  This paper concerns the use of sequential Monte Carlo methods (SMC) for
smoothing in general state space models. A well-known problem when applying the
standard SMC technique in the smoothing mode is that the resampling mechanism
introduces degeneracy of the approximation in the path space. However, when
performing maximum likelihood estimation via the EM algorithm, all functionals
involved are of additive form for a large subclass of models. To cope with the
problem in this case, a modification of the standard method (based on a
technique proposed by Kitagawa and Sato) is suggested. Our algorithm relies on
forgetting properties of the filtering dynamics and the quality of the
estimates produced is investigated, both theoretically and via simulations.
