Gaussian Mixture Model Learning for Multipath Assisted Positioning

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F21%3A00350282" target="_blank" >RIV/68407700:21230/21:00350282 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Gaussian Mixture Model Learning for Multipath Assisted Positioning
Original language description
The wireless signal distortion decreases the precision of the estimated position. However, the distortion caused by the multipath propagation was recently shown not to decrease but even improve the precision when utilized correctly. This approach is called mutipath assisted positioning. In this paper, we propose a particle filter resampling algorithm for multipath assisted positioning exploring high likelihood areas allowing a better approximation of the posterior probability density function. Thanks to the posterior probability density function modeled as a Gaussian mixture model, we can perform the exploration with the same computational load as a regularized particle filter. The proposed algorithm allows decreasing the number of particles orderly while preserving the state of the art approach's precision. We show a comparison of the state of the art Channel-SLAM algorithm with the proposed Gaussian mixture model-based method demonstrating the significance of the achieved improvement.
Czech name
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Czech description
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Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
15th European Conference on Antennas and Propagation
ISBN
978-88-31299-02-2
ISSN
2164-3342
e-ISSN
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Number of pages
5
Pages from-to
1-5
Publisher name
IEEE
Place of publication
Berlin
Event location
Düsseldorf
Event date
Mar 22, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000672699800534

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