Name
Abstract | ||
|---|---|---|
In this research, principal component analysis (PCA) and fuzzy principal component analysis (FPCA) are presented as tools for multisensor data fusion. PCA is a numerical procedure that transforms a number of correlated variables into a number of uncorrelated variables called principal components. We use these principal components (PCs) as weights for sensory data fusion. Based on the theory of fuzzy set, FPCA is used to perform data fusion. The proposed approach will be confirmed by using the simulations for signal fusion of a target tracking of a navigation application and fusion of thermal feedback system. The theoretical analysis and simulation results prove that FPCA gains an advantage over PCA for sensor data fusion. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ISSPA.2012.6310591 | 2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA) |
Keywords | Field | DocType |
fuzzy principal component analysis,FPCA,multisensor data fusion,numerical procedure,correlated variables,uncorrelated variables,fuzzy set theory,signal fusion simulations,target tracking,navigation application,thermal feedback system | Thermal feedback,Pattern recognition,Computer science,Fuzzy logic,Uncorrelated,Fusion,Sensor fusion,Fuzzy set,Artificial intelligence,Numerical analysis,Principal component analysis | Conference |
ISBN | Citations | PageRank |
978-1-4673-0381-1 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ghada Elbanby | 1 | 0 | 0.34 |
| Essam El Madbouly | 2 | 0 | 0.34 |
| Ahmed Abdalla | 3 | 0 | 0.34 |