Consider the setting of Completion of Squares for the Linear-Quadratic-Gaussian Cost: a linear-Gaussian state-observation model on , a control dimension , a control matrix assignment , cost data with every positive definite, a symmetric continuous solution of the backward Riccati equation as there, and the feedback gain defined there. Let and be the fundamental solution of and its inverse from Variation of Constants for Linear Systems, and let be the filter process of The Kalman-Bucy Filter Equation and Its Solution. Then:
1. (Closed-loop estimator) There is a family of -tuples of square-integrable random variables, with mean-square continuous component families, satisfying, componentwise and almost surely,
with the mean-square Riemann integral taken componentwise; any two families of -tuples of square-integrable random variables with mean-square continuous component families satisfying this identity agree almost surely at each time. Moreover each lies in the closed mean-square span of the family consisting of the constant and the values (, ), and is almost surely equal to a -measurable square-integrable random variable.
2. (The feedback control and its estimator) The family with (componentwise matrix-vector product) is an admissible control with values in , and its controlled estimator satisfies almost surely, componentwise, for every ; consequently almost surely for every .
3. (Self-consistency) Let and be the controlled observations and controlled observation -algebras for . Then for every : each , each with , and each lies in the closed mean-square span of the family consisting of the constant and the values (, ), and each is almost surely equal to a -measurable random variable. In particular the feedback control is determined by its own observations.
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