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.
Original language | English |
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Pages (from-to) | 21000-21022 |
Number of pages | 23 |
Journal | Sensors (Switzerland) |
Volume | 14 |
Issue number | 11 |
DOIs | |
Publication status | Published - 6 Nov 2014 |
Externally published | Yes |
Bibliographical note
Publisher Copyright:2014 by the authors; licensee MDPI, Basel, Switzerland.
Keywords
- Advection-diffusion
- Bayesian inference
- Inverse problem
- Land mines localisation
- Markov chain Monte Carlo
- PCA