Identification of ARX-models subject to missing data

Parameter estimation when the measurement information may be incomplete is discussed. An ARX model is used as a basic system representation. The presentation covers both missing output and missing 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 Kalman filtering or fixed-interval smoothing formulas. Several approaches to the identification problem are presented, including a new method based on the EM (expectation maximization) algorithm. The different approaches are tested and compared using Monte Carlo simulations. The choice of method is always a tradeoff between estimation accuracy and computational complexity. According to the simulations the gain in accuracy using the EM method can be considerable if many data are missing