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Robust Statistics for GNSS Positioning under Harsh Conditions: A Useful Tool?

Medina, Daniel and Li, Haoqing and Vilà-Valls, Jordi and Closas, Pau Robust Statistics for GNSS Positioning under Harsh Conditions: A Useful Tool? (2019) Sensors, 19 (24). 5402. ISSN 1424-8220

(Document in English)

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Official URL: https://doi.org/10.3390/s19245402


Navigation problems are generally solved applying least-squares (LS) adjustments. Techniques based on LS can be shown to perform optimally when the system noise is Gaussian distributed and the parametric model is accurately known. Unfortunately, real world problems usually contain unexpectedly large errors, so-called outliers, that violate the noise model assumption, leading to a spoiled solution estimation. In this work, the framework of robust statistics is explored to provide robust solutions to the global navigation satellite systems (GNSS) single point positioning (SPP) problem. Considering that GNSS observables may be contaminated by erroneous measurements, we survey the most popular approaches for robust regression (M-, S-, and MM-estimators) and how they can be adapted into a general methodology for robust GNSS positioning. We provide both theoretical insights and validation over experimental datasets, which serves in discussing the robust methods in detail.

Item Type:Article
HAL Id:hal-03202169
Audience (journal):International peer-reviewed journal
Uncontrolled Keywords:
Institution:Université de Toulouse > Institut Supérieur de l'Aéronautique et de l'Espace - ISAE-SUPAERO (FRANCE)
Other partners > German Aerospace Center - DLR (GERMANY)
Other partners > Northeastern University (USA)
Laboratory name:
Deposited On:19 Apr 2021 13:41

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