Multipath-based SLAM using Belief Propagation with Interacting Multiple Dynamic Models

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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.

Original languageEnglish
Title of host publication15th European Conference on Antennas and Propagation, EuCAP 2021
Place of PublicationDusseldorf, Germany
Number of pages5
ISBN (Electronic)9788831299022
Publication statusPublished - 22 Mar 2021
Event15th European Conference on Antennas and Propagation: EucAP 2021 - Virtuell, Düsseldorf, Germany
Duration: 22 Mar 202126 Mar 2021

Publication series

Name15th European Conference on Antennas and Propagation, EuCAP 2021


Conference15th European Conference on Antennas and Propagation
Abbreviated titleEucAP 2021
CityVirtuell, Düsseldorf

ASJC Scopus subject areas

  • Signal Processing
  • Instrumentation
  • Computer Networks and Communications

Fields of Expertise

  • Information, Communication & Computing

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