Cooperative Fusion Localization of a Nonstationary Target for Multiple UAVs Without GPS
Fei Zhang, Xingling Shao, Wendong Zhang · IEEE Systems Journal · 2024
This article studies a global positioning system (GPS)-free distributed localization problem for a nonstationary target using a cluster of unmanned aerial vehicles (UAVs) loaded with bearing sensors, which aims to cooperatively estimate the relative positions of target by local interactions, regardless of whether or not the target can be directly detected. First, for leader UAVs that can readily detect the target, a novel bearing-based estimator devised in a local frame is proposed by following a prediction and correction configuration, while a sufficient condition is established to assure the asymptotic decaying of position estimation error. Second, considering follower UAVs that cannot directly observe the target, a special consensus-based cooperative fusion algorithm comprised of coupled observation and localization subsystems is proposed for UAVs to synchronize the target estimation with neighbors’ localization, wherein a fixed-time distributed observer is delicately constructed to provide target speed estimates, such that the requirements on the global availability of target speed can be avoided. The remarkable merit is that without resorting to GPS, all members can reach an agreement on relative positioning estimates in a distributed execution sense. Lyapunov approach certifies that all errors can exponentially approximate to the origin. Simulations confirm the efficacy of the presented algorithm.