Multi-AAV Energy-Efficient Detection Coverage Under Jamming Environment: A Hierarchical Collaborative Learning Approach

Autonomous aerial vehicles (AAVs) can provide detection coverage service in many scenarios. The fair coverage is achieved by designing carefully AAVs' trajectories, which are established at each step by choosing the moving action and channel access for transmitting data. However, under the jamming environment, the problematic trajectories could lead to mutual interference and malicious jamming, such that the coverage service fails. The selections of moving action and channel access are usually coupled, and so far no work has addressed them jointly for planning trajectory. As such, this paper investigates the multi-AAV joint optimization of moving action and channel access for the energy-efficient detection coverage. To decouple the strongly-coupled moving action and channel access, we model the studied optimization problem as a hierarchical game, where the stochastic game (resp. potential game) is applied for selecting moving action (resp. channel access). Then, we propose a multi-agent hierarchical cooperative learning (MAHCL) algorithm to attain near-optimal solution for the joint optimization. It is proved that the proposed MAHCL algorithm can asymptotically converge to the near-optimal joint strategy with lower computational complexity. Finally, the simulation results show the higher energy efficiency of MAHCL algorithm compared with the benchmarks.