基于生成对抗网络的无人机系统着陆轨迹预测
Landing Trajectory Prediction for UAS Based on Generative Adversarial Network
AI总结：
本文提出基于生成对抗网络（GAN）的无人机着陆轨迹预测模型，利用生成器学习轨迹特征并输出飞行状态，在多个数据集上优于基线方法GMR，并构建了包含2600余条真实飞行员操控轨迹的着陆数据集。
摘要：
轨迹预测模型是许多先进空中交通研究的重要组成部分。这些模型帮助飞行器检测冲突并规划避让机动，这在无人机系统（UAS）着陆管理中尤为重要，因为垂直起降机场附近的空域十分拥挤。本文提出了一种基于生成对抗网络（GAN）的无人机系统着陆轨迹预测模型。GAN是一种经过多年发展的著名神经网络。在以往研究中，GAN已在许多生成任务中取得了大量最先进的成果。GAN由一个神经网络生成器和一个神经网络判别器组成。由于神经网络的学习能力，生成器能够理解样本轨迹的特征。生成器将先前的轨迹作为输入，并输出飞行的若干随机状态。根据实验结果，所提出的模型在多个数据集上能够输出比基线方法（GMR）更准确的预测。为了评估所提出的模型，我们还创建了一个真实的无人机着陆数据集，其中包含由真实飞行员手动控制的超过2600条无人机轨迹。
英文摘要：
Models for trajectory prediction are an essential component of many advanced air mobility studies. These models help aircraft detect conflict and plan avoidance maneuvers, which is especially important in Unmanned Aircraft systems (UAS) landing management due to the congested airspace near vertiports. In this paper, we propose a landing trajectory prediction model for UAS based on Generative Adversarial Network (GAN). The GAN is a prestigious neural network that has been developed for many years. In previous research, GAN has achieved many state-of-the-art results in many generation tasks. The GAN consists of one neural network generator and a neural network discriminator. Because of the learning capacity of the neural networks, the generator is capable to understand the features of the sample trajectory. The generator takes the previous trajectory as input and outputs some random status of a flight. According to the results of the experiences, the proposed model can output more accurate predictions than the baseline method(GMR) in various datasets. To evaluate the proposed model, we also create a real UAV landing dataset that includes more than 2600 trajectories of drone control manually by real pilots.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
