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ARAIM Stochastic Model Refinements for GNSS Positioning Applications in Support of Critical Vehicle Applications.
Yang, Ling; Sun, Nan; Rizos, Chris; Jiang, Yiping.
Afiliación
  • Yang L; College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China.
  • Sun N; College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China.
  • Rizos C; School of Civil and Environmental Engineering, University of New South Wales, Sydney 2033, Australia.
  • Jiang Y; Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China.
Sensors (Basel) ; 22(24)2022 Dec 13.
Article en En | MEDLINE | ID: mdl-36560166
Integrity monitoring (IM) is essential if GNSS positioning technologies are to be fully trusted by future intelligent transport systems. A tighter and conservative stochastic model can shrink protection levels in the position domain and therefore enhance the user-level integrity. In this study, the stochastic models for vehicle-based GNSS positioning are refined in three respects: (1) Gaussian bounds of precise orbit and clock error products from the International GNSS Service are used; (2) a variable standard deviation to characterize the residual tropospheric delay after model correction is adopted; and (3) an elevation-dependent model describing the receiver-related errors is adaptively refined using least-squares variance component estimation. The refined stochastic models are used for positioning and IM under the Advanced Receiver Autonomous Integrity Monitoring (ARAIM) framework, which is considered the basis for multi-constellation GNSS navigation to support air navigation in the future. These refinements are assessed via global simulations and real data experiments. Different schemes are designed and tested to evaluate the corresponding enhancements on ARAIM availability for both aviation and ground vehicle-based positioning applications.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Sensors (Basel) Año: 2022 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Sensors (Basel) Año: 2022 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza