AbstractThe problem of sensor integration and data fusion is addressed. We consider the problem of combining information from diversified sources in a coherent fashion. We assume that information from various sensors may be available in different forms at the fusion. For example, data from infrared (IR) sensors may be combined with range radar (RR) data and further combined with visual images. In each case, data and information from different sensors are presented in a different format which may not be directly compatible for all sensors. Part of the available information may be in the form of attributes and part in the form of dynamical measurements. A generalized evidence processing theory and an architecture for sensor integration and data fusion that accommodates diversified sources of information are presented. Data (or, more generically, information) fusion may take place at different levels, such as the level of dynamics, the level of attributes, and the level of evidence. The common and different aspects of fusion at the different levels are investigated and several practical examples of real world data fusion problems are discussed.