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

arXiv: https://arxiv.org/abs/2103.12809 · HTML: https://mlsanity.com/paper/2103.12809/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 27 references that are in the map.
- First posted on arXiv 23 Mar 2021.

## Builds on (most cited first, 5 of 5)

- [A Scalable Algorithm for Tracking an Unknown Number of Targets Using Multiple Sensors](https://mlsanity.com/paper/1607.07647/) (2016)
- [A Belief Propagation Algorithm for Multipath-Based SLAM](https://mlsanity.com/paper/1801.04463/) (2018)
- [5G mmWave Downlink Vehicular Positioning](https://mlsanity.com/paper/1901.01931/) (2019)
- [Variational Bayesian Inference of Line Spectra](https://mlsanity.com/paper/1604.03744/) (2016)
- [Superfast Line Spectral Estimation](https://mlsanity.com/paper/1705.06073/) (2017)

## Abstract (arXiv)

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.