@inproceedings{a3654d8ccd9e46ea8be259c26620da21,
title = "High-order least squares identification: A new approach",
abstract = "A high-order least squares (HOLS) identification method is proposed to identify a system which is subject to disturbances affecting both the dynamical equations and the measurement noise. A linear regression model of the system is obtained based on the Kalman filter structure whose equation error is a colored noise process generated by the residual of the Kalman filter. A high-order regression model is derived by whitening the colored noise process so that the equation error is a zero-mean white noise process. The high-order model is estimated using the least-square method. A reduced-order model is derived from the high-order model using the frequency-weighted least-squares method. The proposed scheme has been successfully evaluated on a number of simulated and physical systems and favorably compared with the prediction error method (PEM).",
keywords = "High Order Model, Kalman Filter, Least Squares Method, Prediction Error Method",
author = "Rajamani Doraiswami and Lahouari Cheded",
year = "2013",
language = "English",
isbn = "9789898565709",
series = "ICINCO 2013 - Proceedings of the 10th International Conference on Informatics in Control, Automation and Robotics",
pages = "147--152",
booktitle = "ICINCO 2013 - Proceedings of the 10th International Conference on Informatics in Control, Automation and Robotics",
}