Expectation maximization based parameter estimation by sigma-point and particle smoothing

We consider parameter estimation in non-linear state space models by using expectation-maximization based numerical approximations to likelihood maximization. We present a unified view of approximative EM algorithms that use either sigma-point or particle smoothers to evaluate the integrals involved in the expectation step of the EM method, and compare these methods to direct likelihood maximization. For models that are linear in parameters and have additive noise, we show how the maximization step of the EM algorithm is available in closed form. We compare the methods using simulated data, and discuss the differences between the approximations.