Particle Filtering Applied to Robust Multivariate Likelihood Optimization in the Absence of a Closed-Form Solution

Sequential Monte Carlo (SMC) methods are studied to deal with multivariate optimization problems arising from Maximum Likelihood (ML) estimation approaches. We compare results to those obtained by other methods, showing faster convergence and improved robustness when local optimums are present in the cost function to optimize. This paper presents a SMC method to obtain ML estimates in general multivariate state-spaces where a closed-form solution cannot be obtained, reporting computer simulation results for a particular application.