2012 ACM/IEEE/SCS 26th Workshop on Principles of Advanced and Distributed Simulation (2012)
July 15, 2012 to July 19, 2012
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/PADS.2012.17
Dynamic data driven simulation based on Particle Filter (PF) has been shown to increase the accuracy of wildfire spread simulation by assimilating real time sensor data into the simulation. An important issue in dynamic data driven simulation is to utilize the sensor data in an efficient and effective manner. In our previous work, all sensor readings are treated as independent from each other, however, when sensors are randomly deployed, measurement data from nearby sensors could be correlated and thus biased observation could be incurred. This paper presents a spatial correlation model to exploit sensor correlations from sensor spatial locations and inter-distance, and integrate it as part of the PF measurement model. Experiment results show that with the information of sensor correlation simulation accuracy is further increased.
Fires, Correlation, Data models, Data assimilation, Temperature sensors, Mathematical model, Temperature measurement, wildfire simulation, data assimilation, sensor spatial correlation
H. Xue and X. Hu, "Exploiting Sensor Spatial Correlation for Dynamic Data Driven Simulation of Wildfire," 2012 ACM/IEEE/SCS 26th Workshop on Principles of Advanced and Distributed Simulation(PADS), Zhangjiajie China, 2012, pp. 243-249.