| Year: 2004 | 
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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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