# Aircraft Track Anomaly Detection Based on MOD-Bi-LSTM
## 1 Introduction  
With the rapid development of satellite communication technology, the ADS-B data reflecting aircraft track is becoming more abundant, and there is a lot of valuable information from the track data. In order to realize multi-source information fusion and the tracking of aircraft flights, Chinese and other scholars have carried out a lot of exploration and research work on track data mining methods, such as the ADS-B anomaly data detection model based on VAE-SVDD [1], track clustering [2], track correlation analysis [3, 4], object motion pattern recognition [5], path planning [6], anomalous trajectories detection [7], etc. In the field of information fusion, the abnormal behavior of the target can be mined through the multi-dimensional track characteristics of the target, which is of great significance for situation assessment, threat assessment and command decision-making [8]. In the real environment, there are many unreasonable sampling points that have huge differences with their neighboring track points in motion features in the track data. These points are outliers in the track data [9]. All kinds of outliers affect the data quality of the track, and also have a certain effect on the information mining in subsequent tracks. Therefore, anomaly detection based on track data is an important basis of track data analysis and mining. The track data of an aircraft flight is usually a sequence comprised of Multidimensional data points. The abnormal behavior of the target can be mined through the anomaly detection of Multidimensional tracks. Scholars at home and abroad have done a lot of research on anomaly detection and track anomaly detection. [PERSON] et al. summarized the anomaly detection technology, classification, application scope, advantages and disadvantages [10, 11]. Existing anomaly detection methods can be divided into statistics-based anomaly detection [12], distance-based anomaly detection [13], density-based anomaly detection [14], depth-based anomaly detection [15] and deviation-based anomaly detection [16], etc. Existing track anomaly detection methods include methods based on the Multidimensional Local Outlier Factor [8], extracting global track features [17], classifiers [18, 19], track segment similarity detection [20, 21], etc. In recent years, with theadvancement of deep learning methods in the big data detection field, anomaly detection methods of deep learning have come to include an anomaly detection algorithm based on multi-layer convolution neural network interactive visualization [22], a Long-short Time Memory Network anomaly detection algorithm based on the encoder-decoder framework [23], a novel intrusion detector based on deep learning hybrid methods [24], forecasting and anomaly detection approaches using LSTM and LSTM autoencoder techniques with the applications in supply chain management [25], a new method for anomaly detection of seismic preprecursor data based on LSTM-RNN [26], a deep learning-based hybrid intelligent intrusion detection system [27], deep learning for anomaly detection [28], a track anomaly detection algorithm based on the Bidirectional Long-short Time Memory Network [9], etc. In addition, [PERSON] et al. published a review on deep and shallow anomaly detection [29]. Some of the above methods can only detect the position anomaly of the target track, some do not make full use of the multidimensional features of the target, some rely too much on the parameter threshold, some are unable to automatically learn the difference between the abnormal points and normal points of the track and some have poor applicability to complex types of data. In a word, there are some limitations in mining the abnormal behavior of a target. In view of the above problems, a track anomaly detection algorithm based on the Multidimensional Outlier Descriptor (MOD) combined with the Bi-directional Long-Short Time Memory Network (Bi-LSTM) is proposed in this paper. Firstly, the MOD algorithm is used to detect the track deviation.