State estimation for asynchronous multirate multisensor dynamic systems with missing measurements

This study is concerned with the state estimation problem for a kind of asynchronous multirate multisensor dynamic system, where observations from different sensors are randomly missing. The system is described at the highest sampling rate with different sensors observing a single target independently with multiple sampling rates. The optimal state estimate is obtained by use of the multiscale system theory and the modified Kalman filter. This study extends the federated Kalman filter to the case of asynchronous multirate multisensor dynamic systems with measurements randomly missing. The presented algorithm is proven to be effective in the sense of linear minimum mean squared error. The feasibility and efficiency of the algorithm are illustrated by a numerical simulation example.