Kalman state filtering based least squares iterative parameter estimation for observer canonical state space systems using decomposition

This paper focuses on the parameter and state estimation problems for observer canonical state space systems from measurement information, derives a Kalman filter based least squares iterative (KF-LSI) algorithm to estimate the parameters and states, and a model decomposition based KF-LSI algorithm to enhance computational efficiency. An example is provided to confirm the effectiveness of the proposed algorithms.