## 1 Introduction  
In various engineering fields, identifying the uncertain status of structural systems is a critical task to avoid severe malfunctions due to degradation of system parameters caused by deterioration or sudden damage by natural or man-made disasters. Among various approaches to such system identification (SI), there have been notable research efforts for _probabilistic_ SI to handle uncertainties in the problem. For instance, [PERSON] (1987) formulated SI as a Bayesian inverse problem, in which a joint probability density function (PDF) was introduced for parameters of systems under static loads [1]. Gaussian assumption was used for both prior and posterior distributions. Recently, such approach was extended by several probabilistic SI methods utilizing Bayesian Network, Markov Chain Monte Carlo, and Branch-and-Bound method [2; 3; 4; 5; 6]. Although these methods can handle general distribution models for prior and posterior, they are still subject to fundamental limitations for dynamic systems, i.e., systems under dynamic loads. For precise updates of knowledge of a dynamic system based on the given measurements, sequential Bayesian filters including Kalman filter have been utilized [7; 8; 9; 10]. The main strength of Bayesian filters comes from their two complementary updates: time-update and measurement-update. In this approach, by employing models that are respectively describing time- and measurement-updates using common random variables, a given dynamic system does not need to be expressed as one highly complicated joint PDF. To apply a filter method to dynamic system identification, it is vital to build an accurate state-space model that represents the dynamic system by two numerical models respectively describing the time- and measurement-updates. Then, a suitable filter method should be selected especially when the state-space model is nonlinear or time-variant. To track the status of nonlinear dynamic systems, the extended Kalman filter (EKF) has been utilized [7; 11]. EKF uses first-order linearization to approximate underlying statistics of the state-space model, but this filter may suffer from some limitations including the requirement of sufficient differentiability of the dynamic model. To overcome this inherent problem, the unscented Kalman filter (UKF) was developed using so-called unscented transformation [10; 12]. This transformation linearizes nonlinear dynamic model by using \"sigma points\" instead of the derivatives. Using the UKF as the basic filter method, this paper develops a near-real-time SI method for dynamic systems that may experience time-varying changes in system parameters caused by damage. To handle this target problem with sparsely measured accelerations, the state-space model of this study, which is generally used in parameter estimation only [13], utilizes the Newmark model to update modal-displacement, velocity, and acceleration with current system parameters for just one time-update. Then, regularization techniques with an assumed prior information about system are introduced to the state-space model to avoid divergence in estimations caused by sparse measurements. Even if a proper state-space model is constructed for time-varying systems, conventional filters such as EKF and UKF using time-invariant artificial noises may not be able to achieve accurate estimations. This is because the covariance matrices of artificial noises, which are arbitrarily chosen to satisfy the optimality condition of filter during a certain time interval, may not be optimal as the statistical characteristics of measurements change over the excitation period. To address this issue, the UKF should be able to determine new artificial noises at every time step to satisfy the optimality condition. The authors thus explored several adaptive filtering techniques, which can adapt to time-varying statistical characteristics of measurements [11; 13; 14; 15; 16; 17].