Abstract
In this paper we present a Bayesian framework for utilizing the amplitude information of multipath components (MPCs) in radio-signal-based simultaneous localization and mapping (SLAM). The developed algorithm exploits the complex amplitudes of MPC parameters that are provided by the radio channel parameter estimator. With this information, the algorithm can adapt the probabilities of detecting features within a radio signal in a time-variant way. The algorithm increases the life-time of used features and it better explores 'weak' MPCs, i.e., MPCs with low signal-to-interference-plus-noise-ratios (SINRs) in dense multipath.
Original language | English |
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Title of host publication | 2018 IEEE Statistical Signal Processing Workshop, SSP 2018 |
Publisher | IEEE - Institute of Electrical and Electronics Engineers Inc. |
Pages | 851-855 |
Number of pages | 5 |
ISBN (Print) | 9781538615706 |
DOIs | |
Publication status | Published - 2018 Aug 30 |
Event | 20th IEEE Statistical Signal Processing Workshop, SSP 2018 - Freiburg im Breisgau, Germany Duration: 2018 Jun 10 → 2018 Jun 13 |
Conference
Conference | 20th IEEE Statistical Signal Processing Workshop, SSP 2018 |
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Country/Territory | Germany |
City | Freiburg im Breisgau |
Period | 2018/06/10 → 2018/06/13 |
Subject classification (UKÄ)
- Signal Processing
Free keywords
- Bayesian SLAM
- factor graph
- localization
- message passing
- multipath channel
- probabilistic data association
- radio-signal-based positioning