Abstract
We propose a binary decision fusion rule that reaches a global decision on the presence of a target by integrating local decisions made by multiple sensors. Without requiring a priori probability of target presence, the fusion threshold bounds derived using Chebyshev's inequality ensure a higher hit rate and lower false alarm rate compared to the weighted averages of individual sensors. The Monte Carlo-based simulation results show that the proposed approach significantly improves target detection performance, and can also be used to guide the actual threshold selection in practical sensor network implementation under certain error rate constraints.
Original language | English |
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Article number | 18 |
Journal | ACM Transactions on Sensor Networks |
Volume | 6 |
Issue number | 2 |
DOIs | |
State | Published - Feb 1 2010 |
Keywords
- Binary decision fusion
- Chebyshev inequality
- False alarm rate
- Hit rate
- ROC curve
- Wireless sensor network