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Localisation of an Unknown Number of Land Mines Using a Network of Vapour Detectors
- Source :
- Sensors, Sensors, MDPI, 2014, 14(11), pp.21000-21022. ⟨10.3390/s141121000⟩, Sensors, Vol 14, Iss 11, Pp 21000-21022 (2014), Sensors (Basel, Switzerland), Volume 14, Issue 11, Pages 21000-21022
- Publication Year :
- 2014
- Publisher :
- HAL CCSD, 2014.
-
Abstract
- International audience; We consider the problem of localising an unknown number of land mines usingconcentration information provided by a wireless sensor network. A number of vapoursensors/detectors, deployed in the region of interest, are able to detect the concentrationof the explosive vapours, emanating from buried land mines. The collected data iscommunicated to a fusion centre. Using a model for the transport of the explosive chemicalsin the air, we determine the unknown number of sources using a Principal ComponentAnalysis (PCA)-based technique. We also formulate the inverse problem of determiningthe positions and emission rates of the land mines using concentration measurementsprovided by the wireless sensor network. We present a solution for this problem basedon a probabilistic Bayesian technique using a Markov chain Monte Carlo sampling scheme,and we compare it to the least squares optimisation approach. Experiments conducted onsimulated data show the effectiveness of the proposed approach.
- Subjects :
- Bayesianinference
[SPI.OTHER]Engineering Sciences [physics]/Other
Explosive material
Computer science
Bayesian inference
computer.software_genre
lcsh:Chemical technology
Biochemistry
Least squares
Article
Analytical Chemistry
symbols.namesake
[SPI]Engineering Sciences [physics]
[STAT.ML]Statistics [stat]/Machine Learning [stat.ML]
[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing
lcsh:TP1-1185
Electrical and Electronic Engineering
Instrumentation
land mines localisation
PCA
advection-diffusion
Detector
Probabilistic logic
Markov chain Monte Carlo
Inverse problem
Atomic and Molecular Physics, and Optics
Principal component analysis
symbols
inverse problem
Data mining
Wireless sensor network
computer
Subjects
Details
- Language :
- English
- ISSN :
- 14248220 and 14248239
- Database :
- OpenAIRE
- Journal :
- Sensors, Sensors, MDPI, 2014, 14(11), pp.21000-21022. ⟨10.3390/s141121000⟩, Sensors, Vol 14, Iss 11, Pp 21000-21022 (2014), Sensors (Basel, Switzerland), Volume 14, Issue 11, Pages 21000-21022
- Accession number :
- edsair.doi.dedup.....59503426a6d9e597d4213e0b94467c8d
- Full Text :
- https://doi.org/10.3390/s141121000⟩