In this paper, we describe an uncertainty and data fusion approach that we have developed and tested. This new 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. These two principles are combined by maximizing a positive linear combination of these two constraints related by a fusion function, to be determined. The latter maximization is achieved and the fusion function is uniquely determined using the Euler-Lagrange equations in Calculus of Variations. This method has been tested using various features from synthetic and real data of various types and of many dimensionalities resulting in fused data which satisfies both principles mentioned above. Through these experiments, we have demonstrated the synergism that results through the combination of information available from various sensors. The implementation of this method was performed on both sequential and parallel machines.
M. A. Abidi,
"Sensor Fusion: A New Approach And Its Applications", Proc. SPIE 1198, Sensor Fusion II: Human and Machine Strategies, (1 March 1990); doi: 10.1117/12.969979; https://doi.org/10.1117/12.969979