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In this paper, the problem of optimal state estimation is studied for fusion of asynchronous multirate multiscale sensors with unreliable measurements and correlated noise. The noise of different sensors is cross-correlated and coupled with the system noise of the previous step and the same time step. The system is described at the highest sampling rate with different sensors observing a single target independently with multiple sampling rates. An optimal state estimation algorithm based on iterative estimation of the white noise estimator is presented, which makes full use of the observation information effectively, overcomes the packet loss, data fault, unreliable factors, and improves the precision and the robustness of the system state estimation. A numerical example is used to illustrate the effectiveness of the presented algorithm. [ABSTRACT FROM PUBLISHER]

This paper is concerned with state estimation of a linear system by fusion of asynchronous, multirate, multisensor data. The system is described at the finest scale with multiple sensors observing a common thing independently at different scales with different sampling rates. Unreliable measurements are available. The main results are: the multiscale system is properly formulated, a credibility measure is constructed and used to determine whether the measurement is normal or faulty, and an optimal recursive fusion algorithm is presented. The effectiveness and stability of the algorithm under unreliable measurements are analyzed. A numerical example is given to show the feasibility and effectiveness of the presented algorithm. [ABSTRACT FROM PUBLISHER]