Abstract

Introduction Resilient provision of Position, Navigation and Timing (PNT) data can be considered as a key element of the e-Navigation strategy developed by the International Maritime Organization (IMO). An indication of reliability has been identified as a high level user need with respect to PNT data to be supplied by electronic navigation means. The paper concentrates on the Fault Detection and Exclusion (FDE) component of the Integrity Monitoring (IM) for navigation systems based both on pure GNSS (Global Navigation Satellite Systems) as well as on hybrid GNSS/inertial measurements. Here a PNT-data processing Unit will be responsible for both the integration of data provided by all available on-board sensors as well as for the IM functionality. The IM mechanism can be seen as an instantaneous decision criterion for using or not using the system and, therefore, constitutes a key component within a process of provision of reliable navigational data in future navigation systems. Methods The performance of the FDE functionality is demonstrated for a pure GNSS-based snapshot weighted iterative least-square (WLS) solution, a GNSS-based Extended Kalman Filter (EKF) as well as for a classical error-state tightly-coupled EKF for the hybrid GNSS/inertial system. Pure GNSS approaches are evaluated by combining true measurement data collected in port operation scenario with artificially induced measurement faults, while for the hybrid navigation system the measurement data in an open sea scenario with native GNSS measurement faults have been employed. Results First, the performance of the proposed FDE schemes in terms of the horizontal error is evaluated for both weighted and unweighted approaches in GNSS-based snapshot and KF-based schemes. Here, mainly due to availability of the process model, the KF approaches have demonstrated smaller sensitivity to the injected GNSS faults, while the methods with C N o weighting schemes have resulted in reduced spread of the obtained position solutions. The statistical evaluation of the proposed FDE schemes have been performed for pure GNSS schemes by considering the fault detection rate as a function of the amplitude for the randomly injected GNSS faults. Although the KF-based approaches have clearly outperformed the memoryless schemes, lower detection rates for weighted schemes could be clearly seen due to inability of the FDE to detect faults of fixed amplitude for satellites with lower C N o values. Moreover, the evaluation of the FDE schemes in terms of maximum horizontal position error have indicated bounded response of the FDE schemes when compared to that of non-FDE methods. Finally, the superiority of the FDE-enabled tightly-coupled GNSS/inertial EKF over the non-FDE solution have been demonstrated using a scenario with native GNSS faults. Conclusions The work had successfully demonstrated an applicability of the developed FDE schemes in snapshot and RBE-based algorithms for maritime applications using both non-inertial GNSS-based positioning and a hybrid IMU/GNSS EKF-based approach. The proposed methods form a solid foundation for construction of a more reliable and robust PNT-Unit, where state-of-the-art hybrid navigation algorithms are augmented with integrity monitoring functionality to ensure the system performance in the presence of GNSS faults. The FDE mechanism provides consistent improvements in terms of the horizontal accuracy both in LS and KF-based methods. Although only port operation case and one example of a true GNSS fault in open sea was considered, the presented results are believed to be general enough and the scheme could be adopted for other applications in future.

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http://link.springer.com/article/10.1007/s12544-016-0217-5/fulltext.html,
http://dx.doi.org/10.1007/s12544-016-0217-5 under the license cc-by
https://link.springer.com/content/pdf/10.1007%2Fs12544-016-0217-5.pdf,
https://etrr.springeropen.com/articles/10.1007/s12544-016-0217-5,
https://etrr.springeropen.com/track/pdf/10.1007/s12544-016-0217-5,
https://core.ac.uk/display/81916216,
https://paperity.org/p/78732087/on-fault-detection-and-exclusion-in-snapshot-and-recursive-positioning-algorithms-for,
https://elib.dlr.de/110111,
https://trid.trb.org/view/1440861,
https://rd.springer.com/article/10.1007/s12544-016-0217-5,
https://academic.microsoft.com/#/detail/2561078718 under the license http://creativecommons.org/licenses/by/4.0
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Published on 01/01/2017

Volume 2017, 2017
DOI: 10.1007/s12544-016-0217-5
Licence: Other

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