Bayesian filtering techniques involve recursively combining a mathematical model and a system’s response measurements to enhance the model’s estimation and prediction capabilities. These techniques rely on reduced-order surrogate models for estimation, which limits the type of models used. It is important to choose a model class and its parameters with sufficient complexity to accurately represent the physical system. However, increasing the number of model parameters reduces computational efficiency and poses challenges for modeling and filtering. To tackle this issue, this chapter applies Bayesian filtering for estimating and quantifying uncertainty in a large-scale suspension strut system called the Modular Active Spring-Damper System (MAFDS). Specifically, the performance of the Kalman filter using a 2-degree-of-freedom model is investigated in terms of estimation accuracy and statistical error behavior when estimating the response of the MAFDS.