提出在线稀疏成像算法，用少量数据实时重建雷达图像。
Online Sparse Synthetic Aperture Radar Imaging
- 基于增量式稀疏编码，递归更新存储矩阵，无需保存全部信号数据。
- 相比传统离线方法，内存需求大幅降低，支持实时处理。
- 适合无人机等资源受限平台，可用于在线目标识别任务。
随着现代防御应用越来越多依赖低成本、自主无人机，设计计算与内存高效的机载算法以实现任务目标成为关键挑战。这一挑战在合成孔径雷达（SAR）中尤为突出，因其需采集并处理大量数据以支持下游任务。本文提出一种在线重建方法——在线快速迭代收缩阈值算法（Online FISTA），通过稀疏编码逐步重建场景，无需存储所有接收信号数据。该算法通过递归更新存储矩阵，显著降低内存需求。在线SAR图像重建支持更复杂的下游任务，如自动目标识别（ATR），实现在线处理，相较于现有离线重建与ATR方法，构建了更灵活、集成度更高的系统框架。
原文摘要 · Abstract (English)
With modern defense applications increasingly relying on inexpensive, autonomous drones, lies the major challenge of designing computationally and memory-efficient onboard algorithms to fulfill mission objectives. This challenge is particularly significant in Synthetic Aperture Radar (SAR), where large volumes of data must be collected and processed for downstream tasks. We propose an online reconstruction method, the Online Fast Iterative Shrinkage-Thresholding Algorithm (Online FISTA), which incrementally reconstructs a scene with limited data through sparse coding. Rather than requiring storage of all received signal data, the algorithm recursively updates storage matrices for each iteration, greatly reducing memory demands. Online SAR image reconstruction facilitates more complex downstream tasks, such as Automatic Target Recognition (ATR), in an online manner, resulting in a more versatile and integrated framework compared to existing post-collection reconstruction and ATR approaches.
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