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

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem KonferenzbandBegutachtung

Abstract

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.

Originalspracheenglisch
Titel15th European Conference on Antennas and Propagation, EuCAP 2021
ErscheinungsortDusseldorf, Germany
Seiten1-5
Seitenumfang5
ISBN (elektronisch)9788831299022
DOIs
PublikationsstatusVeröffentlicht - 22 März 2021
Veranstaltung15th European Conference on Antennas and Propagation: EucAP 2021 - Virtuell, Düsseldorf, Deutschland
Dauer: 22 März 202126 März 2021

Publikationsreihe

Name15th European Conference on Antennas and Propagation, EuCAP 2021

Konferenz

Konferenz15th European Conference on Antennas and Propagation
KurztitelEucAP 2021
Land/GebietDeutschland
OrtVirtuell, Düsseldorf
Zeitraum22/03/2126/03/21

ASJC Scopus subject areas

  • Signalverarbeitung
  • Instrumentierung
  • Computernetzwerke und -kommunikation

Fields of Expertise

  • Information, Communication & Computing

Dieses zitieren