Case study of Bayesian RAIM algorithm integrated with Spatial Feature Constraint and Fault Detection and Exclusion algorithms for multi‐sensor positioning-Reference-Cited by-同舟云学术

Case study of Bayesian RAIM algorithm integrated with Spatial Feature Constraint and Fault Detection and Exclusion algorithms for multi‐sensor positioning

Published:2021-06 Issue:2 Volume:68 Page:333-351
ISSN:0028-1522
Container-title:NAVIGATION
language:en
Short-container-title:Navigation

Author:

Gabela Jelena¹²^ORCID,Kealy Allison²,Hedley Mark³,Moran Bill¹

Affiliation:

1. Department of Electrical and Electronic Engineering University of Melbourne Victoria Australia

2. Department of Geospatial Science Royal Melbourne Institute of Technology Victoria Australia

3. Data 61 Commonwealth Scientific and Industrial Research Organisation New South Wales Australia

Publisher

Institute of Navigation

Subject

Electrical and Electronic Engineering,Aerospace Engineering

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/navi.433

Reference48 articles.

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3. Civil Aviation Integrity Requirements for the Global Positioning System

4. A Baseline GPS RAIM Scheme and a Note on the Equivalence of Three RAIM Methods

5. Douc R. &Cappe O.(2005).Comparison of resampling schemes for particle filtering.Proc. of the 4th International Symposium on Image and Signal Processing and Analysis Zagreb Croatia 64–69.https://doi.org/10.1109/ISPA.2005.195385

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