Dependence in sensory data combination

It is common t o assume sensor independence in the sensory data fus ion and integration. In [l] and [2], we have illustrated that the t eam consensus approach based o n information entropy can remarkably improve the measurement accuracy. T h e major benefits of the approach are (a) the simple linear combination of the weighted initial expected estimates f o r 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 t eam consensus approach with the Bayesian approach is presented.