AGI-Enabled Networked Sensing Data Aggregation for Heterogeneous UAV-UGV Coalition in Low-Altitude ISAC Systems

作者：Xing Zhang, Ronghui Zhang, Yuanhao Cui, Minghao Gao, Weijie Yuan, Xiaojun Jing · 发表于：IEEE Transactions on Cognitive Communications and Networking · 年份：2025 · DOI：10.1109/tccn.2025.3631710 · 被引用次数：2 · 研究领域：UAV Applications and Optimization、Air Traffic Management and Optimization、Mobile Crowdsensing and Crowdsourcing

The rapid expansion of the low-altitude economy, powered by emerging sectors such as smart logistics, precision agriculture, and emergency services, necessitates advanced integrated sensing and communication solutions. Leveraging Artificial General Intelligence (AGI) principles, this paper introduces a novel heterogeneous cooperative multi-agent deep reinforcement learning algorithm for Air-Ground Integrated Sensing and Communication Systems (AG-ISAC) systems, designed to balance individuality and cooperation among heterogeneous vehicles for optimized networked sensing data aggregation. The proposed algorithm features two primary modules: an individual characteristics optimization module, which identifies and harnesses the unique features of Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) to facilitate efficient task distribution, and a heterogeneous cooperative strategy optimization module, which models UAV-UGV collaboration to optimize trajectories and sensing data acquisition. Simulation results validate that the algorithm significantly enhances sensing efficiency, data integrity, and power management, representing an advanced solution for integrated air-ground communication and sensing in constrained environments.