为了提升机动目标的航迹预测精准度,提出了一种粒子群(Particle Swarm Optimization,PSO)算法与长短周期记忆单元(Long Short Term Memory,LSTM)神经网络相结合的PSO-LSTM目标航迹预测模型。针对LSTM神经网络中的超参数难以人工最优化...为了提升机动目标的航迹预测精准度,提出了一种粒子群(Particle Swarm Optimization,PSO)算法与长短周期记忆单元(Long Short Term Memory,LSTM)神经网络相结合的PSO-LSTM目标航迹预测模型。针对LSTM神经网络中的超参数难以人工最优化的问题,通过PSO算法进行自动调参,对诸如LSTM隐藏层规模、学习率、训练集批次规模等参数进行优化。使用PSO-LSTM航迹预测模型在真实航迹数据集上进行预测,实验结果表明,PSO-LSTM航迹预测模型在MAE、RMSE等指标上优于传统模型,有着较好的准确性与稳定性。展开更多
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
文摘为了提升机动目标的航迹预测精准度,提出了一种粒子群(Particle Swarm Optimization,PSO)算法与长短周期记忆单元(Long Short Term Memory,LSTM)神经网络相结合的PSO-LSTM目标航迹预测模型。针对LSTM神经网络中的超参数难以人工最优化的问题,通过PSO算法进行自动调参,对诸如LSTM隐藏层规模、学习率、训练集批次规模等参数进行优化。使用PSO-LSTM航迹预测模型在真实航迹数据集上进行预测,实验结果表明,PSO-LSTM航迹预测模型在MAE、RMSE等指标上优于传统模型,有着较好的准确性与稳定性。
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.
