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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 language | English |
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Title of host publication | 55th Asilomar Conference on Signals, Systems and Computers, ACSSC 2021 |
Editors | Michael B. Matthews |
Pages | 1591-1596 |
Number of pages | 6 |
ISBN (Electronic) | 978-1-6654-5828-3 |
DOIs | |
Publication status | Published - 2021 |
Event | 55th Asilomar Conference on Signals, Systems, and Computers - Pacific Grove, United States Duration: 31 Oct 2021 → 3 Nov 2021 |
Publication series
Name | Conference Record - Asilomar Conference on Signals, Systems and Computers |
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Volume | 2021-October |
ISSN (Print) | 1058-6393 |
Conference
Conference | 55th Asilomar Conference on Signals, Systems, and Computers |
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Country/Territory | United States |
City | Pacific Grove |
Period | 31/10/21 → 3/11/21 |
ASJC Scopus subject areas
- Signal Processing
- Computer Networks and Communications
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Dive into the research topics of 'An Adaptive Algorithm for Joint Cooperative Localization and Orientation Estimation using Belief Propagation'. Together they form a unique fingerprint.Projects
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CD-Laboratory for Location-aware Electronic Systems
Witrisal, K., Grebien, S. J., Fuchs, A., Wilding, T., Venus, A. & Wielandner, L.
1/01/18 → 31/12/24
Project: Research project