# Aircraft Track Anomaly Detection Based on MOD-Bi-LSTM
## 4 Conclusions  
In this paper, an aircraft track anomaly detection method based on the combination of MOD and Bi-LSTM is proposed, and the effectiveness of the method is verified by using the real aircraft track data. By analyzing the experimental results, the proposed method can detect both track deviation and track outliers well. The main conclusions of this article are as follows:  
1. The track deviation detection problem is transformed into the track density classification problem, and the MOD is designed to detect track deviation. The accuracy and recall of the MOD algorithm are improved compared to the LOF algorithm.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Accuracy/\%** & **Recall/\%** & **Test Time/s** \\ \hline LSTM & 91.62 & 92.61 & 0.94 \\ Bi-LSTM & 95.51 & 94.28 & 1.37 \\ BP & 93.49 & 94.10 & 2.23 \\ LR & 90.98 & 86.92 & 0.98 \\ CNN & 95.93 & 90.21 & 4.07 \\ ours & 96.00 & 97.36 & 2.06 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of Testing Effects of Different Models.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Height**} & \multicolumn{2}{c}{**Speed**} & \multicolumn{2}{c}{**Heading**} \\ \cline{2-7}  & **RMSE** & **MAPE** & **RMSE** & **MAPE** & **RMSE** & **MAPE** \\  & **/(m)** & **/\%** & **/(Km/h)** & **/\%** & **/(degrees)** & **/\%** \\ \hline LSTM & 1249.43 & 16.32 & 21.80 & 2.79 & 21.92 & 11.36 \\ Bi-LSTM & 810.62 & 10.33 & 19.36 & 2.58 & 19.51 & 10.07 \\ BP & 1032.59 & 13.49 & 21.87 & 2.84 & 20.66 & 10.71 \\ LR & 1520.18 & 19.86 & 26.41 & 3.44 & 24.73 & 12.81 \\ CNN & 402.16 & 5.25 & 16.20 & 2.11 & 13.49 & 6.99 \\ ours & 279.35 & 3.76 & 15.45 & 2.01 & 13.08 & 6.78 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of Prediction Indicators of Different Models.  
2. The track outliers detection problem is transformed into the prediction problem, and the Bi-LSTM model is used to detect the track outliers. Compared with the traditional methods, the accuracy is improved.
3. The anomaly detection algorithm considers not only the track density information, but also the features of the track point. More comprehensive factors are taken into account and the accuracy has improved.  
In future studies, more attention should be paid to the application of the model, and the response time of the model should be reduced to ensure real time detection. Next, some optimization algorithms will be used to further optimize the parameters of the MOD-Bi-LSTM model to improve the accuracy of anomaly detection.  
Conceptualization, Y.C., Z.Z. and Z.L.; methodology, Z.Z. and Z.L.; software, J.C.; formal analysis, Z.Z.; investigation, Y.C., J.C., Z.Z. and Z.L.; writing--original draft preparation, Y.C.; writing--review and editing, J.C., Z.Z. and Z.L.; supervision, Z.Z. and Z.L.; project administration, Z.L.; funding acquisition, Z.L. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the Equipment Pre-research Field Fund Thirteen Five-year (No. 61403120109). Data Availability StatementData was obtained from the third party and are available from the authors with the permission of the third party.  
We would like to thank [PERSON] and [PERSON] for the beneficial discussion.  
The authors declare no conflict of interest.