Dynamic Sensor Scheduling for Target Tracking; A Monte Carlo Sampling Approach

Date: December 06, 2004 Time: (All day)
Event Type: Lecture

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 Time: of particle filtering for belief-state estimation and sampling-based Q-value