基于GRU-VAE的无监督航迹异常检测方法  

Unsupervised abnormal track detection method based on GRU-VAE

作　　者：李磊 张静 欧阳齐铖 周明康 LI Lei;ZHANG Jing;OUYANG Qicheng;ZHOU Mingkang(PLA Strategic Support Force Information Engineering University,Zhengzhou 450001,China;China Communications Construction Fourth Engineering Bureau Co.,Ltd,Zhengzhou 450001,China)

机构地区：[1]中国人民解放军战略支援部队信息工程大学,河南郑州450001 [2]中国通信建设第四工程局有限公司,河南郑州450001

出　　处：《指挥控制与仿真》2023年第5期51-64,共14页Command Control & Simulation

摘　　要：针对海量无行为模式标签航迹数据的目标行为异常检测问题,提出一种基于门控循环单元的变分自编码器模型(Gate Recurrent Unit-Variational Autoencoder,GRU-VAE)无监督航迹异常检测方法。该方法通过检测航迹异常发现目标的行为异常,分为模型训练阶段和异常检测阶段两步实施:在模型训练阶段,构建了以GRU和VAE为主要结构元素的基于门控循环单元的变分自编码器模型,利用无异常信息标签的历史航迹数据对GRU-VAE模型进行训练,根据训练集的航迹点重构损失分布情况,采用正态分布法或百分位数法划定置信区间为航迹点重构损失门限;在异常检测阶段,该模型对实时航迹数据集进行检测,将重构损失超出航迹点重构损失门限的航迹点视为异常航迹点,当航迹序列中的异常航迹点占比超出占比阈值时,判定为异常航迹序列,结合数据异常情况向一线人员发送目标的异常行为信息。AIS数据实验结果表明,模型最高F 1分数达86.36%,查全率达95%。本方法对异常航迹的检测具有高灵敏度和低漏警率,可满足战场态势认知需求。Aiming at the problem of ship behavior anomaly detection based on massive track data without behavior pattern label,an unsupervised track anomaly detection method based on GRU-VAE model is proposed.The abnormal behavior of the target is found by detecting track anomaly,which is implemented in two steps:model training stage and anomaly detection stage.In the model training stage,the timing modeling ability of GRU gated cyclic Autoencoder(VAE)model is introduced.The Gate Recurrent unit-variational Autoencoder model is trained by historical track data without abnormal information labels.According to the reconstruction loss distribution,the normal distribution method or percentile method is used to delimit the confidence interval as the reconstruction loss threshold.In anomaly detection phase,the model of real-time track data set for testing,regarding the damage threshold of the track to refactor losses above points as abnormal track points,when the track is in the sequence beyond the proportion of abnormal track point accounted threshold,it is judged to be abnormal track sequence,combined with the data anomalies target behavior information are presented to the first-line staff.The experimental results on AIS data show that the highest F1 score of the model is up to 86.36%,and the recall rate is up to 95%.The high sensitivity and low miss alarm rate of this method to abnormal track meet the reconnaissance requirements of first-line units.

关 键 词：数据挖掘 航迹数据 异常检测 无监督学习 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]