Name
Abstract | ||
|---|---|---|
A network of small distributed wireless sensors is scattered over an extended geographic area that is to be monitored. A scenario of moving object entered the area and need to be classified as a F riend or T arget is posed in this paper. Using Bayes theorem, sensors build beliefs around the object and classification decisions. As the object moves, sensors collaboratively choose a successor sensor and hand-off beliefs and classification decisions from the current active sensor to the successor. Sensor selection scheme is formulated as an Expected Value of Sample Information (EVSI) problem in the sequential Bayesian framework. Under the assumption that each measurement is independent and identically distributed-we extended the EVSI problem analytically to account for consecutive samples of information. The scheme presents dynamic classification-driven solution for sensor optimization problem under incomplete information and object's time varying dynamics assumptions. We model a multistep classification scenario for moving object using Bayes theorem.Extending Bayes theorem for multiple observations is presented.Sensor selection scheme is built on EVSI problem.Expanding EVSI analytically to account for a multiple sample of information is proposed. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1016/j.robot.2015.06.004 | Robotics and Autonomous Systems |
Keywords | Field | DocType |
Bayes theorem,EVSI,Sensor classification,Unattended ground vehicles,Sensor selection,Ad hoc network | Data mining,Wireless,Successor cardinal,Computer science,Artificial intelligence,Wireless ad hoc network,Optimization problem,Complete information,Bayes' theorem,Computer vision,Expected value of sample information,Algorithm,Bayesian probability | Journal |
Volume | Issue | ISSN |
72 | C | 0921-8890 |
Citations | PageRank | References |
1 | 0.37 | 11 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mariam Faied | 1 | 14 | 4.67 |