Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Robust Functional Regression for Outlier Detection Read chapter Read first chapter

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

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

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.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Modelling Patient Sequences for Rare Disease Detection with Semi-supervised Generative Adversarial Nets
next chapter Unsupervised Anomaly Detection in Multivariate Spatio-Temporal Datasets Using Deep Learning
Footnotes
Literature
1.
2.
3.
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)
4.
5.
6.
7.
8.
9.
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.
Dalsnes, B.R., Hexeberg, S., Flåten, A.L., Eriksen, B.H., Brekke, E.F.: The neighbor course distribution method with Gaussian mixture models for AIS-based vessel trajectory prediction. In: 2018 21st International Conference on Information Fusion (FUSION), pp. 580–587, July 2018. https://​doi.​org/​10.​23919/​ICIF.​2018.​8455607
11.
Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. Commun. ACM 51(1), 107–113 (2008)CrossRef
12.
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.
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.
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.
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.
Korn, U.: A simple method for modelling changes over time. Casualty Actuarial Society E-Forum (2018)
17.
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)
18.
19.
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.
Murphy, K.P.: Machine Learning: A Probabilistic Perspective. The MIT Press, Cambridge (2012)MATH
21.
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
22.
23.
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.
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
25.
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
Book
Advanced Analytics and Learning on Temporal Data

Print ISBN: 978-3-030-39097-6

Electronic ISBN: 978-3-030-39098-3

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-39098-3_12

Premium Partner

    Image Credits