A Recursive System Identification with Non-uniform Data under Coprime Collaborative Sensing

We present a system identification method based on recursive least-squares (RLS) and coprime collaborative sensing, which can recover system dynamics from non-uniform temporal data. Focusing on systems with fast input sampling and slow output sampling, we use a polynomial transformation to reparameterize the system model and create an auxiliary model that can be identified from the non-uniform data. We show the identifiability of the auxiliary model using a Diophantine equation approach. Numerical examples demonstrate successful system reconstruction and the ability to capture fast system response with limited temporal feedback.

Book section