State and disturbance estimators for systems with missing measurements and unknown disturbances

Uncertainty almost exists in the measurements of sensors because of the influence of environment and communication. The uncertainties can be reflected in the loss of measurement data and in the unknown disturbance added on the sensor measurements. In this paper, a linear unbiased minimum variance state filter is designed for discrete-time linear stochastic systems with data loss and unknown disturbance, where data loss phenomenon is described by a Bernoulli distributed random variable and there is not any prior information about the disturbance. The proposed filter is independent of the unknown disturbance. Further, a disturbance estimator is presented based on the state filter. A simulation example shows the effectiveness of the proposed results.