A new estimate method for linear constrained systems

This paper deals with the problem of optimal filtering for linear discrete constrained dynamic systems. The constraint matrix and constraint vector in this system are allowed to vary not only in the value but also in the dimension. Firstly, the original constrained state is transformed into a new reduced state model without constraint. Then, the prediction of the reduced state is given by using the least square method. Finally, the optimal estimate of original state is produced by the update process similar to the Kalman filter. A numerical example is presented to demonstrate the effectiveness of the new method.

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