Machine learning methods in data fusion systems

In heterogeneous, multisensor and multitarget data fusion systems the notion of “levels” is used in order to divide the complex problem of discovering relationships between objects into parts which are easier to understand. In presented paper we consider classifiers as general feature generators, these algorithms are able to connect data from different sensors and different observations. The classifier increases the level of data abstraction, which simplifies the architecture of following system components in data fusion chain. A data fusion engine named DAFNE uses the presented paradigm in its classifier module. The module was implemented in Python and C++, the Naïve Bayesian and decision tree classifiers were used. The tests on simulated data shows improvement of data quality via fusion. The system design allowed to attain real-time processing with limited data volume.