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Dynamic Data-Driven Sensor Tasking with Applications in Space and Aerospace Systems

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Handbook of Dynamic Data Driven Applications Systems

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Abstract

This chapter discusses the Dynamic Data-Driven Applications Systems (DDDAS)-based mathematical approaches associated with the problem of managing a network of static and dynamic sensors to accurately characterize complex dynamical environments. Examples of sensor-network management include assigning a set of sensors for surveillance and tracking multiple ground/aerial/marine targets. Further examples include adaptive sensing of large-scale spatial phenomena such as weather, volcanic eruptions, and pollutants in air or large water bodies. By using information theoretic measures of sensor performance and the value of information predicted by the models, the formulation of optimally tasking a group of sensors to maximize mutual information is detailed. The resulting sensor-tasking optimization problem is shown to be combinatorial in nature, and its computational complexity expands with an increasing number of targets and sensors. Appropriate suboptimal approximations are presented to alleviate this computational complexity of the sensor-tasking problem. The submodular property of the mutual information measure is utilized to provide guarantees on the optimality of different approximations. Numerical simulations involve tracking of aerial and space objects with ground-based sensors and marine objects with sensors mounted on unmanned aerial vehicles. These simulations showcase the efficacy of optimizing the configurations of a limited number of sensor agents to better track multiple noncooperative targets.

This chapter is derived from our earlier publication [7].

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Acknowledgements

This work is based upon work supported by the AFOSR grant FA9550-15-1-0313. Drs. Erik P. Blasch and Frederica Darema are acknowledged for the technical discussions. The authors are also grateful to the anonymous reviewers. Their inputs enhanced the quality of the chapter extensively.

Author information

Authors and Affiliations

  1. Department of Mechanical & Aerospace Engineering, University of Alabama in Huntsville, Huntsville, AL, USA

    Nagavenkat Adurthi

  2. Aerospace Engineering, Pennsylvania State University, University Park, PA, USA

    Puneet Singla

  3. Aerospace Engineering, Director, Land, Air and Space Robotics Laboratory, Texas A & M University, College Station, TX, USA

    Manoranjan Majji

Authors
  1. Nagavenkat Adurthi
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  2. Puneet Singla
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  3. Manoranjan Majji
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Corresponding author

Correspondence to Puneet Singla .

Editor information

Editors and Affiliations

  1. InfoSymbiotic Systems Society, Bethesda, MD, USA

    Frederica Darema

  2. MOVEJ Analytics, Dayton, OH, USA

    Erik P. Blasch

  3. Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA

    Sai Ravela

  4. Information Directorate, Air Force Laboratory, Rome, NY, USA

    Alex J. Aved

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Adurthi, N., Singla, P., Majji, M. (2023). Dynamic Data-Driven Sensor Tasking with Applications in Space and Aerospace Systems. In: Darema, F., Blasch, E.P., Ravela, S., Aved, A.J. (eds) Handbook of Dynamic Data Driven Applications Systems. Springer, Cham. https://doi.org/10.1007/978-3-031-27986-7_10

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  • DOI: https://doi.org/10.1007/978-3-031-27986-7_10

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-031-27986-7

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