Optimal Centered Active Excitation in Linear System Identification
This work investigates designing input sequences for linear system identification to estimate unknown matrices efficiently using active excitation. It derives lower bounds showing the minimum required data size for any such algorithm and proposes a computationally efficient Least Squares Estimator (LSE) algorithm that matches these optimal bounds under specific conditions.