Work

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

Year: 2025

Type: article

Abstract: 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.

Source: Electronics

DOI: 10.3390/electronics14132508

Authors: Zhiqi Gao, Huiying Ma, Pingping Huang, Wei Xu, Weixian Tan, Zhixia Wu

Institution: Inner Mongolia University of Technology

Language: English

Topic: Advanced SAR Imaging Techniques

Subfield: Aerospace Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funder: National Natural Science Foundation of China