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
Towards validation of DEVS-FIRE wildfire simulation model
Proceedings of the 2008 Spring simulation multiconference
Towards applications of particle filters in wildfire spread simulation
Proceedings of the 40th Conference on Winter Simulation
Efficient particle filtering using RANSAC with application to 3D face tracking
Image and Vision Computing
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
Monte Carlo filters for non-linear state estimation
Automatica (Journal of IFAC)
Verification & validation of an agent-based forest fire simulation model
SpringSim '10 Proceedings of the 2010 Spring Simulation Multiconference
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)
| Hi-index | 0.00 |
A fundamental issue in data assimilation of wildfire simulation is to estimate the dynamically changing states, e.g., the fire front position of wildfire, based on observation data of fire sensors. This is a challenging task because of the dynamic and non-linear behavior of fire spread. In this paper, we apply particles filters, also called sequential Monte Carlo methods, to data assimilation in wildfire simulation for estimating the dynamically evolving fire front of a spreading fire. The framework of applying particle filters to the DEVS-FIRE simulation model is presented. Preliminary experiment results show that the particle filtering algorithm was able to track the dynamically changing fire front based on fire sensor data, and thus to provide more accurate predictions of wildfire spread.