Model of Signal In Space Biases in the Integrity Support Message and Advanced RAIM Algorithm

Kurzfassung

In the rapidly evolving GNSS scenario where new constellations, new satellite payloads, new ground monitoring infrastructure and new frequencies will be used, the development of a global integrity concept requires a review of the basic assumption and definition of the integrity performance parameters. This activity is particularly important for the design of the Advanced Receiver Autonomous Integrity Monitoring (ARAIM) architecture and the definition of the Integrity Support Message. The ARAIM architecture will represent an intermediate layer between the user and the Constellation Service Provider (CSP) and aims to protect the user from any misalignment between the CSP commitment and the constellation real performance. For this purpose a common agreement and understanding of the meaning of the Integrity Support Message (ISM) content (biases, sigmas, and failure probabilities) is necessary. Dual frequency users, which can effectively correct the ionospheric errors, will be mostly be affected by other error components like Signal In Space biases (satellite orbit and clock, signal distortions, antenna offsets, inter-frequency biases and code carrier incoherence). These errors need to be investigated, properly modelled in the ISM and possibly corrected. This paper aims to investigate some issues related to the design of the proper ISM format. In particular the first part of the paper analyses the advantages of modelling the SIS error in the satellite orbit reference frame (along track, across track, radial and clock components) rather than in the ranging domain. With this approach the ISM information does not need to refer to the Worst User Location: in fact each user is able to perform the projection in the ranging domain and estimate its specific error contribution. A theoretical analysis of this approach is performed where the reduction of sigma (URA, SISA) and the corresponding improvement in terms of ARAIM user availability are described. Furthermore real data and SIS orbit errors for GPS and Galileo from 2007 to 2015 have been analyzed to support this study and confirm the integrity performance improvement. In fact the effective orientation of the error vector in the orbit reference frame has a distribution concentrated where the WUL concept conservatism is significant. Besides this work addresses another aspect related to the SIS biases. In case the satellites have a common error component and this is contained in the ISM parameters sent to the user, the user clock offset absorbs this component but the ARAIM user integrity protection levels might be significantly affected. In fact in the projection of the biases from the ranging domain to the position domain the ARAIM algorithm conservatively considers the absolute values of the pseudoinverse matrix components. The reason for that is related to the fact that the biases signs are unknown. This is in particular true for biases in the ranging domain, which can have different signs when projected to different user locations in the satellite visibility area. Removing the common component from the ISM bias would not be a practicable solution because the component to be removed is that common only to the satellites in view and not to all satellites: satellite subsets can have very different common components. An alternative solution is proposed and discussed in the paper, where information on signed bias is introduced in the ISM. The Signal In Space Error contains components which depends on the satellite elevation and even on the line of sight direction. These are in particular satellite orbit error and satellite antenna offsets. They can be represented as a four dimensional vector, whose components (along-track, cross-track, radial and clock) are gaussian distributed. These error components can be estimated each one with sign using a network of monitoring stations \cite{WGC_ReportIII}. The other error sources (signal distortions, code carrier coherency, inter-frequency biases) are mostly independent on the satellite elevation and are difficult to be estimated. They are assumed unknown in the present ARAIM algorithms. The reason is due to the fact that these components largely depends on the receiver configuration and characteristics and their variations are difficult to be estimated. Actually it is possible to have their characterization especially using high gain antenna facility, as some studies already show [1]. These components can be characterized as function of the receiver characteristics (correlator spacing, bandwidth, etc.), including the sign information. With this characterization, these components can be reduced by specifying receiver requirements ensuring minimum operation performance (MOPS) [1]. The remaining errors can be characterized as an interval of residual biases including the signs. This process is expected to evolve further in the next years and the effect of geometry independent errors is assumed to be characterized. Providing the information on the bias sign to the user, the ARAIM algorithm can remove the conservative method in the bias projection. The user integrity performance result improved and unaffected by these common biases. The performance are shown in a simulated environment and using real data collected during a flight campaign in March 2015. [1] Thoelert, S., et al. "GNSS Nominal Signal Distortions Estimation, Validation and Impact on Receiver Performance", ION GNSS 2015 Conference, Tampa, Florida