State-space models are represented by a state equation and an observation equation (or system of equations to be more precise). These equations are parametarized by components including a transition matrix (FF in some notation) and GG respectively.
These component matrices can have large dimensions. Indeed, the log-likelihood function is a non-convex function and its maximization is difficult. Typical optimization methods used include Newton-Raphson and EM algorithm.
I am new to using state-space models and am looking for quick and robust optimization procedures used by practitioners of state-space models. Is there any literature or best practices regarding the best ways to estimate these matrices?