  In recent work (arXiv:1006.3100v1), we have presented a novel approach for
improving particle filters for multi-target tracking. The suggested approach
was based on drift homotopy for stochastic differential equations. Drift
homotopy was used to design a Markov Chain Monte Carlo step which is appended
to the particle filter and aims to bring the particle filter samples closer to
the observations. In the current work, we present an alternative way to append
a Markov Chain Monte Carlo step to a particle filter to bring the particle
filter samples closer to the observations. Both current and previous approaches
stem from the general formulation of the filtering problem. We have used the
currently proposed approach on the problem of multi-target tracking for both
linear and nonlinear observation models. The numerical results show that the
suggested approach can improve significantly the performance of a particle
filter.
