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Relative Localization with Application to Adaptive Navigation Based on Mixed Measurements of Distance and Bearing Utilizing Multi-UAVs
Jia Guo, Minggang Gan and Kang Hu

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 estimator 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 theoretical approach.

Keywords: RL, Mixed measurements, Adaptive navigation, Multi-UAVs