Effect of sensor fusion on data-driven learning of Koopman Operators
Published in Preprint, 2021
Recommended citation: S. Balakrishnan, A. Hasnain, ..., E. Yeung. (2021). "The Effect of Sensor Fusion on Data-Driven Learning of Koopman Operators ." arXiv preprint arXiv:2106.15091v.
Dictionary methods for system identification typically rely on one set of measurements to learn governing dynamics of a system. In this paper, we investigate how fusion of output measurements with state measurements affects the dictionary selection process in Koopman operator learning problems. While prior methods use dynamical conjugacy to show a direct link between Koopman eigenfunctions in two distinct data spaces (measurement channels), we explore the specific case where output measurements are nonlinear, non-invertible functions of the system state. This setup reflects the measurement constraints of many classes of physical systems, e.g., biological measurement data, where one type of measurement does not directly transform to another. We propose output constrained Koopman operators (OC-KOs) as a new framework to fuse two measurement sets. Preprint on arXiv:
