A spatial canonical approach to multidimensional state-space identification for distributed parameter systems

State-space model identification methods for 1-dimensional (1D) systems cannot be straightforwardly generalized to the multidimensional case, due to the problem of estimating a spatially structured global state sequence. This paper proposes a particular apriori structured state, such that a series of local 1D models obtained can be identified separately.