  In this paper, we present a Bayesian multipath-based simultaneous
localization and mapping (SLAM) algorithm that continuously adapts interacting
multiple models (IMM) parameters to describe the mobile agent state dynamics.
The time-evolution of the IMM parameters is described by a Markov chain and the
parameters are incorporated into the factor graph structure that represents the
statistical structure of the SLAM problem. The proposed belief propagation
(BP)-based algorithm adapts, in an online manner, to time-varying system models
by jointly inferring the model parameters along with the agent and map feature
states. The performance of the proposed algorithm is finally evaluating with a
simulated scenario. Our numerical simulation results show that the proposed
multipath-based SLAM algorithm is able to cope with strongly changing agent
state dynamics.
