Digital Twin Enabled Simultaneous Learning and Modeling for UAV-Assisted Secure Wireless Sensing in Unknown Environment

This paper focuses on secure communications in UAV-assisted wireless sensing systems in the presence of a mobile UAV eavesdropper (MUE), without prior information about the traffic demands of ground users (GUs) in the sensing environment. To maximize secrecy performance, we propose a digital-twin enhanced proximal policy optimization (DTEPPO) algorithm that optimizes the GUs' sensing scheduling and the UAVs' trajectory planning and network formation. Unlike traditional reinforcement learning methods that require complete environment-interacted information, we build a digital twin (DT) model using Gaussian process regression (GPR) based on the historical sensing information collected by the UAVs. Moreover, DT model can be dynamically updated by exploiting the PPO algorithm to lean the UAVs' interactions with the environment, continuously enhancing its accuracy and providing a reliable virtual learning environment for fast network adaptation. Numerical simulations demonstrate that the proposed DTEPPO algorithm achieves rapid convergence and improved secure throughput with reduced communication overhead compared to conventional approaches. Moreover, the proposed framework enables simultaneous learning and modeling (SLAM) in an unknown environment, providing a general framework to solve complex network control problems in wireless and mobile systems with high costs of environmental interactions.