skip to main content
DOE PAGES title logo U.S. Department of Energy
Office of Scientific and Technical Information

This content will become publicly available on July 19, 2020

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]
+ Show Author Affiliations
  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; Project No. 17‐ERD‐101; DE‐AC52‐07NA27344; DE‐AC05‐00O; DE‐NA0002576]
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.
Copy to clipboard
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. doi:10.1002/sam.11431.
Copy to clipboard
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. doi:10.1002/sam.11431.
Copy to clipboard
@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}
}
Copy to clipboard
Journal Article:
Free Publicly Available Full Text
This content will become publicly available on July 19, 2020
Publisher's Version of Record
DOI:  10.1002/sam.11431
Copyright Statement
Other availability
Search WorldCat Search WorldCat to find libraries that may hold this journal
Save / Share:
Export Metadata
Save to My Library
You must Sign In or Create an Account in order to save documents to your library.

Works referenced in this record:

Coverage Control for Mobile Sensing Networks
journal, April 2004

  • Cortes, J.; Martinez, S.; Karatas, T.
  • IEEE Transactions on Robotics and Automation, Vol. 20, Issue 2
  • DOI: 10.1109/TRA.2004.824698

Hybrid optimization and Bayesian inference techniques for a non-smooth radiation detection problem: INFERENCE TECHNIQUES FOR A RADIATION DETECTION PROBLEM
journal, February 2017

  • Ştefănescu, Răzvan; Schmidt, Kathleen; Hite, Jason
  • International Journal for Numerical Methods in Engineering, Vol. 111, Issue 10
  • DOI: 10.1002/nme.5491

An urban environment simulation framework for evaluating novel distributed radiation detection architectures
conference, November 2010

  • King, Michael J.; Harris, Bernard; Toolin, Maurice
  • 2010 IEEE International Conference on Technologies for Homeland Security (HST)
  • DOI: 10.1109/THS.2010.5654958

Mobility improves coverage of sensor networks
conference, January 2005

  • Liu, Benyuan; Brass, Peter; Dousse, Olivier
  • Proceedings of the 6th ACM international symposium on Mobile ad hoc networking and computing - MobiHoc '05
  • DOI: 10.1145/1062689.1062728

Evaluation of a Puff Dispersion Model in Complex Terrain
journal, March 1992

Bayesian inverse modeling of the atmospheric transport and emissions of a controlled tracer release from a nuclear power plant
journal, January 2017

  • Lucas, Donald D.; Simpson, Matthew; Cameron-Smith, Philip
  • Atmospheric Chemistry and Physics, Vol. 17, Issue 22
  • DOI: 10.5194/acp-17-13521-2017

Detection and parameter estimation of multiple radioactive sources
conference, July 2007

  • Morelande, Mark; Ristic, Branko; Gunatilaka, Ajith
  • 2007 10th International Conference on Information Fusion
  • DOI: 10.1109/ICIF.2007.4408094

An Information-Theoretic Approach to Optimally Calibrate Approximate Models
conference, November 2012

  • Bryant, Corey; Terejanu, Gabriel
  • 50th AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition
  • DOI: 10.2514/6.2012-153

False Discovery Rate Based Sensor Decision Rules for the Network-Wide Distributed Detection Problem
journal, July 2011

  • Ray, Priyadip; Varshney, Pramod K.
  • IEEE Transactions on Aerospace and Electronic Systems, Vol. 47, Issue 3
  • DOI: 10.1109/TAES.2011.5937265

Bayesian experimental design for the active nitridation of graphite by atomic nitrogen
journal, January 2012

Sensors on patrol (SOP): using mobile sensors to detect potential airborne nuclear, biological, and chemical attacks
conference, January 2005

  • Zhang, T.; Madhani, S.; van den Berg, E.
  • MILCOM 2005. 2006 IEEE Military Communications Conference, MILCOM 2005 - 2005 IEEE Military Communications Conference
  • DOI: 10.1109/MILCOM.2005.1606107

The spread of smoke and gases from chimneys
journal, January 1936

  • Bosanquet, C. H.; Pearson, J. L.
  • Transactions of the Faraday Society, Vol. 32
  • DOI: 10.1039/tf9363201249

Estimating mutual information
journal, June 2004

Maximizing the Information Content of Experiments in Systems Biology
journal, January 2013

Global risk from the atmospheric dispersion of radionuclides by nuclear power plant accidents in the coming decades
journal, January 2014

  • Christoudias, T.; Proestos, Y.; Lelieveld, J.
  • Atmospheric Chemistry and Physics, Vol. 14, Issue 9
  • DOI: 10.5194/acp-14-4607-2014

Inverse calculation approaches for source determination in hazardous chemical releases
journal, July 2011

  • Zheng, Xiaoping; Chen, Zengqiang
  • Journal of Loss Prevention in the Process Industries, Vol. 24, Issue 4
  • DOI: 10.1016/j.jlp.2011.01.002

An information theoretic approach to use high-fidelity codes to calibrate low-fidelity codes
journal, November 2016

    [ × clear filter / sort ]

    Similar Records in DOE PAGES and OSTI.GOV collections: