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On theory of multisensor state fusion estimation, more research is a single rate synchronization problem, however, it is the multirate asynchronous problem often encountered in practice. Therefore, research on the state fusion estimation of asynchronous multirate multisensor have more practice application value. In this paple, by expand the dimension of the system state and measurements and by dividing them into proper data blocks, the multirate asynchronous sampling system is formalized into a synchronous sampling system with single sampling rate, therefore, by use of Kalman filter and Carlson optimal data fusion criterion, the optimal state fusion estimation in the sense of linear minimum variance is achieved. The experiment of state fusion estimation on radar tracking shows that this algorithm is better than the result of directed Kalman filtering on smallest scale, the estimation error is less than single sensor Kalman filtering. The method can also be used for integrated navigation, signal processing, image processing and many fields. Keywords-state fusion estimation; asynchronous; multirate; multisensor; Kalman filter

State Fusion Estimation Based on Asynchronous Multirate Multisensor