Existing Kalman filter-based parameter identification algorithms estimate the system parameters as either sole states or a subset of augmented states. While the former approach requires the measurement to be sufficiently clean, the latter is reported to have numerical stability issues. Since the parameters are estimated in both these approaches in an optimal sense, in the presence of a significant variation in parameters (due to damage), the estimates may often diverge. In this article, we propose an online health monitoring scheme powered by dual extended Kalman filtering technique to simultaneously estimate the system parameters along with the response states of a reduced-order system. To capacitate damage localization beyond sensor resolution, the proposed method employs location-based structural properties as system parameter. This reduces the dimensionality of the formulation from 4�2 elements in the state matrix to only a few physical parameters. Unnecessary estimation of a large number of unmeasured response states has been avoided by employing the system reduction technique and thus by describing the system using only measured DOFs. This in turn enables estimating a poorly observed system as a fully observed one. Two numerical experiments are performed on two degrading structures: an Euler–Bernoulli beam and a bridge truss to demonstrate the competency of the algorithm with reduced-order models.