Name
Abstract | ||
|---|---|---|
In our previous paper, we formalized an active information fusion framework based on dynamic Bayesian networks to provide active information fusion. This paper focuses on a central issue of active information fusion, i.e., the efficient identification of a subset of sensors that are most decision relevant and cost effective. Determining the most informative and cost-effective sensors requires an evaluation of all the possible subsets of sensors, which is computationally intractable, particularly when information-theoretic criterion such as mutual information is used. To overcome this challenge, we propose a new quantitative measure for sensor synergy based on which a sensor synergy graph is constructed. Using the sensor synergy graph, we first introduce an alternative measure to multisensor mutual information for characterizing the sensor information gain. We then propose an approximated nonmyopic sensor selection method that can efficiently and near-optimally select a subset of sensors for active fusion. The simulation study demonstrates both the performance and the efficiency of the proposed sensor selection method. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/TSMCB.2009.2021272 | IEEE Transactions on Systems, Man, and Cybernetics, Part B |
Keywords | Field | DocType |
information theoretic criterion,belief networks,efficient sensor selection,cost effective sensor,sensor information gain,situation awareness,dynamic bayesian network,sensor selection,bayesian networks (bns),approximated nonmyopic sensor selection,proposed sensor selection method,cost-effective sensor,information theory,sensor synergy,sensor synergy graph,active information fusion framework,active information fusion,graph theory,nonmyopic sensor selection method,multisensor mutual information,active fusion,mutual information,sensor fusion,bayesian network,transducers,computer simulation,algorithms,cost effectiveness,artificial intelligence,modeling,bayesian methods,availability,bayes theorem,information gain | Graph theory,Information theory,Data mining,Situation awareness,Computer science,Soft sensor,Sensor fusion,Artificial intelligence,Mutual information,Machine learning,Dynamic Bayesian network,Bayesian probability | Journal |
Volume | Issue | ISSN |
40 | 3 | 1941-0492 |
Citations | PageRank | References |
17 | 0.73 | 43 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yongmian Zhang | 1 | 381 | 18.78 |
| Qiang Ji | 2 | 2780 | 168.90 |