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To fuse information observed by asynchronous multirate sensors, a hybrid data fusion framework is presented. By use of the presented framework, information from different sensors may be fused effectively. To generate the optimal state estimate, the method is implemented by prediction and two times update in sequence. The information observed by the sensor with the highest sampling rate in the finest scale is used to update the state prediction, and the re-innovation is taken by use of the sensors with lower sampling rates at coarser scales. The process is carried out successively, and the fused state estimate at the finest scale is generated. The effectiveness of the algorithm is illustrated through theoretical proof and simulation results.