A continuous-time linear system identification method for slowly sampled data

Both direct and indirect methods exist for identifying continuous-time linear systems. A direct method estimates continuous-time input and output signals from their samples and then use them to obtain a continuous-time model, whereas an indirect method estimates a discrete-time model first. Both methods rely on fast sampling to ensure good accuracy. In this paper, we propose a more direct method where a continuous-time linear model is directly fitted to the available samples. This method produces an exact model asymptotically, modulo some possible aliasing ambiguity, even when the sampling rate is relatively slow. We also state conditions under which the aliasing ambiguity can be resolved, and we provide experiments showing that the proposed method is a valid option when a slow sampling frequency must be used but a large number of samples is available.

• Type: Journal
• Year: 2010
• Where: TSP
• Authors: Damián Marelli, Minyue Fu
• Keywords: Artificial Intelligence, Continuous-time, Continuous-time Linear, Direct Method, TSP 2010