Chhadé, H.H., Abdallah, F., Mougharbel, I. et al. (3 more authors) (2014) Localisation of an Unknown Number of Land Mines Using a Network of Vapour Detectors. Sensors, 14 (11). 21000 - 21022 . ISSN 1424-8239
Abstract
We consider the problem of localising an unknown number of land mines using concentration information provided by a wireless sensor network. A number of vapour sensors/detectors, deployed in the region of interest, are able to detect the concentration of the explosive vapours, emanating from buried land mines. The collected data is communicated to a fusion centre. Using a model for the transport of the explosive chemicals in the air, we determine the unknown number of sources using a Principal Component Analysis (PCA)-based technique. We also formulate the inverse problem of determining the positions and emission rates of the land mines using concentration measurements provided by the wireless sensor network. We present a solution for this problem based on a probabilistic Bayesian technique using a Markov chain Monte Carlo sampling scheme, and we compare it to the least squares optimisation approach. Experiments conducted on simulated data show the effectiveness of the proposed approach.
Metadata
Authors/Creators: |
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Copyright, Publisher and Additional Information: | This is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
Keywords: | land mines localisation; advection-diffusion; inverse problem; Bayesianinference; Markov chain Monte Carlo; PCA |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 30 Oct 2015 12:37 |
Last Modified: | 30 Oct 2015 12:45 |
Published Version: | http://dx.doi.org/10.3390/s141121000 |
Status: | Published |
Publisher: | MDPI |
Refereed: | Yes |
Identification Number: | https://doi.org/10.3390/s141121000 |
Related URLs: |