Sensor fusion places prime emphasis on associating, combining, and interpreting data from multiple sources and sensors in order to determine unknown properties and parameters of objects to be measured. Within the scope of tasks to be solved are knowledge acquisition concerning both the objects and the measuring process and construction of fusion algorithms, to extract information, that is not stored explicitly in the signal parameters, but exists implicitly in terms of coherences, redundancies, or inconsistencies among sensor signals. In this contribution the cooperative sensor fusion and its performance is shown by considering two examples, a Coriolis mass flow meter and the production of micro organisms in a bioreactor.
Print ISSN: 0171-8096
Volume: 71, 03/2004
Pages: 154 - 163