A Bayesian Framework for Target Tracking in Sensor Networks
Cheng Yuanguo, Guohui Li · 2006
The work described in this paper presents a framework to track a moving target in sensor networks. The framework employs a method based on Bayesian classification to calculate the detection probabilities and proposes a tracking algorithm by defining the moving scope of a target, where the sensors have the higher values of detection probabilities. Experiment results show this scheme is effective.