Abstract
With the rapid increase in the number of flights all over the world, the management and control of flight operations has become difficult in recent years. Moreover, the expectations for the aviation sector indicate that this increase will continue in the upcoming years. Therefore, safer and systematic monitoring systems by eliminating the requirement of human-dependent tracking during the air travel of an aircraft and automating the detection of abnormal situations has become a major problem in aviation sector. With the recent advances in artificial intelligence, a safer and systematic tracking system for controlling the airspace by eliminating the need for human-dependent tracking during the flight of aircraft in the air has become possible.
In this study, we aimed to create a system that detects and predicts movements to indicate abnormal, dangerous situations in the airspace by monitoring radar flight data using machine learning and deep learning techniques. We applied two different methods, i.e., Proximity Based kNN and Auto Encoder We used real-life historical radar flight data set which consists of Flight Radar 24 data were converted from ADS-B messages for learning. We created simulation data and used this data for testing and validation for our trained model. Within the scope of this project, we also developed a system to monitor air traffic through radar tracks with our model and present the abnormal situations to the user through a visual interface for decision support. In this visualization, we present the abnormal situations if one of the algorithms labeled as anomaly. Results for both methods have shown that our findings were similar to the real-life predictions.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Akça, M., Pınar, R.İ.: Air traffic controller performance: a field study. Empirical Econ. Soc. Sci. 1, 68–82 (2019)
Pimentel, T., Monteiro, M., Viana, J., Veloso, V., Ziviani, N.: A Generalized active learning approach for unsupervised anomaly detection, Section 3 (2018)
Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. 41, 1–58 (2009)
Ester, M.; Kriegel, H.P.; Sander, J.; Xu, X.: A Density-based algorithm for discovering clusters in large spatial databases with noise, pp. 226–231 (1996)
Campello, R.J.G.B., Moulavi, D., Sander, J.: Density-based clustering based on hierarchical density estimates. In: Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu, G. (eds.) PAKDD 2013. LNCS (LNAI), vol. 7819, pp. 160–172. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-37456-2_14
Ankerst, M., Breunig, M.M., Kriegel, H.P., Sander, J.: OPTICS: ordering points to identify the clustering structure. ACM Sigmod. Record 28, 49–60 (1999)
Hartigan, J.A.; Wong, M.A.: Algorithm AS 136: a k-means clustering algorithm. J. R. Stat. Soc. Ser. C (Appl. Stat.) 28, 100–108 (1979)
Aggarwal, C.C.: Outlier ensembles: position paper. ACM SIGKDD Explor. Newslett. 14, 49 (2013). https://doi.org/10.1145/2481244.2481252
Breunig, M.M., Kriegel, H.P., Ng, R.T., Sander, J.: LOF: identifying density-based local outliers. ACM SIGKDD Explor. Newslett. 29, 93–104 (2000)
Liu, F.T.; Ting, K.M.; Zhou, Z.H.: Isolation forest. In Proceedings of the 2008 Eighth IEEE International Conference on Data Mining, pp. 413–422. IEEE, Pisa, Italy (2016) . https://doi.org/10.1109/ICDM.2008.17
Zhang, D., Li, N., Zhou, Z., Chen, C., Sun, L., Li, S.,: iBAT: detecting anomalous taxi trajectories from gps traces. In: Proceedings of the 13th international conference on Ubiquitous computing, pp. 99–108. ACM (2011)
Chen, C., et al.: iBOAT:Isolation-based online anomalous trajectory detection. IEEE Trans. Intell. Transp. Syst. 14(2), 806–818 (2013)
Wang, L., Mehrabi, M.G., Kannatey-Asibu, E., Jr.: Hidden markov model-based tool wear monitoring in turning. J. Manuf. Sci. Eng. 124, 651–658 (2002)
Biernacki, C., Celeux, G., Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Trans. Pattern Anal. Mach. Intell. 22, 719–725 (2000)
Kriegel, H., Schubert, M., Zimek, A.: Angle-based outlier detection in high-dimensional data. In: Proceedings of the 14th International Conference on Knowledge Discovery and Data Mining, pp. 444–452 (2008)
Pimentel, M.A., Clifton, D.A., Clifton, L., Tarassenko L.: A review of novelty detection, signal Process 99, 215–249 (2014). https://doi.org/10.1016/j.sigpro.2013.12.026
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (2013). https://doi.org/10.1007/978-1-4757-2440-0
Schölkopf, B., Williamson, R.C., Smola, A.J., Shawe-Taylor, J.; Platt, J.C.: Support vector method for novelty detection. In: Advances in Neural Information Processing Systems, MIT Press, pp. 582–588 (2000)
Piciarelli, C. and Foresti, G.L.: Anomalous trajectory detection using support vector machines. In 2007 IEEE Conference on Advanced Video and Signal Based Surveillance, pp. 153–158. IEEE (2007)
Das, S., Matthews, B.L., Srivastava, A.N., Oza, N.C.: Multiple kernel learning for heterogeneous anomaly detection: algorithm and aviation safety case study. In: Proceedings of the 16th ACM SIGKDD International Conference On Knowledge Discovery and Data Mining, pp. 47–56. ACM (2010)
Jolliffe, I.T: Principal Component Analysis, Springer Series in Statistics, Springer, New York (1986). https://doi.org/10.1007/978-1-4757-1904-8
Günter, S.; Schraudolph, N.N.; Vishwanathan, S.V.N.: Fast iterative kernel principal component analysis. J. Mach. Learn. Res. 8, 1893–1918 (2007)
Kramer, M.: Nonlinear principal component analysis using autoassociative neural networks. AIChE J. 37(2), 233–243 (1991)
Reddy, K.K., Sarkar, S., Venugopalan, V., Giering, M.: Anomaly detection and fault disambiguation in large flight data: a multi-modal deep auto-encoder approach, In: Proceedings of the Annual Conference of the Prognostics and Health Management Society, Denver, CO, USA (2016)
Kingma, D.P., Welling, M.: Auto-Encoding Variational Bayes, arXiv (2013). arXiv:1312.6114
Goodfellow, I., et al.: Generative adversarial nets. In: Advances in neural information processing systems, pp. 2672–2680 (2014)
Li, L., Hansman, R.J.: Anomaly Detection in Airline Routine Operations using Flight Data Recorder Data. MIT ICAT, Cambridge (2013)
Ramaswamy, S., Rastogi, R., Shim, K.: Efficient algorithms for mining outliers from large data sets. ACM Sigmod Rec. 29(2), 427–438 (2000)
Aggarwal, C.C.: Outlier analysis. In Data mining, pp. 237–263. Springer, Cham (2015)
Bentley, J.L.: Multidimensional binary search trees used for associative searching. ACM (1975). https://doi.org/10.1145/361002.361007
EUROCONTROL Page. https://www.eurocontrol.int/publication/cat021-eurocontrol-specification-surveillance-data-exchange-asterix-part-12-category-0
Taha, A., Hadi, A.: Anomaly detection methods for categorical data: a review. ACM Comput. Surv. 52, 1–35 (2019). https://doi.org/10.1145/3312739
Dave, D., Varma, T.: A review of various statistical methods for outlier detection. IJCSET 5(2), 137–140 (2014)
Acknowledgements
Osman Tasdelen extends his gratitude to Aselsan for their support and his colleagues for their cooperation and fruitful discussions. It was always helpful to exchange ideas about his research with his team mates.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Taşdelen, O., Çarkacioglu, L., Töreyin, B.U. (2021). Anomaly Detection on ADS-B Flight Data Using Machine Learning Techniques. In: Nguyen, N.T., Iliadis, L., Maglogiannis, I., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2021. Lecture Notes in Computer Science(), vol 12876. Springer, Cham. https://doi.org/10.1007/978-3-030-88081-1_58
Download citation
DOI: https://doi.org/10.1007/978-3-030-88081-1_58
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-88080-4
Online ISBN: 978-3-030-88081-1
eBook Packages: Computer ScienceComputer Science (R0)