A Deep Unsupervised Learning Approach for Airspace Complexity Evaluation
A Deep Unsupervised Learning Approach for Airspace Complexity Evaluation
复制标题
空域复杂性评估的深度无监督学习方法
DOI：
10.1109/tits.2021.3106779
复制
发表时间：
2022
影响因子：
8.5
通讯作者：
Li B
中科院分区：
文献类型：
--
作者：
Li B
空域复杂度是当前空中交通管理系统中反映空域运行安全程度的重要指标。空域复杂性受多种因素的耦合影响，具有复杂的非线性特征，评估难度极大。近年来，机器学习被证明是一种很有前途的方法，并在评估空域复杂性方面取得了显著的成果。然而，现有的基于机器学习的方法需要大量由专家标记的空域操作数据。由于标记操作数据的高成本和空域操作环境的动态性质，这样的数据通常是有限的，并且可能不适合于变化的空域情况。有鉴于此，我们提出了一种新的无监督学习方法，用于空域复杂性评估，该方法基于由未标记样本训练的深度神经网络。我们引入了一个新的损失函数，以更好地解决有关空域复杂性数据的特点，包括维度耦合，类别不平衡，重叠的边界。由于这些特征，现有无监督模型的泛化能力受到不利影响。基于西南空域6个扇区的实际数据，对该方法进行了实验验证。实验结果表明，我们的深度无监督模型在空域复杂度评估准确性方面优于最先进的方法。
Airspace complexity is a critical metric in current Air Traffic Management systems for indicating the security degree of airspace operations. Airspace complexity can be affected by many coupling factors in a complicated and nonlinear way, making it extremely difficult to be evaluated. In recent years, machine learning has been proved as a promising approach and achieved significant results in evaluating airspace complexity. However, existing machine learning based approaches require a large number of airspace operational data labeled by experts. Due to the high cost in labeling the operational data and the dynamical nature of the airspace operating environment, such data are often limited and may not be suitable for the changing airspace situation. In light of these, we propose a novel unsupervised learning approach for airspace complexity evaluation based on a deep neural network trained by unlabeled samples. We introduce a new loss function to better address the characteristics pertaining to airspace complexity data, including dimension coupling, category imbalance, and overlapped boundaries. Due to these characteristics, the generalization ability of existing unsupervised models is adversely impacted. The proposed approach is validated through extensive experiments based on the real-world data of six sectors in Southwestern China airspace. Experimental results show that our deep unsupervised model outperforms the state-of-the-art methods in terms of airspace complexity evaluation accuracy.