Asynchronous Multirate Multisensor Data Fusion Over Unreliable Measurements With Correlated Noise

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.