Sensor data monitoring and decision level fusion scheme for early fire detection
2017
The aim of this paper is to present the sensor monitoring and decision level fusion scheme for early fire detection which
has been developed in the context of the AF3 Advanced Forest Fire Fighting European FP7 research project, adopted
specifically in the OCULUS-Fire control and command system and tested during a firefighting field test in Greece with
prescribed real fire, generating early-warning detection alerts and notifications. For this purpose and in order to improve
the reliability of the fire detection system, a two-level fusion scheme is developed exploiting a variety of observation
solutions from air e.g. UAV infrared cameras, ground e.g. meteorological and atmospheric sensors and ancillary sources
e.g. public information channels, citizens smartphone applications and social media. In the first level, a change point
detection technique is applied to detect changes in the mean value of each measured parameter by the ground sensors
such as temperature, humidity and CO 2 and then the Rate-of-Rise of each changed parameter is calculated. In the second
level the fire event Basic Probability Assignment (BPA) function is determined for each ground sensor using Fuzzy-logic
theory and then the corresponding mass values are combined in a decision level fusion process using Evidential
Reasoning theory to estimate the final fire event probability.
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