Efficient Near-Field Millimeter Wave Imaging Based on Spatio-Temporal Adaptive Synergistic Constraint.
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| Title: | Efficient Near-Field Millimeter Wave Imaging Based on Spatio-Temporal Adaptive Synergistic Constraint. |
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| Authors: | Wang, Jingjing1,2 (AUTHOR), Sun, Rongbo1,2 (AUTHOR), Duan, Haowei1 (AUTHOR), Chen, Hao1,2 (AUTHOR), Yu, Gang1 (AUTHOR), Xu, Huaqiang1 (AUTHOR) xuhq@sdnu.edu.cn |
| Source: | Remote Sensing. Jun2026, Vol. 18 Issue 11, p1846. 24p. |
| Subjects: | Synthetic aperture radar, Compressed sensing, Mathematical regularization, Optimization algorithms, Signal detection, Regularization parameter, Submillimeter wave imaging, Image processing |
| Abstract: | Highlights: What are the main findings? A spatio-temporal adaptive synergistic constraint imaging algorithm is proposed for near-field millimeter-wave SAR, which realizes a dynamic balance between sparse and low-rank priors. The RSVD-accelerated ADMM framework is adopted, and the non-convex Geman-McClure constraint is introduced to enhance weak target and detail preservation. What is the implication of the main finding? The proposed method effectively alleviates the inherent zero-sum trade-off between artifact suppression and detail retention in traditional SAR imaging methods. It provides a feasible high-efficiency imaging scheme for low-sampling millimeter-wave SAR in practical applications such as security inspection and target detection. Compressed sensing (CS) and matrix completion algorithms (MCA) have each introduced sparse and low-rank priors into synthetic aperture radar (SAR) imaging. However, their combined use reveals a fundamental zero-sum trade-off: enhancing spatial continuity tends to obscure weak targets, while strengthening sparse recovery amplifies off-grid artifacts. This inherent conflict is further exacerbated by static regularization, which imposes a rigid global compromise and prevents genuine synergy between the two priors. To overcome this limitation, this paper proposes a Spatio-Temporal Adaptive Synergistic Constraint Imaging (STASCI) algorithm, which dynamically balances the two priors in a scene-aware manner. The core of STASCI is a unified regularization framework. The low-rank constraint models' spatial continuity in the background to suppress off-grid artifacts. The sparse constraint, enhanced by a non-convex Geman-McClure function, is employed to detect weak targets and compensate for detail loss. A key innovation is a spatio-temporal dual-dimensional regularization mechanism that employs Sobel operators to probe local spatial gradients and dynamically adjusts the strength of each prior according to regional scene characteristics. This enables adaptive synergy rather than a fixed trade-off. The optimization is solved via the alternating direction method of multipliers (ADMM), with the low-rank subproblem accelerated by randomized singular value decomposition (RSVD). Final imaging is performed using the Range Migration Algorithm (RMA). Experiments on real measurements and public datasets demonstrate that STASCI breaks the conventional detail-background trade-off. It effectively suppresses off-grid artifacts while retaining weak targets, leading to significant improvements in imaging accuracy and robustness across complex scenarios. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? A spatio-temporal adaptive synergistic constraint imaging algorithm is proposed for near-field millimeter-wave SAR, which realizes a dynamic balance between sparse and low-rank priors. The RSVD-accelerated ADMM framework is adopted, and the non-convex Geman-McClure constraint is introduced to enhance weak target and detail preservation. What is the implication of the main finding? The proposed method effectively alleviates the inherent zero-sum trade-off between artifact suppression and detail retention in traditional SAR imaging methods. It provides a feasible high-efficiency imaging scheme for low-sampling millimeter-wave SAR in practical applications such as security inspection and target detection. Compressed sensing (CS) and matrix completion algorithms (MCA) have each introduced sparse and low-rank priors into synthetic aperture radar (SAR) imaging. However, their combined use reveals a fundamental zero-sum trade-off: enhancing spatial continuity tends to obscure weak targets, while strengthening sparse recovery amplifies off-grid artifacts. This inherent conflict is further exacerbated by static regularization, which imposes a rigid global compromise and prevents genuine synergy between the two priors. To overcome this limitation, this paper proposes a Spatio-Temporal Adaptive Synergistic Constraint Imaging (STASCI) algorithm, which dynamically balances the two priors in a scene-aware manner. The core of STASCI is a unified regularization framework. The low-rank constraint models' spatial continuity in the background to suppress off-grid artifacts. The sparse constraint, enhanced by a non-convex Geman-McClure function, is employed to detect weak targets and compensate for detail loss. A key innovation is a spatio-temporal dual-dimensional regularization mechanism that employs Sobel operators to probe local spatial gradients and dynamically adjusts the strength of each prior according to regional scene characteristics. This enables adaptive synergy rather than a fixed trade-off. The optimization is solved via the alternating direction method of multipliers (ADMM), with the low-rank subproblem accelerated by randomized singular value decomposition (RSVD). Final imaging is performed using the Range Migration Algorithm (RMA). Experiments on real measurements and public datasets demonstrate that STASCI breaks the conventional detail-background trade-off. It effectively suppresses off-grid artifacts while retaining weak targets, leading to significant improvements in imaging accuracy and robustness across complex scenarios. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18111846 |
