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

  • 2020
  • OriginalPaper
  • Chapter
Published in:
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.

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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
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
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