Sensing in Low-altitude Wireless Networks: Systems, Techniques, and Developments

2026-08-11

AI Summary

This study addresses the stringent demands imposed by the high dynamics and safety-critical nature of Low-Altitude Wireless Networks (LAWN) on sensing technologies, which existing approaches struggle to meet. It presents the first systematic survey of LAWN-oriented sensing, comprehensively examining sensing tasks, node objectives, and representative scenarios through the lenses of system architecture, core methodologies, and emerging research trends. The work comparatively analyzes technical pathways across diverse propagation media, collaboration mechanisms, methodological paradigms, and sensing modalities. Innovatively, it identifies promising future directions, including non-cooperative/cooperative sensing, model-driven/data-driven fusion, and multimodal collaboration. Empirical case studies further validate the efficacy of joint model- and data-driven multimodal fusion for real-time aerial target perception, offering a clear and actionable roadmap toward deployable LAWN sensing systems.

Abstract

The highly dynamic and safety-critical characteristics of low-altitude airspace render sensing an indispensable component of low-altitude wireless networks (LAWN). Although sensing techniques have been extensively studied under diverse paradigms, a prominent mismatch persists between state-of-the-art sensing schemes and the practical sensing demands of LAWN. To fill this research gap, this article systematically reviews LAWN-oriented sensing from the dimensions of system framework, core technologies, and research trends. Specifically, we first analyze the sensing system framework, covering concepts, services and tasks, nodes and targets, and scenarios for LAWN sensing. Next, we conduct a comparative analysis of existing sensing techniques from the perspectives of propagation medium, cooperation, methodology, and modality, analyzing their advantages and limitations. Then, we summarize promising future research directions for deployable LAWN sensing systems, covering non-cooperative and cooperative sensing, model-driven and data-driven sensing, and model-and-data-driven multi-modal sensing. Finally, we present a case study of a model-and-data-driven multi-modal method for real-time aerial target sensing. Compared with existing surveys on LAWN or sensing, this article delivers a more comprehensive, targeted review exclusively centered on LAWN sensing.

Problem

• low-altitude wireless networks
• sensing
• dynamic airspace
• safety-critical
• perception-demand mismatch

Innovation

• low-altitude wireless networks
• sensing framework
• multi-modal sensing
• model-and-data-driven
• aerial target sensing