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An Efficient Sparse Synthetic Aperture Radar Imaging Method Based on L1-Norm and Total Variation Regularization

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Publicado en:Electronics vol. 14, no. 13 (2025), p. 2508-2530
Autor principal: Gao Zhiqi
Otros Autores: Ma, Huiying, Huang, Pingping, Xu, Wei, Tan Weixian, Wu, Zhixia
Publicado:
MDPI AG
Materias:
Sparsity
Regularization
Dictionaries
Accuracy
Data acquisition
Image reconstruction
Fourier transforms
Iterative methods
Decoupling
Synthetic aperture radar
Signal processing
Data sampling
Regularization methods
Algorithms
Clutter
Radar imaging
Real time
Radar
Efficiency
Acceso en línea:Citation/Abstract
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Resumen:The continuous progress of synthetic aperture radar (SAR) imaging has led to a growing emphasis on the challenges involved in data acquisition and processing. And the challenges in data acquisition and processing have become increasingly prominent. However, traditional SAR imaging models are limited by their large demand for data sampling and slow image reconstruction speeds, which is particularly prominent in large-scale scene applications. To overcome these limitations, this study proposes an innovative L1-Total Variation (TV) regularization sparse SAR imaging algorithm. The submitted algorithm constructs an imaging operator and an echo simulation operator to achieve decoupling in the azimuth and range dimensions, respectively, as well as to reduce the requirement for sampling data. In addition, a Newton acceleration iterative method is introduced to the optimization process, aiming to accelerate the speed of image reconstruction. Comparative analysis and experimental validation indicate that the proposed sparse SAR imaging algorithm outperforms conventional methods in resolution, target localization, and clutter suppression. The results suggest strong potential for rapid scene reconstruction and real-time monitoring in complex environments.
ISSN:2079-9292
DOI:10.3390/electronics14132508
Fuente:Advanced Technologies & Aerospace Database