A Doubly Recursive Algorithm for System Identification from Nonstationary Cross-Sectional Data

Many practical applications require the simultaneous estimation of unknown dynamical parameters and unknown initial means and covariances from an ensemble of tests. A recursive algorithm which asymptotically obtains the maximum-likelihood estimate of both sets of unknown parameters is presented. The computational requirements of the algorithm are greatly reduced by partitioning the parameter vector into initial and dynamical parameters and making use of a sufficient statistic as an intermediate variable for the estimation of initial condition parameters. The results are illustrated by a numerical example.