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Discovering Probabilistic Models of Pilot Behavior from Aircraft Telemetry Data

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Advances in Chance Discovery

Part of the book series: Studies in Computational Intelligence ((SCI,volume 423))

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Abstract

A mobile ground-based sense-and-avoid system for Unmanned Aircraft System (UAS) operations was developed by the University of North Dakota. This system detected proximate aircraft with various sensor systems, including a 2D radar and an Automatic Dependent Surveillance – Broadcast (ADS-B) receiver. Information about those aircraft was then displayed to UAS operators with customized visualization software. Its risk mitigation subsystem was designed to estimate the current risk of midair collision for UAS operations below 18,000 feet MSL. However, accurate probabilistic models for the behavior of pilots of manned aircraft flying at these altitudes were needed before this subsystem could be implemented. In this paper the authors present the results of data mining a Flight Data Monitoring (FDM) data set from a consecutive 9 month period in 2011. Arbitrarily complex subpaths were discovered from the data set using an ant colony algorithm. Then, probabilistic models were data mined from those subpaths using extensions of the Genetic K-Means and Expectation-Maximization algorithms.

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Authors and Affiliations

  1. Department of Computer Science, University of North Dakota, Grand Forks, North Dakota, USA

    Kirk Ogaard & Ronald Marsh

Authors
  1. Kirk Ogaard
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  2. Ronald Marsh
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Correspondence to Kirk Ogaard .

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Editors and Affiliations

  1. The University of Tokyo, Hongo 7-3-1, Tokyo, 113-8656, Japan

    Yukio Ohsawa

  2. Faculty of Letters, Chiba University, Chiba, Japan

    Akinori Abe

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Ogaard, K., Marsh, R. (2013). Discovering Probabilistic Models of Pilot Behavior from Aircraft Telemetry Data. In: Ohsawa, Y., Abe, A. (eds) Advances in Chance Discovery. Studies in Computational Intelligence, vol 423. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30114-8_13

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  • DOI: https://doi.org/10.1007/978-3-642-30114-8_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-30113-1

  • Online ISBN: 978-3-642-30114-8

  • eBook Packages: EngineeringEngineering (R0)

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