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Title: Sequential optimal positioning of mobile sensors using mutual information

Abstract

Source localization, such as detecting a nuclear source in an urban area or ascertaining the origin of a chemical plume, is generally regarded as a well-documented inverse problem; however, optimally placing sensors to collect data for such problems is a more challenging task. In particular, optimal sensor placement—that is, measurement locations resulting in the least uncertainty in the estimated source parameters—depends on the location of the source, which is typically unknown a priori. Mobile sensors are advantageous because they have the flexibility to adapt to any given source position. While most mobile sensor strategies designate a trajectory for sensor movement, we instead employ mutual information, based on Shannon entropy, to choose the next measurement location from a discrete set of design conditions.

Authors:
ORCiD logo [1];  [2];  [2];  [2];  [2];  [1];  [1]
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  1. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
  2. North Carolina State Univ., Raleigh, NC (United States)
Publication Date:
Research Org.:
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1548374
Alternate Identifier(s):
OSTI ID: 1543170
Report Number(s):
LLNL-JRNL-753008
Journal ID: ISSN 1932-1864; 939255
Grant/Contract Number:  
AC52-07NA27344; DE‐AC52‐07NA27344; DE‐AC05‐00O
Resource Type:
Accepted Manuscript
Journal Name:
Statistical Analysis and Data Mining
Additional Journal Information:
Journal Volume: 12; Journal Issue: 6; Journal ID: ISSN 1932-1864
Publisher:
Wiley
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; Bayesian inference; inverse problem; mutual information; sensor placement; source localization

Citation Formats

Schmidt, Kathleen, Smith, Ralph C., Hite, Jason, Mattingly, John, Azmy, Yousry, Rajan, Deepak, and Goldhahn, Ryan. Sequential optimal positioning of mobile sensors using mutual information. United States: N. p., 2019. Web. doi:10.1002/sam.11431.
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Schmidt, Kathleen, Smith, Ralph C., Hite, Jason, Mattingly, John, Azmy, Yousry, Rajan, Deepak, & Goldhahn, Ryan. Sequential optimal positioning of mobile sensors using mutual information. United States. https://doi.org/10.1002/sam.11431
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Schmidt, Kathleen, Smith, Ralph C., Hite, Jason, Mattingly, John, Azmy, Yousry, Rajan, Deepak, and Goldhahn, Ryan. Fri . "Sequential optimal positioning of mobile sensors using mutual information". United States. https://doi.org/10.1002/sam.11431. https://www.osti.gov/servlets/purl/1548374.
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@article{osti_1548374,
title = {Sequential optimal positioning of mobile sensors using mutual information},
author = {Schmidt, Kathleen and Smith, Ralph C. and Hite, Jason and Mattingly, John and Azmy, Yousry and Rajan, Deepak and Goldhahn, Ryan},
abstractNote = {Source localization, such as detecting a nuclear source in an urban area or ascertaining the origin of a chemical plume, is generally regarded as a well-documented inverse problem; however, optimally placing sensors to collect data for such problems is a more challenging task. In particular, optimal sensor placement—that is, measurement locations resulting in the least uncertainty in the estimated source parameters—depends on the location of the source, which is typically unknown a priori. Mobile sensors are advantageous because they have the flexibility to adapt to any given source position. While most mobile sensor strategies designate a trajectory for sensor movement, we instead employ mutual information, based on Shannon entropy, to choose the next measurement location from a discrete set of design conditions.},
doi = {10.1002/sam.11431},
journal = {Statistical Analysis and Data Mining},
number = 6,
volume = 12,
place = {United States},
year = {2019},
month = {7}
}
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Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
https://doi.org/10.1002/sam.11431
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Figures / Tables:

Figure 1 Figure 1: Satellite image of the problem geometry, source location, and stationary detector positions from [26]. Satellite imagery is c©2015 Commonwealth of Virginia, DigitalGlobe, District of Columbia (DC GIS), Sanborn, as well as the U.S. Geological Survey and is provided by Google Maps. Building footprints shown in satellite overlays ismore » c©2015 OpenStreetMaps contributors and is publicly available under the terms of the Open Database License.« less
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    Works referencing / citing this record:

    Application and Evaluation of Surrogate Models for Radiation Source Search
    journal, December 2019

    • Cook, Jared A.; Smith, Ralph C.; Hite, Jason M.
    • Algorithms, Vol. 12, Issue 12
    • DOI: 10.3390/a12120269
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      Figures / Tables found in this record:

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      Figure 3 (p. 8)figure
      Algorithm 1 (p. 9)figure
      Figure 4 (p. 11)figure
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