Dependence in sensory data combination
It is common to assume sensor independence in the sensory data fusion and integration. The authors previously (1997, 1998) illustrated that the team consensus approach based on information entropy can remarkably improve the measurement accuracy. The major benefits of the approach are (a) the simple linear combination of the weighted initial expected estimates for each sensor; and (b) the low order bivariate likelihood functions which can be represented easily. In this paper, we demonstrate specifically both the positive and negative impacts of including dependent information in sensory data combination process; and show how the measurable consensus uncertainty level can be derived. A comparison of the team consensus approach with the Bayesian approach is presented.