  Precise indoor localization remains a challenging problem for a variety of
essential applications. A promising approach to address this problem is to
exchange radio signals between mobile agents and static physical anchors (PAs)
that bounce off flat surfaces in the indoor environment. Radio frequency
simultaneous localization and mapping (RF-SLAM) methods can be used to jointly
estimates the time-varying location of agents as well as the static locations
of the flat surfaces. Recent work on RF-SLAM methods has shown that each
surface can be efficiently represented by a single master virtual anchor (MVA).
The measurement model related to this MVA-based RF-SLAM method is highly
nonlinear. Thus, Bayesian estimation relies on sampling-based techniques. The
original MVA-based RF-SLAM method employs conventional "bootstrap" sampling. In
challenging scenarios it was observed that the original method might converge
to incorrect MVA positions corresponding to local maxima. In this paper, we
introduce MVA-based RF-SLAM with an improved sampling technique that succeeds
in the aforementioned challenging scenarios. Our simulation results demonstrate
significant performance advantages.
