Adaptive Bayesian Sensor Motion Planning for Hazardous Source Term Recsontruction.pdf (1.94 MB)
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Adaptive Bayesian sensor motion planning for hazardous source term reconstruction

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journal contribution
posted on 13.11.2017 by Michael Hutchinson, Hyondong Oh, Wen-Hua Chen
There has been a strong interest in emergency planning in response to an attack or accidental release of harmful chemical, biological, radiological or nuclear substances. Under such circumstances, it is of paramount importance to determine the location and release rate of the hazardous source to forecast the future harm it may cause and employ methods to minimize the disturbance. In this paper, a sensor data collection strategy is proposed whereby an autonomous mobile sensor is guided to address such a problem with a high degree of accuracy and in a short amount of time. First, the parameters of the release source are estimated using the Markov chain Monte Carlo sampling approach. The most informative manoeuvre from the set of possible choices is then selected using the concept of maximum entropy sampling. Numerical simulations demonstrate the superior performance of the proposed algorithm compared to traditional approaches in terms of estimation accuracy and the number of measurements required.

Funding

This work was supported by the UK Engineering and Physical Sciences Research Council (EPSRC) Grant number EP/K014307/1 and the MOD University Defence Research Collaboration in Signal Processing.

History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Aeronautical and Automotive Engineering

Published in

IFAC-PapersOnLine

Volume

50

Issue

1

Pages

2812 - 2817

Citation

HUTCHINSON, M., OH, H. and CHEN, W-H., 2017. Adaptive Bayesian sensor motion planning for hazardous source term reconstruction. IFAC-Papers OnLine, 50(1), pp. 2812-2817.

Publisher

Elsevier / © International Federation of Automatic Control (IFAC)

Version

AM (Accepted Manuscript)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc/4.0/

Publication date

2017-10-18

ISSN

1474-6670

eISSN

2405-8963

Language

en

Exports