  This paper presents a factor graph formulation and particle-based sum-product
algorithm (SPA) for robust sequential localization in multipath-prone
environments. The proposed algorithm jointly performs data association,
sequential estimation of a mobile agent position, and adapts all relevant model
parameters. We derive a novel non-uniform false alarm (FA) model that captures
the delay and amplitude statistics of the multipath radio channel. This model
enables the algorithm to indirectly exploit position-related information
contained in the multipath components (MPCs) for the estimation of the agent
position without using any prior information such as floorplan information or
training data. Using simulated and real measurements in different channel
conditions, we demonstrate that the algorithm can provide high-accuracy
position estimates even in fully obstructed line-of-sight (OLOS) situations and
show that the performance of our algorithm constantly attains the posterior
Cramer-Rao lower bound (P-CRLB), facilitating the additional information
contained in the presented FA model. The algorithm is shown to provide robust
estimates in both, dense multipath channels as well as channels showing
specular, resolved MPCs, significantly outperforming state-of-the-art
radio-based localization methods.
