Robust GNSS phase tracking in case of slow dynamics using variational Bayes inference
| Autor: | Stephanie Bidon, Sebastien Roche, Benoit Priot, Fabio Fabozzi |
|---|---|
| Přispěvatelé: | Airbus (FRANCE), Institut Supérieur de l'Aéronautique et de l'Espace - ISAE-SUPAERO (FRANCE) |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Variational Bayes approximation
Computer science Cycle slips Phase (waves) Inference Estimator 020206 networking & telecommunications 02 engineering and technology Nonlinear Bayesian filtering Phase tracking Phase-locked loop Bayes' theorem Autre Nonlinear filter GNSS applications Control theory 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Slipping |
| Zdroj: | PLANS |
| Popis: | For a precise GNSS (Global Navigation Satellite System) positioning, carrier phase measurements are required. However, cycle slipping in classical phase locked loop (PLL) can lead to a local or permanent loss of lock. To address this problem, we propose a robust nonlinear filter for carrier phase tracking based on Variational Bayes (VB) inference. So far, the algorithm is designed only for slow phase dynamics (i.e., first order loop). Interestingly, the estimator update equation can be expressed in closed form. Performance of our algorithm is assessed on synthetic and experimental GNSS data and compared to that of conventional PLL-based techniques. Results show that the proposed method brings significant improvement in terms of cycle slipping. |
| Databáze: | OpenAIRE |
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