Dynamic weighting fusion based on kalman filter

Abstract:

Multiple sensor measurement fusion can strip away the shortcomings of a single source which the information is not comprehensive in the process of radar track fusion. Weighted average fusion is widely used. The weighted average fusion of traditional and weights fixed can only combine with information from multiple sensors, but not pick out better information adaptively. Therefore, this paper suggests changing the fixed weight to dynamic weight. Before every fusion, calculating simple arithmetic average of multiple sensor measurements, then performing Kalman filter. Making the measurements subtract the values from Kalman filter. That is equivalent to make prediction for distinguishing data of stand or fall. And the dynamic weight is inversely proportional to the value using for prediction. Finally, the simulation experiments prove that the method in this paper can improve the precision of the fusion of target significantly.