Asynchronous Multirate Multisensor State Fusion Estimation with Incomplete Measurements

Asynchronous multirate multisensor data fusion with measurements partially missing is studied in this paper and an effective state estimation algorithm is presented. The measurements are assumed missing stochastically with a certain probability. The algorithm is fulfilled by missing measurements checking and then data fusion. System model is used to deduce the rule for checking measurements missing or not. In state fusion estimation, multiscale system theory and filter design are used to connect measurements observed by different sensors with different sampling rates, and Kalman filter is used in each updating step. Theoretical analysis and simulation results show the effectiveness of the algorithm.