A regularized multi-dimensional data fusion technique

An uncertainty and data fusion approach was developed and tested. This fusion algorithm is based on the interaction between two constraints: (1) the principle of knowledge source corroboration, which tends to maximize the final belief in a given proposition (often modeled by a probability density function or fuzzy membership distribution), if either of the knowledge sources supports the occurrence of this proposition, and (2) the principle of belief enhancement/withdrawal, which adjusts the belief of one knowledge source according to the belief of the second knowledge source by maximizing the similarity between the two source outputs. This method has been tested using various features from synthetic and real data of various types of many dimensionalities, resulting fused data which satisfy both principles.<
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