Abstract: This paper presents criteria to evaluate different approaches of redundant data fusion; the criteria mostly concern the expectation and the maximum of the fusion error. A new fusion approach for multiple data is also presented, as the data have different accuracies. This approach is an extension of the weighted average. Through theoretic analysis, we obtain the formula to calculate the parameters for fusion of two uniform distribution data. Through Monte Carlo method, we get the parameters when we fuse more data or other distribution data. Our extended weighted method can fuse uniform data that cannot be handled by maximum likelihood approach. Its result is compared with other three representative fusion algorithms: maximum likelihood, optimal weighted average, and HILARE method. Comparison shows that our approach is better than all weighted average approaches; it has the smallest expectation and smallest maximum of the fusion error in all the four approaches.