122 Threat Space
• Computation of integrity parameters from independent Advanced RAIM processing: too optimistic URA/SISA values will reduce the probability of having discontinuity events (𝑃𝑓𝑓). However, tuning the values too conservative will increase the probability of having an integrity event (𝑃𝑚𝑐). This trade-off is not further elaborated within this thesis and therefore identified as a potential future work.
• Receiver acquisition of new GNSS signals needs to be such that it satisfies continuity needs.
• Failure rates on navigation message updates need to be sufficiently low.
The task of allocating specific probabilities to each threat case identified in the above tree shall not be further pursued in this thesis. For that task, extensive performance characterizations over long periods are required in order to derive consolidated probability values for each identified threat case. Especially the characterization of the new identified threat cases with respect to the probability of occurrence has not been performed to the knowledge of the author and would go beyond the scope of this thesis. In addition, the performance characteriza-tion of the younger GNSS (e.g. Galileo) is not possible to the required extent. Another aspect is the implementa-tion and characterizaimplementa-tion of dedicated failure detecimplementa-tion mechanisms in order to define and tune the required false alert probabilities. This is deemed a long-term task as all GNSS mature with time. In the end, in order to comply with IMO continuity requirement, it is required that the sum of all probabilities for all considered threat cases satisfies the requirement for the maximum allowable continuity risk.
Conclusion 123
requirements. This perception is based on a direct comparison of both the IMO and LPV200 requirements in terms of continuity where it turns out that the IMO requirements are more stringent (see Table 7-2). Therefore, it is required that both the independent ground segment and the maritime user would need to take dedicated responsibility in order to reduce the risk of a feared event. This can be done by applying failure detection mech-anisms with sufficient probability of missed detection rate. Using the independent ground segment an appropri-ate lappropri-atency of the ISM notifying the user would need to be defined. The longer the ISM lappropri-atency the more conserva-tive the satellite failure probability must be set because the user is notified with the respecconserva-tive latency.
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8 Performance Results
RAIM algorithms have originally been developed with the focus on aeronautical applications. Due to different demands for a maritime user, also the algorithms are required to be adapted. The major differences in the re-quirements are the restriction to the horizontal position component only and a higher demand for continuity.
That is why maritime operations are carefully to be distinguished from the aeronautics.
The following questions need to be answered: first, what is the level of performance that can be achieved with the selected RAIM algorithms? Second, under which conditions compliance can be achieved? This question is related to geometry aspects answering which and how many independent GNSS constellations are required.
Also, it will be interesting to see the performance enhancement considering continuity exposure periods of 3 hours towards 15 minutes. Third, what are the expectations and can they be met? Certainly, availability will increase with a higher number of satellites as geometry is usually identified being one of the major performance drivers. Further expectations are that the Novel RAIM brings better performance compared to the LSR RAIM as well as the fact that MHSS RAIM leads to best results within the selection of algorithms, as being a representative for a new RAIM generation.
The selected RAIM algorithms (LSR RAIM, Novel RAIM and MHSS RAIM) are individually assessed in terms of accuracy, integrity, continuity and availability. Horizontal positioning accuracy evaluation is independent of the integrity algorithm and is driven by the satellite geometry and the fault-free error model. The use of up to three GNSS constellations (GPS only, GPS+Galileo, GPS+Galileo+GLONASS) is considered. For GPS and Galileo, a 24 satellite based constellation is assumed while for GLONASS a 23 satellite constellation is assumed (see Annex A.2). For the position accuracy also a comparison of – on the one hand considering nominal biases and the other hand not considering nominal biases on the pseudoranges – is done. For integrity, continuity and availability analyses, the presence of nominal biases on the pseudoranges is assumed. The LSR RAIM and the Novel RAIM are evaluated using GPS only and GPS+Galileo while the MHSS RAIM uses also triple constellations. The reason of not considering triple constellations for LSR RAIM and Novel RAIM is the non-compatibility regarding the multiple-failure assumption.
GPS-only scenarios are simulated over one day with a sampling rate of 60 seconds while the multi-constellation scenarios (GPS+Galileo and GPS+Galileo+GLONASS) are based on a total simulation time of 10 days with a sam-pling rate of 600 seconds. A global grid within the parameters of [90°S 90°N] for the latitude and [180°W 180°E]
for the longitude is used with a 10° sampling for latitude and longitude. All collected samples per grid point are used to derive representative statistics per grid point in the global figures. An elevation mask angle of 5° has been consistently used throughout the simulations except for the Novel RAIM which applies an elevation mask angle of 20° due to performance optimization reasons (see section 8.4). For the analyses, the elevation depend-ent fault-free error model (UERE) as described in chapter 6 and the threat space defined in chapter 7 have been used consistently for all constellations. The threat space is considered in the sense that the respective failure probabilities are considered in the algorithms. However, it is noted that no artificial threats (e.g. MIs or HMIs) are simluated in the performance evaluations.
126 Performance Results
Basically, these form the overall frame and describe the conditions in which the RAIM algorithms apply and for which the resulting performance is referring to. The demand of maritime services is to achieve a global coverage for the required performance level. The performance results therefore follow a global representation as well as the given statistics which correspond to the global average (average over all grid points and time steps). In addition, a sensitivity analysis is presented supporting the optimization of Novel RAIM performance. All analyses are performed based on the MAAST tool (see Annex A.1).
This chapter is structured in such a way that every performance aspect is covered in a separate subsection. This allows for a detailed assessment and a direct comparison of the results between the individual RAIM algorithms which are discussed and compared in a conclusive section. A final discussion clarifies the performance of the RAIM algorithms and provides feedback for usage for a maritime GNSS user.