Theory of Modeling and Simulation
Theory of Modeling and Simulation
Monte Carlo Bayesian Signal Processing for Wireless Communications
Journal of VLSI Signal Processing Systems
On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
DDDAS approaches to wildland fire modeling and contaminant tracking
Proceedings of the 38th conference on Winter simulation
Particle filters for positioning, navigation, and tracking
IEEE Transactions on Signal Processing
Monte Carlo filters for non-linear state estimation
Automatica (Journal of IFAC)
State estimation using particle filters in wildfire spread simulation
SpringSim '09 Proceedings of the 2009 Spring Simulation Multiconference
A dynamic data driven application system for wildfire spread simulation
Winter Simulation Conference
Estimation of new ignited fires using particle filters in wildfire spread simulation
Proceedings of the 44th Annual Simulation Symposium
Towards parameter estimation in wildfire spread simulation based on sequential Monte Carlo methods
Proceedings of the 44th Annual Simulation Symposium
Exploiting Sensor Spatial Correlation for Dynamic Data Driven Simulation of Wildfire
PADS '12 Proceedings of the 2012 ACM/IEEE/SCS 26th Workshop on Principles of Advanced and Distributed Simulation
Data assimilation using sequential monte carlo methods in wildfire spread simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
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Wildfire propagation is a complex process influenced by many factors. Simulation models of wildfire spread, such as DEVS-FIRE, are important tools for studying fire behavior. This paper presents how the sequential Monte Carlo methods, i.e., particle filters, can work together with DEVS-FIRE for better simulation and prediction of wildfire. We define an application framework of particle filters for the problem of wildfire spread using the DEVS-FIRE model, and discuss several applications. A case study example is provided and preliminary results are presented.