Distributed particle filters for sensor networks
Proceedings of the 3rd international symposium on Information processing in sensor networks
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Fast and Robust Background Updating for Real-time Traffic Surveillance and Monitoring
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Model-Based Techniques for Data Reliability in Wireless Sensor Networks
IEEE Transactions on Mobile Computing
Online Filtering, Smoothing and Probabilistic Modeling of Streaming data
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Decentralized sensor fusion with distributed particle filters
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
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Location estimation is an important part of a traffic surveillance system. Markov chain Monte Carlo methods based on particle filters have proved to be an effective solution in sensing error correction. We investigate in this paper the influence of particle filter parameters variation on sensing errors correction accuracy. Considered traffic surveillance system is based on a wireless sensor network. Several forms of probability density matrix and various methods for particle weight computation where considered, allowing us to find the dependencies between parameters. Finally, we use simulation to find optimal solutions in different traffic conditions.