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2020 | OriginalPaper | Chapter

Extended Kalman Filter for Large Scale Vessels Trajectory Tracking in Distributed Stream Processing Systems

Authors : Katarzyna Juraszek, Nidhi Saini, Marcela Charfuelan, Holmer Hemsen, Volker Markl

Published in: Advanced Analytics and Learning on Temporal Data

Publisher: Springer International Publishing

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Abstract

The growing number of vessel data being constantly reported by a variety of remote sensors, such as the Automatic Identification System (AIS), requires new data analytics that can operate at high data rates and are highly scalable. Based on a real-world dataset from maritime transport, we propose a large scale vessel trajectory tracking application implemented in the distributed stream processing system Apache Flink. By implementing a state-space model (SSM) - the Extended Kalman Filter (EKF) - we firstly demonstrate that an implementation of SSMs is feasible in modern distributed data flow systems and secondly we show that we can reach a high performance by leveraging the inherent parallelization of the distributed system. In our experiments we show that the distributed tracking system is able to handle a throughput of several hundred vessels per ms. Moreover, we show that the latency to predict the position of a vessel is well below 500 ms on average, allowing for real-time applications.
Literature
3.
go back to reference Alessandrini, A., et al.: Mining vessel tracking data for maritime domain applications. In: 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW), pp. 361–367. IEEE (2016) Alessandrini, A., et al.: Mining vessel tracking data for maritime domain applications. In: 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW), pp. 361–367. IEEE (2016)
9.
go back to reference Brandt, T., Grawunder, M.: Moving object stream processing with short-time prediction. In: Proceedings of the 8th ACM SIGSPATIAL Workshop on GeoStreaming, pp. 49–56. ACM (2017) Brandt, T., Grawunder, M.: Moving object stream processing with short-time prediction. In: Proceedings of the 8th ACM SIGSPATIAL Workshop on GeoStreaming, pp. 49–56. ACM (2017)
10.
11.
go back to reference Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. Commun. ACM 51(1), 107–113 (2008) CrossRef Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. Commun. ACM 51(1), 107–113 (2008) CrossRef
12.
go back to reference He, B., et al.: Comet: batched stream processing for data intensive distributed computing. In: Proceedings of the 1st ACM Symposium on Cloud Computing, pp. 63–74. ACM (2010) He, B., et al.: Comet: batched stream processing for data intensive distributed computing. In: Proceedings of the 1st ACM Symposium on Cloud Computing, pp. 63–74. ACM (2010)
13.
go back to reference Jwo, D.J., Wang, S.H.: Adaptive fuzzy strong tracking extended Kalman filtering for GPS navigation. IEEE Sens. J. 7(5), 778–789 (2007) CrossRef Jwo, D.J., Wang, S.H.: Adaptive fuzzy strong tracking extended Kalman filtering for GPS navigation. IEEE Sens. J. 7(5), 778–789 (2007) CrossRef
14.
go back to reference Karimov, J., Rabl, T., Katsifodimos, A., Samarev, R., Heiskanen, H., Markl, V.: Benchmarking distributed stream data processing systems. In: 2018 IEEE 34th International Conference on Data Engineering (ICDE), pp. 1507–1518. IEEE (2018) Karimov, J., Rabl, T., Katsifodimos, A., Samarev, R., Heiskanen, H., Markl, V.: Benchmarking distributed stream data processing systems. In: 2018 IEEE 34th International Conference on Data Engineering (ICDE), pp. 1507–1518. IEEE (2018)
15.
go back to reference Kelly, A.: A 3D state space formulation of a navigation Kalman filter for autonomous vehicles. Technical report, Carnegie-Mellon University Pittsburgh PA Robotics Institute (1994) Kelly, A.: A 3D state space formulation of a navigation Kalman filter for autonomous vehicles. Technical report, Carnegie-Mellon University Pittsburgh PA Robotics Institute (1994)
16.
go back to reference Korn, U.: A simple method for modelling changes over time. Casualty Actuarial Society E-Forum (2018) Korn, U.: A simple method for modelling changes over time. Casualty Actuarial Society E-Forum (2018)
17.
go back to reference Lee, J.W., Kim, M.S., Kweon, I.S.: A Kalman filter based visual tracking algorithm for an object moving in 3D. In: Proceedings 1995 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human Robot Interaction and Cooperative Robots, vol. 1, pp. 342–347. IEEE (1995) Lee, J.W., Kim, M.S., Kweon, I.S.: A Kalman filter based visual tracking algorithm for an object moving in 3D. In: Proceedings 1995 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human Robot Interaction and Cooperative Robots, vol. 1, pp. 342–347. IEEE (1995)
19.
go back to reference Moussa, R.: Scalable maritime traffic map inference and real-time prediction of vessels’ future locations on apache spark. In: Proceedings of the 12th ACM International Conference on Distributed and Event-based Systems, pp. 213–216. ACM (2018) Moussa, R.: Scalable maritime traffic map inference and real-time prediction of vessels’ future locations on apache spark. In: Proceedings of the 12th ACM International Conference on Distributed and Event-based Systems, pp. 213–216. ACM (2018)
20.
go back to reference Murphy, K.P.: Machine Learning: A Probabilistic Perspective. The MIT Press, Cambridge (2012) MATH Murphy, K.P.: Machine Learning: A Probabilistic Perspective. The MIT Press, Cambridge (2012) MATH
21.
go back to reference Perera, L.P., Oliveira, P., Soares, C.G.: Maritime traffic monitoring based on vessel detection, tracking, state estimation, and trajectory prediction. IEEE Trans. Intell. Transp. Syst. 13(3), 1188–1200 (2012) CrossRef Perera, L.P., Oliveira, P., Soares, C.G.: Maritime traffic monitoring based on vessel detection, tracking, state estimation, and trajectory prediction. IEEE Trans. Intell. Transp. Syst. 13(3), 1188–1200 (2012) CrossRef
23.
go back to reference Sheng, C., Zhao, J., Leung, H., Wang, W.: Extended Kalman filter based echo state network for time series prediction using mapreduce framework. In: 2013 IEEE 9th International Conference on Mobile Ad-hoc and Sensor Networks, pp. 175–180. IEEE (2013) Sheng, C., Zhao, J., Leung, H., Wang, W.: Extended Kalman filter based echo state network for time series prediction using mapreduce framework. In: 2013 IEEE 9th International Conference on Mobile Ad-hoc and Sensor Networks, pp. 175–180. IEEE (2013)
24.
go back to reference Tu, E., Zhang, G., Rachmawati, L., Rajabally, E., Huang, G.B.: Exploiting AIS data for intelligent maritime navigation: a comprehensive survey from data to methodology. IEEE Trans. Intell. Transp. Syst. 19(5), 1559–1582 (2017) CrossRef Tu, E., Zhang, G., Rachmawati, L., Rajabally, E., Huang, G.B.: Exploiting AIS data for intelligent maritime navigation: a comprehensive survey from data to methodology. IEEE Trans. Intell. Transp. Syst. 19(5), 1559–1582 (2017) CrossRef
Metadata
Title
Extended Kalman Filter for Large Scale Vessels Trajectory Tracking in Distributed Stream Processing Systems
Authors
Katarzyna Juraszek
Nidhi Saini
Marcela Charfuelan
Holmer Hemsen
Volker Markl
Copyright Year
2020
DOI
https://doi.org/10.1007/978-3-030-39098-3_12

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