Source diversity and feature-level fusion

We briefly review the various models proposed for data fusion systems. A common theme of these models is the existence of multiple levels of processing within the data fusion process. We highlight some of the issues which emerge from using such a layered approach, in particular the selection of sources at each level which are both relevant and complementary. The balance between relevance and complementarity is shown to be present at all levels in the data fusion process. Each strand of processing cannot afford to rely too heavily on other information sources since the system needs to be robust to sensor or communications failures. For the purposes of illustration we develop a number of small data fusion systems which carry out simple fusion at the feature level. We use a multilayer perceptron neural network and show how a mixed error criterion which incorporates both local performance and fused performance leads to a selection of sources which is both relevant (in a local sense) and complementary (in a global sense)