In practice, the consensus performance of a multi-UAV system can degrade significantly due to the presence of measurement noise and disturbances. However, simultaneously rejecting the noise and disturbances to achieve high-precision consensus tracking control is rather challenging. In this paper, to address this issue, we propose a novel distributed consensus tracking control framework consisting of a distributed observer and a local dual-estimator-based tracking controller. Each UAV’s distributed observer estimates the leader’s states and generates the local reference, functioning even under a switching communication topology. In the local tracking controller design, we reveal that classic uncertainty and disturbance estimator (UDE)-based control can magnify the noise. By combining the measurement error estimator (MEE) with UDE, a local robust tracking controller is designed to reject noise and disturbances simultaneously. The parameter tuning of MEE and UDE is unified into a single parameter, and the monotonic relationship between this parameter and system performance is revealed by the singular perturbation theorem. Finally, the validity of the proposed control framework is verified by both simulation and comparative real-world experiments.

Low-cost and real-time remote sensing of moving targets is increasingly required in civilian applications. Micro unmanned aerial vehicles (UAVs) provide a promising platform for such missions because of their small size and flexible deployment, but they are constrained by payload capacity and energy budget. Consequently, they typically carry lightweight monocular cameras only. These cameras cannot directly measure distance and suffer from scale ambiguity, which makes accurate geolocation difficult. This paper tackles geolocation and short-term trajectory prediction of moving targets over uneven terrain using bearing-only measurements from a monocular camera. We present a two-stage estimation framework in which a pseudo-linear Kalman filter (PLKF) provides real-time state estimates, while a sliding-window nonlinear least-squares (NLS) back end refines them. Future target positions are obtained by extrapolating the estimated trajectory. To improve localization accuracy, we analyze the relationship between the UAV path and the Cramér–Rao lower bound (CRLB) using the Fisher Information Matrix (FIM) and derive an observability-enhanced trajectory planning method. Real-flight experiments validate the framework, showing that accurate geolocation can be achieved in real time using only low-cost monocular bearing measurements.