A class of state fusion estimation algorithm for multirate multisensor systems

Based on mulitsensor single model dynamic systems, a state fusion estimation algorithm is presented. Multisensors observe the same target, where different sensors may have different sampling rates and the ratio between them may be positive rational numbers. The algorithm is in real-time, and the optimal in the sense of linear minimum covariance. It is proved that the fused estimate is more accurate than the Kalman filtering result based on single sensors. The fused estimation error covariance will increase if any of the sensors' information is neglected. The feasibility and effectiveness of the algorithm are shown through simulation results.