Filter design is the signification foundation for system identification and state estimation.Based on the realization construction of state prediction and measurement update

Kalman filter can obtain the optimal estimation of state estimated under the linear minimum variance criterion

but the filtering precision is vulnerable to the random characteristics in single sensor condition.A novel realization structure of Kalman filter based on measurement lifting strategy is proposed in the paper.At first

virtual measurement is constructed on the basis of latest measurement and the prior statistical information of measurement noise modeling.Then

virtual measurements are reasonably sampled and fusion to modify the measurement reliability

and the estimation precision is improved.In addition

aiming to the algorithm requirements including real-time

precise and robustness in engineering application

the distributed weight fusion structure and the centralized consistency fusion are designed respectively.Finally

the theoretical analysis and experimental results show the feasibility and efficiency of algorithm proposed.