Transactions of Nanjing University of Aeronautics & Astronautics
ADS-B Anomaly Data Detection Model Based on Deep Learning and Difference of Gaussian Approach
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Abstract:
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.
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Clc Number:
TN279
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