De Silva A H T Eranga, Katupitiya Jayantha
School of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, NSW 2052, Australia.
Sensors (Basel). 2024 Jul 17;24(14):4629. doi: 10.3390/s24144629.
This paper presents a novel methodology to localise Unmanned Ground Vehicles (UGVs) using Unmanned Aerial Vehicles (UAVs). The UGVs are assumed to be operating in a Global Navigation Satellite System (GNSS)-denied environment. The localisation of the ground vehicles is achieved using UAVs that have full access to the GNSS. The UAVs use range sensors to localise the UGV. One of the major requirements is to use the minimum number of UAVs, which is two UAVs in this paper. Using only two UAVs leads to a significant complication that results an estimation unobservability under certain circumstances. As a solution to the unobservability problem, the main contribution of this paper is to present a methodology to treat the unobservability problem. A Constrained Extended Kalman Filter (CEKF)-based solution, which uses novel kinematics and heuristics-based constraints, is presented. The proposed methodology has been assessed based on the stochastic observability using the Posterior Cramér-Rao Bound (PCRB), and the results demonstrate the successful operation of the proposed localisation method.
本文提出了一种利用无人机（UAV）对无人地面车辆（UGV）进行定位的新方法。假设无人地面车辆在全球导航卫星系统（GNSS）信号被阻断的环境中运行。地面车辆的定位是通过能够完全接入GNSS的无人机来实现的。无人机使用距离传感器对无人地面车辆进行定位。其中一个主要要求是使用最少数量的无人机，本文中为两架无人机。仅使用两架无人机导致了一个重大的复杂性问题，即在某些情况下会导致估计不可观测。作为不可观测问题的解决方案，本文的主要贡献是提出一种处理不可观测问题的方法。提出了一种基于约束扩展卡尔曼滤波器（CEKF）的解决方案，该方案使用了新颖的运动学和基于启发式的约束。所提出的方法已基于使用后验克拉美罗界（PCRB）的随机可观测性进行了评估，结果证明了所提出的定位方法的成功运行。
