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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Ming Xiang |
| Copyright Year | 2007 |
| Description | Author affiliation: Xi'an Jiaotong Univ., Xi'an (Ming Xiang) |
| Abstract | In this paper, we consider a parallel distributed detection network consisting of a fusion center and N sensors. We assume that the observations at different sensors are conditionally dependent, and optimize the system performance under the Neyman- Pearson criterion. Unlike previous papers dealing with the optimal N-P detection problem, we allow the sensor decision rules to be randomized, and obtain the necessary conditions for optimal fusion rule and sensor decision rules without making any assumptions on the joint density functions of sensor observations. The optimality conditions are obtained using an important property of points on the overall ROC curve that is established in the paper. And, a sufficient condition that guarantees the optimal sensor decision rules to be deterministic is also presented. |
| Starting Page | 1 |
| Ending Page | 6 |
| File Size | 1045800 |
| Page Count | 6 |
| File Format | |
| ISBN | 9780662458043 |
| DOI | 10.1109/ICIF.2007.4408033 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2007-07-09 |
| Publisher Place | Canada |
| Access Restriction | Subscribed |
| Rights Holder | ISIF |
| Subject Keyword | Sensor fusion Testing Density functional theory Sensor systems Constraint optimization Lagrangian functions Sensor phenomena and characterization Statistical analysis System performance Sufficient conditions ROC curves Distributed detection dependent sensor servations Neyman-Pearson criteion |
| Content Type | Text |
| Resource Type | Article |
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