Distributive target tracking in sensor networks with a Markov random field model
Document Type
Conference Proceeding
Publication Date
12-11-2009
Abstract
Tracking in sensor networks has shown great potentials in many real world surveillance and emergency system. Due to the distributive nature and unpredictable topology structure of the randomly distributed sensor network, a good tracking algorithm must be able to aggregate large amounts of data from various unknown sources. In this paper, a distributive tracking algorithm is developed using a Markov random field (MRF) model to solve this problem. The Markov random field (MRF) utilizes probability distribution and conditional independency to identify the most relevant data from the less important data. The algorithm converts the randomly distributed network into a regularly distributed topology structure using cliques. This makes tracking in the randomly distributed network topology simple and more predictable. Simulation demonstrate that the algorithm performs well for various sensor field setting, and for various target sizes. © 2009 IEEE.
Publication Title
2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009
Recommended Citation
Shi, L.,
&
Tan, J.
(2009).
Distributive target tracking in sensor networks with a Markov random field model.
2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009, 854-859.
http://doi.org/10.1109/IROS.2009.5354756
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/10666