Theory of Modeling and Simulation
Theory of Modeling and Simulation
DDDAS approaches to wildland fire modeling and contaminant tracking
Proceedings of the 38th conference on Winter simulation
A wildland fire model with data assimilation
Mathematics and Computers in Simulation
Towards applications of particle filters in wildfire spread simulation
Proceedings of the 40th Conference on Winter Simulation
Integrated simulation and optimization for wildfire containment
ACM Transactions on Modeling and Computer Simulation (TOMACS)
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
A New Algorithm for Simulating Wildfire Spread through Cellular Automata
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Particle filters for positioning, navigation, and tracking
IEEE Transactions on Signal Processing
Mobility Tracking in Cellular Networks Using Particle Filtering
IEEE Transactions on Wireless Communications
Nonlinear Kalman Filtering Algorithms for On-Line Calibration of Dynamic Traffic Assignment Models
IEEE Transactions on Intelligent Transportation Systems
Cloud MapReduce for particle filter-based data assimilation for wildfire spread simulation
Proceedings of the High Performance Computing Symposium
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Assimilating real-time sensor data into large-scale spatial-temporal simulations, such as simulations of wildfires, is a promising technique for improving simulation results. This asks for advanced data assimilation methods that can work with the complex structures and nonlinear behaviors associated with the simulation models. This article presents a data assimilation framework using Sequential Monte Carlo (SMC) methods for wildfire spread simulations. The models and algorithms of the framework are described, and experimental results are provided. This work demonstrates the feasibility of applying SMC methods to data assimilation of wildfire spread simulations. The developed framework can potentially be generalized to other application areas where sophisticated simulation models are used.