Journal article
Optimal data fusion of correlated local decisions in multiple sensor detection systems
IEEE transactions on aerospace and electronic systems, v 28(3), pp 916-920
Jul 1992
Abstract
Z. Chair and P.R. Varshney (1986) solved the data fusion problem for fixed binary local detectors with statistically independent decisions. Their solution is generalized by using the Bahadur-Lazarsfeld expansion of probability density functions. The optimal data fusion rule is developed for correlation local binary decisions, in terms of the conditional correlation coefficients of all orders. It is shown that when all these coefficients are zero, the rule coincides with the original Chair-Varshney design.< >
Metrics
Details
- Title
- Optimal data fusion of correlated local decisions in multiple sensor detection systems
- Creators
- M Kam - Drexel UniversityQ Zhu - Drexel UniversityW.S Gray - Drexel University
- Publication Details
- IEEE transactions on aerospace and electronic systems, v 28(3), pp 916-920
- Publisher
- IEEE
- Resource Type
- Journal article
- Language
- English
- Web of Science ID
- WOS:A1992JM71000034
- Scopus ID
- 2-s2.0-0026892161
- Other Identifier
- 991019346806804721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Engineering, Aerospace
- Engineering, Electrical & Electronic
- Telecommunications