A data fusion based on recursive estimation and its application

For the data from the noisy observations, we estimate the amount of a non-random, a data fusion based on recursive estimation algorithm is proposed in this paper. First, considering the single sensor in temporal iterative, the suboptimal value is obtained. Then using the changes in variance of all the sensors, in the minimum mean square error condition, the optimal values of the overall data are obtained by adjusting its weighted coefficient of each sensor. Comparing with the adaptive weighted data fusion algorithm and the consensus data fusion algorithm, simulation results and its applications show that the algorithm is of high accuracy and has better robustness.