Non-asymptotic Identification of Linear Dynamical Systems Using Multiple Trajectories

Understand

This paper considers the problem of linear time-invariant (LTI) system identification using input/output data.

• Recent work has provided non-asymptotic results on partially observed LTI system identification using a single trajectory but is only suitable for stable systems.
• We provide finite-time analysis for learning Markov parameters based on the ordinary least-squares (OLS) estimator using multiple trajectories, which covers both stable and unstable systems.
• For unstable systems, our results suggest that the Markov parameters are harder to estimate in the presence of process noise.