用于低空监视的无线成像：ISAC网络的新范式
Wireless Imaging for Low-Altitude Surveillance: A New Paradigm for ISAC Networks
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中文总结 AI 辅助
针对低空经济对飞行监测的需求，提出分层无线成像框架作为ISAC网络的新范式，结合广域成像、动态轨迹成像与细粒度表征，助力低空监视。
中文摘要 AI 辅助
低空经济的快速发展需要具备鲁棒飞行监测能力的集成感知与通信（ISAC）网络。本文倡导无线成像作为一种统一的感知范式，使ISAC网络能够成为全面的低空守护者。我们提出了一种分层成像框架，可将感知能力从广域快照成像提升至动态轨迹感知成像，再到以目标为中心的细粒度表征。在广域层面，低空感知被重新定义为空间成像问题，分布式基站和通信用户协同构建空域的整体视图。在此基础上，多帧动态成像利用时间相关性，在遮挡等移动性引发的挑战下支持鲁棒的轨迹跟踪与预测。对于安全关键场景，该框架还能对飞行和悬停的无人飞行器进行细粒度成像，提供超越传统点目标抽象的详细表征。此外，我们提出了一种名为成像覆盖率的新型评估指标，用于检验所提框架的感知保真度。示例案例研究证明了ISAC网络中成像在支持广域监测、运动感知跟踪和细粒度目标分析方面的潜力。
英文摘要
The rapid growth of the low-altitude economy calls for integrated sensing and communication (ISAC) networks capable of robust flight monitoring. This article advocates wireless imaging as a unifying sensing paradigm that enables ISAC networks to function as comprehensive low-altitude guardians. We present a hierarchical imaging framework that enhances sensing capability from wide-area snapshot imaging to dynamic trajectory-aware imaging and target-centric fine-grained characterization. At the wide-area level, low-altitude sensing is reformulated as a spatial imaging problem, where distributed base stations and communication users collaboratively construct a holistic view of the aerial space. Building on this foundation, multi-frame dynamic imaging exploits temporal correlations to support robust trajectory tracking and prediction under mobility induced challenges such as occlusions. For security-critical scenarios, the framework further enables fine-grained imaging of flying and hovering uncrewed aerial vehicles, providing detailed characterization beyond conventional point-target abstractions. Additionally, we propose a novel evaluation metric named imaging coverage to examine the sensing fidelity of the proposed framework. Illustrative case studies demonstrate the potential of imaging in ISAC networks to support wide-area monitoring, motion-aware tracking, and fine-grained target analysis.
