Fusion of threshold rules for target detection in wireless sensor networks
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Feb 01, 2010
- Source ID
- 10.1145/1689239.1689248
Entities
People
- Mengxia Zhu
- N. S. V. Rao
- Qishi Wu
- R. R. Brooks
- Song Ding
- Sundaraja Sitharama Iyengar
Organizations
- Army Research Office
- Clemson University
- Louisiana State University
- Oak Ridge National Laboratory
- Southern Illinois University
- University of Memphis