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 position...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.展开更多
为研究自由飞行条件下给定间距的飞机碰撞风险评估问题,通过分析自由飞行下的飞机碰撞过程,分解碰撞事故发生过程,将与碰撞密切相关的风险因素或过程事件视为节点,并确定节点之间的关系,建立自由飞行状态下基于贝叶斯网络的碰撞风险模型...为研究自由飞行条件下给定间距的飞机碰撞风险评估问题,通过分析自由飞行下的飞机碰撞过程,分解碰撞事故发生过程,将与碰撞密切相关的风险因素或过程事件视为节点,并确定节点之间的关系,建立自由飞行状态下基于贝叶斯网络的碰撞风险模型;利用传统的位置误差模型,以及最大期望(EM)算法,求解节点事件的先验概率,导入贝叶斯网络模型,求得2架飞机碰撞风险。算例结果表明,用该模型计算出的碰撞风险与实际情况相符,算例中飞机之间保持8 n mile的间距是安全的;利用该模型可在满足安全目标水平条件下缩小最小安全间距,提高空域利用率。展开更多
基金supported by the National Key R&D Program of China(No.2018AAA0100804)the Talent Project of Revitalization Liaoning(No.XLYC1907022)+5 种基金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.
文摘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.
文摘为研究自由飞行条件下给定间距的飞机碰撞风险评估问题,通过分析自由飞行下的飞机碰撞过程,分解碰撞事故发生过程,将与碰撞密切相关的风险因素或过程事件视为节点,并确定节点之间的关系,建立自由飞行状态下基于贝叶斯网络的碰撞风险模型;利用传统的位置误差模型,以及最大期望(EM)算法,求解节点事件的先验概率,导入贝叶斯网络模型,求得2架飞机碰撞风险。算例结果表明,用该模型计算出的碰撞风险与实际情况相符,算例中飞机之间保持8 n mile的间距是安全的;利用该模型可在满足安全目标水平条件下缩小最小安全间距,提高空域利用率。
