Year: 2004 | ![Adobe Acrobat Reader Adobe Acrobat Reader](https://webarchive.library.unt.edu/web/20130216003603im_/http://www.lhncbc.nlm.nih.gov/lhc/images/lhc/misc/getacro.gif)
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2004-010 |
Dynamic Sensor Scheduling for Target Tracking; A Monte Carlo Sampling Approach |
Dr. Ying (Joanna) He |
2004-12-6 |
This presentation focuses on research on the problem of sensor scheduling for target tracking. The goal is to design a policy to determine which sensors to activate over time to trade off tracking performance with sensor usage costs. We approach this problem by formulating it as a partially observable Markov decision process (POMDP), and develop a Monte Carlo solution method using a combination of particle filtering for belief-state estimation and sampling-based Q-value approximation. |
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