用在线边缘映射代替传统SAR成像，提升无人机目标识别效率
Online CS-based SAR Edge-Mapping
- 直接在边缘层面进行目标分类，跳过图像重建步骤
- 通过稀疏边缘图降低测量次数与计算开销
- 适合资源受限的无人机实时目标识别场景
随着现代防御应用越来越多依赖廉价小型无人机，设计智能且计算高效的机载自动目标识别（ATR）算法以实现作战目标成为关键挑战。这在合成孔径雷达（SAR）中尤为突出，传统处理常需在数据采集后进行，导致机载系统需存储大量回波信号，带来高内存负担。为此，本文提出一种在线、直接的边缘映射技术，绕过图像重建步骤，直接对场景和目标进行分类。同时，通过将场景重构为边缘图，天然促进稀疏性，所需测量数和计算量远低于经典SAR重建方法（如后向投影）。该方法显著降低了资源消耗，适用于嵌入式平台。
原文摘要 · Abstract (English)
With modern defense applications increasingly relying on inexpensive, small Unmanned Aerial Vehicles (UAVs), a major challenge lies in designing intelligent and computationally efficient onboard Automatic Target Recognition (ATR) algorithms to carry out operational objectives. This is especially critical in Synthetic Aperture Radar (SAR), where processing techniques such as ATR are often carried out post data collection, requiring onboard systems to bear the memory burden of storing the back-scattered signals. To alleviate this high cost, we propose an online, direct, edge-mapping technique which bypasses the image reconstruction step to classify scenes and targets. Furthermore, by reconstructing the scene as an edge-map we inherently promote sparsity, requiring fewer measurements and computational power than classic SAR reconstruction algorithms such as backprojection.
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