An Adaptive Algorithm for Joint Cooperative Localization and Orientation Estimation using Belief Propagation

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

Abstract

In cooperative localization applications, measurement-model related model parameters are often assumed to be known even though they can depend strongly on the environment. This assumption can lead to a reduced localization accuracy due to parameter mismatch. In this paper, we propose an adaptive factor-graph-based algorithm for joint cooperative localization and orientation estimation which inherently estimates all unknown model parameters as well as the measurement uncertainty. We use RSS radio measurements and account for the directivity of the antennas with a parametric antenna pattern. We validate our proposed methods with simulations in a static scenario and show that there is only a small loss in positioning accuracy compared to known model parameters and measurement noise.
Original languageEnglish
Title of host publication55th Asilomar Conference on Signals, Systems and Computers, ACSSC 2021
EditorsMichael B. Matthews
Pages1591-1596
Number of pages6
ISBN (Electronic)978-1-6654-5828-3
DOIs
Publication statusPublished - 2021
Event55th Asilomar Conference on Signals, Systems, and Computers - Pacific Grove, United States
Duration: 31 Oct 20213 Nov 2021

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
Volume2021-October
ISSN (Print)1058-6393

Conference

Conference55th Asilomar Conference on Signals, Systems, and Computers
Country/TerritoryUnited States
CityPacific Grove
Period31/10/213/11/21

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

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