LARGE-SCALE AUTOREGRESSIVE SYSTEM IDENTIFICATION USING KRONECKER PRODUCT EQUATIONS

By exploiting the intrinsic structure and/or sparsity of the system coefficients in large-scale system identification, one can enable efficient processing. In this paper, we employ this strategy for large-scale single-input multiple-output autoregressive system identification by assuming the coefficients can be well approximated by Kronecker products of smaller vectors. We show that the identification problem can be refor-mulated as the computation of a Kronecker product equation, allowing one to use optimization-based and algebraic solvers.