Monitoring linear time-varying (LTV) systems using model- based approaches typically requires dense instrumentation and high- fidelity support models, resulting in significant computational and finan- cial costs. Bayesian filtering-based methods adopt a joint state-parameter estimation approach for LTV system monitoring wherein states/parameters are observed as well as inferred from the measurements. Eventually, spar- sity in measurement while dealing with high-fidelity models can aggra- vate the ill-posedness in the estimation diverging the estimation to im- practical or no solutions. To enhance estimation resolution and preci- sion, an alternative approach can be supporting estimation with virtual sensor measurements sampled from future time steps. Virtual measure- ments are in fact measurements sampled from a future time step relative to the time at which estimation is sought. To leverage virtual measure- ments, the estimation needs to be time-delayed, enabling the observation of states/parameters through measurements taken at both current and subsequent time steps, thereby alleviating the ill-posedness. This method has been explored for an LTV spring-mass-dashpot system, where the numerical investigation utilizes an interacting filtering environment to estimate states and parameters in the presence of sparse measurements. The study has shown how incorporating additional information can sta- bilize the estimation process, leading to improved estimates for both states and parameters.