In sensory data fusion and integration consideration, sensor independence is a common assumption. We demonstrate the impact of including dependent information in the sensory data combination process. The team consensus approach based on information entropy can improve the measurement accuracy remarkably. The major benefits of the approach are: (a) the simple linear combination of the weighted initial local estimates for each sensor; and (b) the low order bivariate likelihood functions which can be represented easily. A comparison of the team consensus approach with the Bayesian approach is presented.