed using segmented phase coding (SPC) waveforms. The proposed MSS-WS scheme can halve the number of interferences from far arrival angles, allowing for a reduction of the system complexity. In addition, constrained/generalized least absolute shrinkage and selection operator (LASSO) regression is exploited to form the beamformer with relatively high robustness in terms of dealing with the presence of topographical variations. The so-called LASSO-based dynamic beam response (LASSO-DBR) technique introduced here contains two parts: The source localization and the beamforming based on the designed constraint matrices. In this respect, the proposed LASSO-DBR beamformer can produce a distortionless response to the desired signal and still yield wide nulls for the unwanted interferences. Using numerical simulations, we illustrate the feasibility and performance of the proposed MSS-WS framework using the LASSO-DBR beamforming technique.</p>}},
  author       = {{Wang, Yu and Jin, Guodong and Jiang, Penghui and Jakobsson, Andreas and Shi, Tianyue and Wang, Qinglu and Zheng, Yangcheng and Wu, Di and Zhu, Daiyin}},
  issn         = {{0196-2892}},
  keywords     = {{Digital beamforming (DBF); echo separation; least absolute shrinkage and selection operator (LASSO); multiple-input multiple-output (MIMO) synthetic aperture radar (SAR); multiple-subpulse separation and weighting synthesis (MSS-WS) regression}},
  language     = {{eng}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  series       = {{IEEE Transactions on Geoscience and Remote Sensing}},
  title        = {{Improved MIMO-SAR Echo Separation Scheme with Constrained/Generalized LASSO Regression : New Insights and Applications}},
  url          = {{http://dx.doi.org/10.1109/TGRS.2024.3464541}},
  doi          = {{10.1109/TGRS.2024.3464541}},
  volume       = {{62}},
  year         = {{2024}},
}