Bulut, YalcinUsluogullari, Omer F.Temugan, Ahmet2025-10-242025-10-242015978-3-319-15048-2978-3-319-15047-52191-5644https://doi.org/10.1007/978-3-319-15048-2_4https://hdl.handle.net/20.500.12899/329033rd IMAC Conference and Exposition on Structural Dynamics -- FEB 02-05, 2015 -- Orlando, FLThe information of spatial distribution of unmeasured disturbances is utilized in controller and observer design. In reality, due to the complexity in the systems, this information is seldom known a priori. Our focus in this study is to estimate the spatial distribution of disturbances from available measurements using a correlations approach that is developed in Kalman filter theory. In this approach one begins by guessing a filter gain and then the approach calculates the disturbance covariance matrices from analysis of the resulting innovations. This paper reviews the innovations correlations approach and examines its merit to localize the disturbances.eninfo:eu-repo/semantics/closedAccessDisturbance localization; Process noise; Measurement noise; Kalman filterConference Object10.1007/978-3-319-15048-2_449542-s2.0-84946026779N/AWOS:000364986400004N/A