Relative Localization with Application to Adaptive Navigation Based on Mixed Measurements of Distance and Bearing Utilizing Multi-UAVs

This paper addresses the problem of Relative Localization (RL) for Multiple Unmanned Aerial Vehicles (Multi-UAVs), which aims to estimate the relative coordinates of each Unmanned Aerial Vehicle (UAV) concerning a target (UAV1). In sensor networks, the network topology can be unstable due to unreliable communication. To tackle this challenge, this paper proposes a fully distributed algorithm called Distance and Bearing-based Relative Localization (DBRL) that enables each UAV to estimate the relative coordinates of the UAV1 in real time, even if it cannot directly detect the UAV1. A consensus-based RL fusion estimation is proposed. The fundamental principle of fusion is that each UAV collaboratively performs direct and indirect RL estimation through consensus fusion, resulting in real-time production of the relative positions of UAV1. The results demonstrate that as long as there is a path from each UAV to the UAV1 in the perception graph, each UAV esti-mator and fusion method achieves global asymptotic stability. The proposed RL estimation is then applied to adaptive navigation. Simulation results are provided to validate the effectiveness of the proposed theo-retical approach.