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State estimation in the case of loss of observations

Loss of information (observations) is a common problem in control and communication systems. Kalman filter is a versatile tool for state estimation, but would it still produce accurate estimation in such a case? In this paper we investigate this situation and propose several approaches to compensate the loss of information in employing Kalman filter to estimate the state of a system. Minimum error variance for these approaches is derived from the basic structure of the classical Kalman filer. Necessary discussion for all approaches regarding their applications and drawbacks are stated. Optimal Kalman gain matrix for these approaches is calculated. Selection criterion for the approaches are also presented. Details of the theoretical properties such as convergence and stabilization of Riccati equation are not, however, included due to limited length of a conference paper. Numerical example is included to illustrate the effectiveness of these approaches.