Subspace Identification of Spatially Distributed Systems

We propose a new subspace identification algorithm for identifying a class of spatially varying distributed systems. By exploiting the spatial decay of the distributed system, proposed algorithm identifies the local subsystem dynamics from the local-input output data (inputoutput data of the subsystems in the vicinity of the local subsystem). In contrast to approach where the global system dynamics is identified as a MIMO system, new algorithm has a low computational complexity. Furthermore, algorithm preserves the distributed structure of the global system. We demonstrate the effectiveness of the algorithm on the identification of the distributed system that originates from discretization of the heat equation. In this paper we present the fundamentals of the algorithm, without considering the effect of the measurement noise. The issues of the measurement noise, process disturbance, and experimental verification of the spatial decay will be addressed in the future research papers.