计算机科学 ›› 2023, Vol. 50 ›› Issue (3): 121-128.doi: 10.11896/jsjkx.220100086
饶丹, 时宏伟
RAO Dan, SHI Hongwei
摘要: 针对传统的聚类算法无法捕获高维轨迹数据在低维空间中的隐含关系,且难以定义适当的相似性度量以同时考虑轨迹的局部和全局特征的问题,提出了一种基于深度神经网络的多变量轨迹深度聚类框架(MTDC)并将其用于航空交通流识别与异常检测。该框架主要包含一个非对称的自编码器和一个自定义的轨迹聚类层。自编码器由一维卷积神经网络和双向长短时记忆网络堆叠而成,用于学习原始输入在低维隐空间中的特征表示。轨迹聚类层则通过计算隐空间中样本的Q分布实现聚类。结合自编码器的重建损失和轨迹聚类Q分布定义了一个新的异常分数,用于检测异常轨迹。使用基于广播式自动相关监视(ADS-B)的真实轨迹数据进行实验,结果表明,所提框架能有效地进行航空交通流识别,并能检测出具有实际意义且可解释的异常轨迹。
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