  Particle filters have, in recent years, been found to perform well in highly
nonlinear problems as well as in estimation of parameters. However, there is
still the problem of particle degeneracy in particle filters which has led to
the invention of, among others, feedback particle filters. In this paper, we
introduce a stochastically perturbed feedback particle filter and show that it
is exact. The novelty is in the fact that the innovation process is
stochastically perturbed. Resampled sinkhorn particle filter is also
introduced. We then compare their performance with that of other filters in
simultaneous state and parameter estimation.
