  We propose a decentralized subspace algorithm for identification of
large-scale, interconnected systems that are described by sparse (multi) banded
state-space matrices. First, we prove that the state of a local subsystem can
be approximated by a linear combination of inputs and outputs of the local
subsystems that are in its neighborhood. Furthermore, we prove that for
interconnected systems with well-conditioned, finite-time observability
Gramians (or observability matrices), the size of this neighborhood is
relatively small. On the basis of these results, we develop a subspace
identification algorithm that identifies a state-space model of a local
subsystem from the local input-output data. Consequently, the developed
algorithm is computationally feasible for interconnected systems with a large
number of local subsystems. Numerical results confirm the effectiveness of the
new identification algorithm.
