When system parameters vary at a fast rate, identification schemes based on model-free local estimation approaches do not yield satisfactory results. In cases like this, more sophisticated parameter tracking procedures must be used, based on explicit models of parameter variation (often referred to as hypermodels), either deterministic or stochastic. Kalman filter trackers, which belong to the second category, are seldom used in practice due to difficulties in adjusting their internal parameters such as the smoothness coefficient and the order of the hypermodel. The paper presents a new solution to this problem, based on the concept of preestimation of system parameters. The resulting identification algorithms, which can be characterized as decoupled Kalman trackers, are computationally attractive, easy to tune and can be optimized in an adaptive fashion using the parallel estimation approach. The decoupled KF algorithms can be regarded as an attractive alternative to the state-of-the-art algorithms which are much more computationally demanding.