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      Localisation of an Unknown Number of Land Mines Using a Network of Vapour Detectors

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          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.

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          Most cited references40

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          Inverse Problem Theory and Methods for Model Parameter Estimation

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            Machine Learning, 50(1/2), 5-43
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              Sensors: From biosensors to the electronic nose

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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                November 2014
                06 November 2014
                : 14
                : 11
                : 21000-21022
                Affiliations
                [1 ] University of Technology of Compiègne, rue Roger Couttolenc, Compiègne 60200, France; E-Mail: fahed.abdallah@ 123456hds.utc.fr
                [2 ] Lebanese University, Beirut, Lebanon; E-Mail: imadmoug@ 123456ul.edu.lb
                [3 ] Department of Computer Science, University College London, WC1E 6BT London, UK; E-Mails: e.gning@ 123456ucl.ac.uk (A.G.); s.julier@ 123456ucl.ac.uk (S.J.)
                [4 ] University of Sheffield, S1 3JD Sheffield, UK; E-Mail: l.s.mihaylova@ 123456sheffield.ac.uk
                Author notes

                External Editor: W. Rudolf Seitz

                [* ] Author to whom correspondence should be addressed; E-Mail: hiba.hajchhade@ 123456hds.utc.fr ; Tel.: +33-3-44-23-46-99.
                Article
                sensors-14-21000
                10.3390/s141121000
                4279522
                25384008
                496dc558-2b9c-411a-a6d3-edd35a722dcc
                © 2014 by the authors; licensee MDPI, Basel, Switzerland.

                This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license ( http://creativecommons.org/licenses/by/3.0/).

                History
                : 19 June 2014
                : 15 October 2014
                : 21 October 2014
                Categories
                Article

                Biomedical engineering
                land mines localisation,advection-diffusion,inverse problem,bayesian inference,markov chain monte carlo,pca

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