The BDBA selects the most discriminant basis. The SDFS first selects features, which are interpretable in terms of symbolic knowledge about the domain, from the most discriminant basis determined for each sensor separately. Then, it fuses these features into one combined feature vector. The SDFS uses formal languages to describe the domain and the sensing process. Models represent sensor data, operations on data, and relations among the data.
Theories represent symbolic knowledge about the domain and about the sensors.
Fusion is treated as a goal-driven operation of combining languages, models, and theories related to different sensors into one combined language, one combined model of the world and one combined theory. Z. Korona and M. M. Kokar. Proceedings of the 1996 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, 1996. Y. Eracar, C. A. DiMarzio, and M. M. Kokar. Using Theories and Models of the World.