Name
Title | ||
|---|---|---|
Approximate stochastic dynamic programming for sensor scheduling to track multiple targets |
Abstract | ||
|---|---|---|
The problem of sensor scheduling is to select the number and combination of sensors to activate over time. The goal is usually to trade off tracking performance and sensor usage. We formulate a version of this problem involving multiple targets as a partially observable Markov decision process, and use this formulation to develop a nonmyopic sensor-scheduling scheme. Our scheme integrates sequential multisensor joint probabilistic data association and particle filtering for belief-state estimation, and use a simulation-based Q-value approximation method called completely observable rollout for decision making. We illustrate the effectiveness of our approach by an example with multiple sensors activated simultaneously to track multiple targets. We also explore the trade-off between tracking error and sensor cost using our nonmyopic scheme. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1016/j.dsp.2007.05.004 | Digital Signal Processing |
Keywords | Field | DocType |
multiple target tracking,observable rollout,partially observable markov decision process,stochastic dynamic programming,observable markov decision process,multiple sensor,sensor scheduling,nonmyopic sensor-scheduling scheme,sensor usage,particle filtering,belief-state estimation,multiple target,sensor cost,approximate stochastic dynamic programming,nonmyopic scheme,particle filter | Mathematical optimization,Observable,Partially observable Markov decision process,Scheduling (computing),Particle filter,Data association,Probabilistic logic,Stochastic programming,Mathematics,Tracking error | Journal |
Volume | Issue | ISSN |
19 | 6 | Digital Signal Processing |
Citations | PageRank | References |
24 | 1.22 | 6 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yun Li | 1 | 69 | 3.11 |
| L. W. Krakow | 2 | 24 | 1.22 |
| E. K. P. Chong | 3 | 47 | 3.91 |
| Kenneth Groom | 4 | 33 | 2.82 |