Multimodal Data Fusion Using Canonical Variates Analysis Confusion Matrix Fusion
Erik Blasch, Asad Vakil, Jia Li, Robert L. Ewing · 2021
Data fusion from a variety of sources requires alignment, association, and analysis. One method to determine the relationship between two variables measuring the same information is a correlation analysis. The canonical variates analysis (CVA) supports the assessments of two sets of data. In this paper, we compare results from the fusion of histograms to that of the fusion of confusion matrices developed from data of the same modality and that of a cross modality. We use the Confusion Matrix Fusion (CMF) approach in the analysis and compare the results for EO/RF fusion. In the analysis, the Experiments, Scenarios, Concept of Operations, and Prototype Engineering (ESCAPE) data set is used for comparison to previous aerospace results.