Together with Dr. Richard Dearden, I have developed the Gaussian particle fiter
(GPF) (see my papers).
It generalizes the Rao-Blackwellised particle filter (RBPF) to the non-linear case, approximating the belief state for every particle by a Gaussian that is propagated using an unscented Kalman filter for each particle.
For linear Gaussian systems, GPF is equivalent to RBPF.
Obviously, it is a biased filter for non-linear, non-Gaussian systems, but our experiments show that this bias is very small in the domains we study, and that the results of GPF are much better than for standard PF. The best-performing variant in our experiments is look-ahead GPF (la-GPF), the generalization of la-RBPF.
Here's the Matlab code for GPF,
including a demo that compares GPF, la-GPF, PF and the unscented particle filter
It includes models for real data, a non-linear toy-model, and a linear model used in an application of RBPF to rover data.