IDENTIFICATION OF ARX-MODELS SUBJECT TO MISSING DATA

In this note, we study parameter estimation when the measurement infor mation may be incomplete. As a basic system representation we use an A RX-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. Seve ral approaches to the identification problem are then presented, inclu ding 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 ac curacy using the EM method can be considerable if much data are missin g.