Exact Bayesian filtering is impossible in Jump Markov State Space Systems (JMSS), even in the simple linear and Gaussian case. Suboptimal solutions include sequential Monte-Carlo (SMC) algorithms which are indeed popular, and are declined in different versions according to the JMSS considered. In particular, Jump Markov Linear Systems (JMLS) are particular JMSS for which a Rao-Blackwellized (RB) Particle Filter (PF) has been derived. The RBPF solution relies on a combination of PF and Kalman Filtering (KF), and RBPF-based moment estimators outperform purely SMC-based ones when the number of samples tends to infinity. In this paper, we show that it is possible to derive a new RBPF solution, which implements a further RB step in the already RBPF with optimal importance distribution (ID). The new RBPF-based moment estimator outperforms the classical RBPF one whatever the number of particles, at the expense of a reasonable extra computational cost.