This paper develops a data-based moving horizon estimation (MHE) method for agile quadrotors. Accurate state estimation of the system is paramount for precise trajectory control for agile quadrotors; however, the high level of aerodynamic forces experienced by the quadrotors during high-speed flights make this task extremely challenging. These complex turbulent effects are difficult to model and the unmodelled dynamics introduce inaccuracies in the state estimation. In this work, we propose a method to model these aerodynamic effects using Gaussian Processes which we integrate into the MHE to achieve efficient and accurate state estimation with minimal computational burden. Through extensive simulation and experimental studies, this method has demonstrated significant improvement in state estimation performance displaying superior robustness to poor state measurements.

翻译：本文提出了一种基于数据的移动视界估计（MHE）方法，用于敏捷四旋翼飞行器。在高速飞行中，精确的状态估计对于实现精准轨迹控制至关重要；然而，敏捷四旋翼在高速飞行过程中承受的巨大气动力使这一任务极具挑战性。这些复杂的湍流效应难以建模，而未建模的动态特性会导致状态估计出现误差。在本研究中，我们提出了一种利用高斯过程对这些气动效应进行建模的方法，并将其集成到MHE中，以在最小化计算负担的同时实现高效准确的状态估计。通过广泛的仿真和实验研究，该方法在状态估计性能上展现出显著提升，并对较差的状态测量值表现出更强的鲁棒性。