Abstract
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.
Abstract (translated)
随着现代防御应用越来越多地依赖于廉价、自主的无人机，设计计算和内存效率高的机载算法以实现任务目标成为了一个重大挑战。尤其是在合成孔径雷达（SAR）领域，由于需要收集和处理大量的数据来进行后续任务，这一挑战尤为突出。我们提出了一种在线重建方法——在线快速迭代收缩阈值法（Online FISTA），该方法通过稀疏编码逐步使用有限的数据重建场景。与存储所有接收到的信号数据相比，算法递归地更新每次迭代中的存储矩阵，大大减少了内存需求。在线SAR图像重建使得像自动目标识别（ATR）等更复杂的后续任务能够实时进行，从而形成一个比现有的事后收集重建和ATR方法更为灵活和集成的框架。
URL
https://arxiv.org/abs/2603.08582
