STATE ESTIMATION OF LINEAR SYSTEMS WITH STATE EQUALITY CONSTRAINTS

Abstract This paper deals with the state estimation problem for linear systems with state equality constraints. Using noisy measurements which are available from the observable system, we construct the optimal estimate which also satisfies linear equality constraints. For this purpose, after reviewing modeling problems in linear stochastic systems with state equality constraints, we formulate a projected system representation. By using the constrained Kalman predictor for the projected system and comparing its predictor Ricccati Equation with those of the unconstrained and the projected Kalman predictors, we reach the conclusion that the current constrained estimator outperforms other filters for estimating the constrained system.

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