Optimal sequential estimation for multirate dynamic systems with unreliable measurements and correlated noise

In the field of target tracking and navigation, multi-sensor data fusion has been widely applied. Most of the data fusion algorithms are built on the premise that the sensor observation information is reliable. However, in practical problems, due to the limitation of communication and sensor fault, etc., data missing or unreliable measurements will happen inevitably. In addition, at present a lot of research is aimed at the situation where measurement noise between various sensors is not relevant, and process noise and measurement noise is irrelevant. Noise correlation is more practical. In this paper, a multi-rate multi-sensor data fusion state estimation algorithm with unreliable observations under correlated noises is presented. A numerical example is given to show the feasibility and effectiveness of the presented algorithm.