Multi Level Fusion with Fuzzy Operators using Confidence

The paper presents a methodology for using fuzzy operators for the hierarchical fusion of processing results in a multi sensor data processing system. Tracking and fusion of intermediate results is performed in several levels of processing (signal level, several feature levels, object level). To produce higher level hypotheses on the basis of lower level components, grouping rules using certain assignment decisions are used. In this paper this is seen as a classification procedure that is step by step testing and assigning components to a higher level feature or object. For these classifications a suitable combination of a fuzzy operator for fusion and membership functions for classification is proposed to meet the requirements of the hierarchical classification and the necessity to include confidence values for that. Especially the dependencies between the n-fold one-dimensional classification and the n-dimensional classification is addressed. We use a straightforward example to demonstrate the concept of the multi level fusion and classification procedure