文献类型：期刊文献

中文题名：基于深度学习与高斯差分法的ADS-B异常数据检测模型

英文题名：ADS-B Anomaly Data Detection Model Based on Deep Learning and Difference of Gaussian Approach

作者：王尔申[1];宋远上[1];徐嵩[1];郭婧[2];宏晨[3];曲萍萍[1];庞涛[1];张建通[4]

第一作者：WANG Ershen

通讯作者：Wang, Ershen

机构：[1]沈阳航空航天大学电子信息工程学院,中国沈阳110136;[2]中国民航科学技术研究院,中国北京100028;[3]北京联合大学机器人学院,中国北京100101;[4]中国交通通信信息中心,中国北京100011

第一机构：School of Electronic and Information Engineering,Shenyang Aerospace University,Shenyang 110136,P.R.China

年份：2020

卷号：37

期号：4

起止页码：550-561

中文期刊名：南京航空航天大学学报：英文版

外文期刊名：Transactions of Nanjing University of Aeronautics and Astronautics

收录：CSTPCD;;Scopus;CSCD:【CSCD2019_2020】；

基金：supported by the National Key R&D Program of China(No.2018AAA0100804);the Talent Project of Revitalization Liaoning(No.XLYC1907022);the Key R&D Projects of Liaoning Province(No.2020JH2/10100045);the Capacity Building of Civil Aviation Safety(No.TMSA1614);the Natural Science Foundation of Liaoning Province(No.2019-MS-251);the Scientific Research Project of Liaoning Provincial Department of Education(Nos.L201705,L201716);the High-Level Innovation Talent Project of Shenyang(No.RC190030);the Second Young and Middle-Aged Talents Support Program of Shenyang Aerospace University.

语种：中文

中文关键词：通用航空飞行器;广播式自动相关监视;异常数据检测;深度学习;高斯差分;长短期记忆单元

外文关键词：general aviation aircraft;automatic dependent surveillance-broadcast(ADS-B);anomaly data detection;deep learning;difference of Gaussian(DoG);long short-term memory(LSTM)

摘要：受地形结构、气象条件等多种因素的影响,用于低空通航飞行器定位的广播式自动相关监视(Automatic de-pendent surveillance-broadcast,ADS-B)设备获取的位置信息存在异常数据。为检测异常数据,提出一种基于深度学习与高斯差分法的ADS-B异常数据检测模型。首先,依据ADS-B位置数据的特点,将ADS-B位置数据转换到以起飞点为原点的坐标系中,利用运动学原理去除ADS-B位置数据中的离群点。然后,利用高斯差分法(Difference of Gaussian,DoG)获取位置数据的细节信息。最后,利用长短期记忆单元(Long short-term memory,LSTM)神经网络优化在ADS-B位置数据中梯度减小严重的循环神经网络(Recurrent neural network,RNN)。通过LSTM神经网络构成的seq2seq(Sequence to sequence)模型对位置数据进行重构,利用重构误差检测异常数据。通过实际数据对模型进行验证和对比分析表明:利用seq2seq模型对ADS-B位置数据重构的方法能有效地检测异常数据,运行时间得到减少,而且相较于RNN神经网络,检测的平均准确率提高了近2.7%,相较于传统的异常检测模型具有更高的准确率。
Due to the influence of terrain structure,meteorological conditions and various factors,there are anomalous data in automatic dependent surveillance-broadcast(ADS-B)message.The ADS-B equipment can be used for positioning of general aviation aircraft.Aim to acquire the accurate position information of aircraft and detect anomaly data,the ADS-B anomaly data detection model based on deep learning and difference of Gaussian(DoG)approach is proposed.First,according to the characteristic of ADS-B data,the ADS-B position data are transformed into the coordinate system.And the origin of the coordinate system is set up as the take-off point.Then,based on the kinematic principle,the ADS-B anomaly data can be removed.Moreover,the details of the ADS-B position data can be got by the DoG approach.Finally,the long short-term memory(LSTM)neural network is used to optimize the recurrent neural network(RNN)with severe gradient reduction for processing ADS-B data.The position data of ADS-B are reconstructed by the sequence to sequence(seq2seq)model which is composed of LSTM neural network,and the reconstruction error is used to detect the anomalous data.Based on the real flight data of general aviation aircraft,the simulation results show that the anomaly data can be detected effectively by the proposed method of reconstructing ADS-B data with the seq2seq model,and its running time is reduced.Compared with the RNN,the accuracy of anomaly detection is increased by 2.7%.The performance of the proposed model is better than that of the traditional anomaly detection models.