Multi-sensor Optimal Information Fusion Estimation under One-step Random Sensor Delay

Sensor delay and observation uncertainty often randomly occur in modern computer based systems, e.g., when the measurement is transmitted to a remote controller through a network medium. In this paper, multi-sensor fusion estimation weighted by scalars under one-step random sensory delay is studied. According to information fusion criterion weighted by scalars in the linear minimum variance sense, a global optimal state estimator is obtained via fusion of local optimal state estimates for each single sensor subsystem, and it can minimize the effects of the communication network delay. A simulation example is given to show that the precision of the fusion estimation is superior to that based on each single sensor subsystem.