Abstract
The paper proposes an ontology-based multi-sensor data fusion model framework for the wide application of multi-sensor data fusion, which uses ontology as the semantics model of data in the feature level data fusion to solve the heterogeneous problem of multi-source data. In the framework, an effective data processing algorithm is presented to preserve a reliable confidence level for data in a dynamic environment based on the requirements of data timeliness in real-time data fusion systems. Considering the uncertainty of fuzzy information, Transferable Belief Model (TBM) is used in the decision level of data fusion to achieve multi-source heterogeneous distributed data fusion. Finally, the effectiveness of the fusion framework and algorithm is verified via an example instance of onboard sensors data fusion.
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