The ability to ensure covert unmanned aerial vehicle (UAV) communications is imperative in critical missions such as military surveillance and emergency response. In this paper, a multi-UAV covert communication system with informed jammers is investigated. To increase ground wardens’ detection uncertainty, we propose a joint dynamic scheduling and sensing jamming (DSSJ) scheme. Unlike existing approaches with fixed UAV roles and non-informed jamming, DSSJ dynamically schedules UAVs across adjacent time slots (TSs), while the jamming UAV performs sensing-based informed jamming per TS. Closed-form expressions are derived for the covert rate and minimum detection error probability (MDEP) under the worst-case scenario with optimal warden detection. An optimization problem is formulated to maximize the normalized weighted sum of covert rate and MDEP, subject to multiple constraints, including scheduling, sensing ratio, and other key factors. To solve this mixed-integer non-convex problem, we design a double deep Q-network (DDQN)-DSSJ algorithm, integrating DSSJ within a deep reinforcement learning framework, accelerated by experience replay and dynamic exploration, achieving real-time covert decision-making with polynomial complexity. Simulations demonstrate that DDQN-DSSJ achieves 25% faster convergence, enhanced stability, and superior covertness compared to proximal policy optimization and deep Q-network. Additionally, DDQN-DSSJ improves the covert rate by over 4× and MDEP by up to 28.3%, outperforming state-of-the-art schemes.

This paper proposes a digital twin network (DTN)-driven framework for multi-unmanned aerial vehicle (UAV) covert communication with informed jammers. To maximize warden detection uncertainty, we design a dynamic scheduling and sensing jamming (DSSJ) scheme that dynamically schedules UAV roles (communication/jamming/backup) across adjacent time slots (TSs), while the jamming UAV employs sensing-based informed jamming per TS. Leveraging DTN’s real-time digital-physical synchronization, closed-form expressions for the user’s covert rate and warden’s minimum detection error probability (MDEP) are derived under worst-case detection. A DTN-driven double deep Q-network (DDQN)-DSSJ algorithm solves the normalized weighted-sum maximization problem under covertness, sensing ratio, power, and speed constraints. Simulations demonstrate that DTN-driven DDQN-DSSJ achieves 25% faster convergence, enhanced stability, and superior covertness compared to proximal policy optimization (PPO) and deep Q-network (DQN).