System Identification Subject to Missing Data
1990 (English)Report (Other academic)
In this paper we study parameter estimation when the measurement information may be incomplete. As a basic system representation we use an ARX-model. The presentation covers both missing output and input. First reconstruction of the missing values is discussed. The reconstruction is based on a state-space formulation of the system, and is performed using the Kalman filtering or fixed-interval smoothing formulas. Several approaches to the identification problem are then presented, including a new method based on the so called EM algorithm. The different approaches are tested and compared using Monte-Carlo simulations. The choice of method is always a trade off between estimation accuracy and computational complexity. According to the simulations the gain in accuracy using the EM method can be considerable if much data are missing.
Place, publisher, year, edition, pages
Linköping: Linköping University , 1990. , 18 p.
LiTH-ISY-I, ISSN 8765-4321 ; 1065
Computational complexity, Kalman filters, Parameter estimation, Statistics, System identification
IdentifiersURN: urn:nbn:se:liu:diva-104038OAI: oai:DiVA.org:liu-104038DiVA: diva2:694435