Abstract: A dynamic timevary linear system is studied. An effective data fusion algorithm is presented in times of multiple sensors observing a single target with different sampling rates. The robustness of the algorithm in case of data missing is also considered, where measurements from each sensor are missing stochastically with certain probabilities. By technical processing, the multirate data fusion system is transformed into a single rate linear dynamic system. By use of the modified federated Kalman filter to the newly established system, the state estimation is obtained. The augmentation of state or measurement dimensions are avoided by use of the presented algorithm, and the realtime property is guaranteed. In times of measurements missing, the observation is replaced by the predicted one, and the divergence of the traditional Kalman filter is omitted. Theoretical analysis and simulation results show the effectiveness of the proposed algorithm.