Standoff Target Tracking for Networked UAVs With Specified Performance via Deep Reinforcement Learning

Maintaining rapid and prolonged standoff target tracking for networked unmanned aerial vehicles (UAVs) is challenging, as existing methods fail to improve tracking performance while simultaneously reducing energy consumption. This paper proposes a deep reinforcement learning (DRL)-based tracking scheme for UAVs to approximate an escape target, effectively addressing time constraints and guaranteeing low energy expenditure. In the first phase, a coordinated target tracking protocol and a target position estimator are developed using only bearing measurements, which enable the deployment of UAVs along a standoff circle centered at the target with an expected angular spacing. Additionally, an unknown system dynamics estimator (USDE) is devised based on concise filtering operations to mitigate adverse disturbances. In the second phase, multi-agent deep deterministic policy gradient (MADDPG) is employed to strike an optimal balance between tracking accuracy and energy consumption by encoding time limitations as skilled barrier functions. Simulation results demonstrate that the proposed method outperforms benchmarks in terms of tracking accuracy and control cost.