Reduced Order Estimation
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When dealing with large scale systems, sometimes it is not necessary to estimate the complete state vector. Rather, one might be interested in only some state variables or a linear combination of the state vector which is of smaller dimension than the original system. In this case it is not economical, and maybe, not feasible to design a full order Kalman filter. It is more attractive from at least computational and economical reasons to design a reduced order filter. The objective here is to design such reduced-order filters to estimate a set of desired variables. This problem was addressed by many investigators. For -example, in (1] the authors derived an unbiased filter provided that the desired and the measurable variables satisfy some rank conditions. The procedure presented here is based on an appropriate Ressenberg [21 representation. The desired variables are viewed as the states of a subsystem driven by the interface variables. Additional measurements on these interface variables are required to obtain an unbiased filter. Conditions for the stability of the filter are derived6