DocumentCode :
3223752
Title :
A probabilistic chemical sensor model for data fusion
Author :
Robins, Peter ; Rapley, Veronica ; Thomas, Paul
Author_Institution :
Hazard Assessment, Simulation & Prediction Group, Dstl, Salisbury, UK
Volume :
2
fYear :
2005
fDate :
25-28 July 2005
Abstract :
For a data fusion system, taking data from chemical sensors and making inference about the source parameters of a chemical release in real time, a reversible probabilistic sensor model is proposed. Based on a simple ion mobility sensor with bar display, the proposed model represents a realistic sensor while yielding likelihood calculations fast enough to be used within a large inference engine. The sensor model and implementation strategy is described with initial results discussed in relation to a larger inference engine. The importance of approximating the integrals contained in the sensor models to ensure the model produces inference in the short time span required for real time inference is discussed and methods for improving the efficiency of the integral approximation proposed.
Keywords :
chemical sensors; chemical variables measurement; inference mechanisms; integration; polynomial approximation; sensor fusion; bar display; data fusion; inference engine; integral approximation; ion mobility sensor; likelihood calculation; probabilistic chemical sensor model; source parameter; Bayesian methods; Chemical hazards; Chemical sensors; Clouds; Engines; Fluctuations; Fuses; Predictive models; Sensor arrays; Sensor fusion; Bayesian inference; likelihood model; probabilistic sensor modelling;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Fusion, 2005 8th International Conference on
Print_ISBN :
0-7803-9286-8
Type :
conf
DOI :
10.1109/ICIF.2005.1591982
Filename :
1591982
Link To Document :
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