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In this article, we present a structured Kalman filter associated with thetransformation matrix for observable Kalman canonical decomposition fromconventional Kalman filter (CKF) in order to generate a more accurate timescale. The conventional Kalman filter is a special case of the proposedstructured Kalman filter which yields the same predicted unobservable orobservable states when some conditions are satisfied. We consider anoptimization problem respective to the transformation matrix where theobjective function is associated with not only the expected value of predictionerror but also its variance. We reveal that such an objective function is aconvex function and show some conditions under which CKF is nothing but theoptimal algorithm if ideal computation is possible without computation error. Anumerical example is presented to show the robustness of the proposed method interms of the initial error covariance

Structured Kalman Filter for Time Scale Generation in Atomic Clock Ensembles