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Algorithms for Context Learning and Information Representation for Multi-Sensor Teams

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Context-Enhanced Information Fusion

Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

Abstract

Sensor measurements of the state of a system are affected by natural and man-made operating conditions that are not accounted for in the definition of system states. It is postulated that these conditions, called contexts, are such that the measurements from individual sensors are independent conditioned on each pair of system state and context. This postulation leads to kernel-based unsupervised learning of a measurement model that defines a common context set for all different sensor modalities and automatically takes into account known and unknown contextual effects. The resulting measurement model is used to develop a context-aware sensor fusion technique for multi-modal sensor teams performing state estimation. Moreover, a symbolic compression technique, which replaces raw measurement data with their low-dimensional features in real time, makes the proposed context learning approach scalable to large amounts of data from heterogeneous sensors. The developed approach is tested with field experiments for multi-modal unattended ground sensors performing human walking style classification.

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Acknowledgments

The work reported in this chapter has been supported in part by U.S. Air Force Office of Scientific Research (AFOSR) under Grant No. FA9550-12-1-0270 and by the Office of Naval Research (ONR) under Grant No N00014-11-1-0893. Any opinions, findings, and conclusions or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of the sponsoring agencies.

Author information

Authors and Affiliations

  1. Department of Mechanical and Nuclear Engineering, Pennsylvania State University, University Park, PA, USA

    Nurali Virani, Soumalya Sarkar & Asok Ray

  2. State College, University Park, PA, USA

    Ji-Woong Lee

  3. Applied Research Laboratory, Pennsylvania State University, University Park, PA, USA

    Shashi Phoha

Authors
  1. Nurali Virani
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  2. Soumalya Sarkar
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  3. Ji-Woong Lee
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  4. Shashi Phoha
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  5. Asok Ray
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Corresponding author

Correspondence to Shashi Phoha .

Editor information

Editors and Affiliations

  1. Department Mathematics and Computer Science, University of Udine, Udine, Italy

    Lauro Snidaro

  2. University Carlos III, Department of Computer Science and Engg. University Carlos III, Colmenarejo, Spain

    Jesús García

  3. Center Multisource Info. Fusion & Dept, Univ. at Buffalo, of Industrial and Engg Center Multisource Info. Fusion & Dept, Buffalo, New York, USA

    James Llinas

  4. Information Directorate, Air Force Research Laboratory Information Directorate, Rome, New York, USA

    Erik Blasch

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Virani, N., Sarkar, S., Lee, JW., Phoha, S., Ray, A. (2016). Algorithms for Context Learning and Information Representation for Multi-Sensor Teams. In: Snidaro, L., García, J., Llinas, J., Blasch, E. (eds) Context-Enhanced Information Fusion. Advances in Computer Vision and Pattern Recognition. Springer, Cham. https://doi.org/10.1007/978-3-319-28971-7_15

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  • DOI: https://doi.org/10.1007/978-3-319-28971-7_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28969-4

  • Online ISBN: 978-3-319-28971-7

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