Adaptive extended Kalman filter for recursive identification under missing data

In this work, the parameter identification for systems with scarce measurements is addressed. A linear plant is assumed and its output is assumed to be available only at sporadic instants of time and affected by noise measurement. The identification is carried out estimating the missing outputs in order to construct the regression vector needed by the parameter estimation algorithm and using the available output information not only to update the estimated parameter vector, but also to update the regression vector in order to fasten the convergence of the algorithm. The problem is addressed with an adaptive extended Kalman filter that estimates and correct both the parameters and the regression vector, allowing to improve the convergence speed of the algorithm with respect to other existing ones on the literature as it is shown with several examples.