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Precise indoor localization remains a challenging problem for a variety ofessential applications. A promising approach to address this problem is toexchange radio signals between mobile agents and static physical anchors (PAs)that bounce off flat surfaces in the indoor environment. Radio frequencysimultaneous localization and mapping (RF-SLAM) methods can be used to jointlyestimates the time-varying location of agents as well as the static locationsof the flat surfaces. Recent work on RF-SLAM methods has shown that eachsurface can be efficiently represented by a single master virtual anchor (MVA).The measurement model related to this MVA-based RF-SLAM method is highlynonlinear. Thus, Bayesian estimation relies on sampling-based techniques. Theoriginal MVA-based RF-SLAM method employs conventional "bootstrap" sampling. Inchallenging scenarios it was observed that the original method might convergeto incorrect MVA positions corresponding to local maxima. In this paper, weintroduce MVA-based RF-SLAM with an improved sampling technique that succeedsin the aforementioned challenging scenarios. Our simulation results demonstratesignificant performance advantages.

Data Fusion for Radio Frequency SLAM with Robust Sampling