ION GNSS 16 19 ION Sept GNSS 2008

ION GNSS 16 -19 ION Sept. GNSS 2008 - ENRI Savannah, GA Sept. 16 -19, 2008 Modeling Ionospheric Spatial Threat Based on Dense Observation Datasets for MSAS T. Sakai, K. Matsunaga, K. Hoshinoo, ENRI T. Walter, Stanford University

ION GNSS 16 -19 Sept. 2008 - ENRI Introduction 1 • The ionospheric effect is a major error source for SBAS: – The ionospheric term is the dominant factor of protection levels; – Necessary to develop ionosphere algorithms reducing ionospheric component of protection levels to improve availability of vertical guidance. • Threat model should be prepared for new algorithms: – Any algorithms need the associate spatial threat model to ensure overbounding residual error; – The threat model depends upon the algorithms; – Developed a methodology to create a spatial threat model. • Threat models created by the proposed methodology: – Evaluation of the current MSAS threat model; – Some new threat models evaluated; System availability also evaluated.

ION GNSS 16 -19 Sept. 2008 - ENRI MSAS Status 2 • All facilities installed: – 2 GEOs: MTSAT-1 R (PRN 129) and MTSAT-2 (PRN 137) on orbit; – 4 domestic GMSs and 2 RMSs (Hawaii and Australia) connected with 2 MCSs; – IOC WAAS software with localization. • Successfully certified for aviation use. • IOC service since Sept. 27, 2007; – Certified for Enroute to NPA operations; – Approved for navigation use in Japanese FIR. Launch of MTSAT-1 R (Photo: RSC)

ION GNSS 16 -19 Sept. 2008 - ENRI 3 Position Accuracy GPS @Takayama (940058) 05/11/14 to 16 PRN 129 MSAS Horizontal RMS 0. 50 m MAX 4. 87 m GPS @Takayama (940058) 05/11/14 to 16 PRN 129 MSAS Vertical RMS 0. 73 m MAX 3. 70 m

ION GNSS 16 -19 Sept. 2008 - ENRI Concerns for MSAS 4 • The current MSAS is built on the IOC WAAS: – As the first satellite navigation system developed by Japan, the design tends to be conservative; – The primary purpose is providing horizontal navigation means to aviation users; Ionopsheric corrections may not be used; – Achieves 100% availability of Enroute to NPA flight modes. • The major concern for vertical guidance is ionosphere: – The ionospheric term is dominant factor of protection levels; – Necessary to reduce ionospheric term to provide vertical guidance with reasonable availability.

ION GNSS 16 -19 Sept. 2008 - ENRI 5 NPA Availability MSAS Broadcast 08/1/17 00: 00 -24: 00 PRN 129 (MTSAT-1 R) Operational Signal 100% Everywhere Contour plot for: NPA Availability HAL = 556 m VAL = N/A • 100% Availability for Enroute thru NPA.

ION GNSS 16 -19 Sept. 2008 - ENRI 6 APV-I Availability MSAS Broadcast 08/1/17 00: 00 -24: 00 PRN 129 (MTSAT-1 R) Operational Signal Contour plot for: APV-I Availability HAL = 40 m VAL = 50 m • Vertical guidance cannot be provided by the current MSAS.

ION GNSS 16 -19 Sept. 2008 - ENRI 7 Components of VPL Ionosphere (5. 33 s. UIRE) Clock & Orbit (5. 33 sflt) MSAS Broadcast 06/10/17 00: 00 -12: 00 3011 Tokyo PRN 129 (MTSAT-1 R) Test Signal • The ionospheric term (GIVE) is dominant component of Vertical Protection Level.

ION GNSS 16 -19 Sept. 2008 - ENRI Ionosphere Term: GIVE 8 • Ionospheric component: GIVE: – Uncertainty of estimated vertical ionospheric delay; – Broadcast as 4 -bit GIVEI index. • Current algorithm: ‘Planar Fit’: – Vertical delay is estimated as parameters of planar ionosphere model; – GIVE is computed based on the formal variance of the estimation. • The formal variance is inflated by: – Rirreg: Inflation factor based on chi-square statistics handling the worst case that the distribution of true residual errors is not well-sampled; a function of the number of IPPs; Rirreg = 2. 38 for 30 IPPs; – Undersampled threat model: Margin for threat the significant structure of ionosphere is not captured by IPP samples; a function of spatial distribution (weighted centroid) of available IPPs.

ION GNSS 16 -19 Sept. 2008 - ENRI 9 Planar Fit and GIVE • Developed for WAAS; MSAS employs the same algorithm; Vertical Delay Cutoff Radius IPP Fit Plane IGP • Assume ionospheric vertical delay can be modeled as a plane; • Model parameters are estimated by the least square fit; • GIVE (grid ionosphere vertical error): Uncertainty of the estimation including spatial and temporal threats. • GIVE Equation Formal Sigma Spatial Threat Model Spatial Threat Temporal Threat

ION GNSS 16 -19 Sept. 2008 - ENRI Ionospheric Spatial Threat IPP for fit User IPP IGP 10 • Planar fit is performed with IPPs (ionospheric pierce points) measured by GMS stations; • Local irregularities might not be sampled by any GMS stations; • Users might use IPPs within the local irregularities; Potential threat of large position error; • MSAS must protect users against such a condition; The spatial threat term is added to GIVE; Rfit Irregularity • Spatial threat model created based on the historical severe ionospheric storm data.

ION GNSS 16 -19 Sept. 2008 - ENRI Example of Spatial Threat Model Max Residual • • • 11 Threat Model Function of fit radius (cutoff radius) and RCM metric; Good and bad IPP geometries are distinguished by these two metrics; Resulted sundersampled is roughly between 0 and 2. 5.

ION GNSS 16 -19 Sept. 2008 - ENRI The Second Metric: RCM 12 • RCM (Relative Centroid Metric) is used as the second metric of the threat model; The first one is fit radius; IGP dcent Rfit Weighted centroid of IPPs • RCM is the distance between the weighted centroid of IPPs and IGP divided by fit radius; • Using Rfit and RCM, it is possible to distinguish good and bad geometries of IPP distribution, and thus reduce undersampled threat term; • For detail, see Ref. [11].

ION GNSS 16 -19 Sept. 2008 - ENRI Methodology: Data Deprivation 13 IGP R 1 • • • R 2 Rfit Threat Model Removes some IPPs (shown in red) for planar fit; They become virtual users; Residual: difference between estimated plane and removed IPPs (virtual users); Tabulates residuals within the threat region (5 -deg square) with respect to fit radius and RCM; The largest residual in each cell contributes to the threat model because it means the possible maximum residual users may experience; • MSAS employs annular (shown above) and three-quadrant deprivation (Ref. [10]).

ION GNSS 16 -19 Sept. 2008 - ENRI Problems 14 • Current methodology: – Data deprivation; Annular and three-quadrant deprivation schemes; – Problem A: Possibility that some irregularities are not sampled in the input datasets prepared from GMS data; Only 6 domestic for MSAS; – Problem B: Resulted threat model seems to be too much conservative. • Proposal 1 (Problem A): Oversampling: – Creates spatial threat model based on dense observation datasets; – Captures any irregularities even in severe storm conditions; – In Japan, GEONET is available source of such a dense observation. • Proposal 2 (Problem B): Alternative deprivation schemes: – Malicious deprivation and missing station deprivation schemes provide realistic conditions to be considered and avoid being over conservative.

ION GNSS 16 -19 Sept. 2008 - ENRI Datasets for Oversampling 15 • GEONET (GPS Earth Observation Network): – Operated by Geographical Survey Institute of Japan; – Near 1200 stations all over Japan; – 20 -30 km separation on average. • Prepared datasets: – Small/Large datasets are extracted from the complete datasets; – 6 -station datasets for simulating the current model; Domestic GMSs; – 210 -station datasets for oversampling. (Blue triangle) (Red circle) 6 -Station Datasets 210 -Station Datasets

ION GNSS 16 -19 Sept. 2008 - ENRI 16 Oversampling • Methodology: – Planar fit is performed based on measurements at MSAS GMSs; – All other measurements act as virtual users; Residuals from the estimated plane represent potential threats; – Threat region is sampled with a great density of measurements. • Storm Datasets: Set # Period Max Kp Remark 1 03 / 10 / 29 – 31 Storm 2 03 / 11 / 20 – 22 9 9 - 3 04 / 7 / 25 – 27 9 - Storm 4 04 / 11 / 8 – 10 9 - Strom 5 06 / 12 / 5 – 7 5 Solar Flare Storm

ION GNSS 16 -19 Sept. 2008 - ENRI Current Threat Model Max Residual 17 Threat Model (Current Model) • The threat model created by the same method as the current MSAS.

ION GNSS 16 -19 Sept. 2008 - ENRI Unsampled Threat: Safety Model 18 Detected Threat Max Residual Threat Model (Safety Model) • Oversampled by 210 stations; Created model: ‘Safety Model’ • Detected some irregularities not sampled by MSAS GMSs and not reflected to the current threat model.

ION GNSS 16 -19 Sept. 2008 - ENRI 19 Threat Detected by Oversampling View from MSAS GMS (6 -Station Set) Oversampling (210 -Station Set) • 6 -Station Set provided only one IPP within the threat region; • The threat was detected at the upper right corner of the threat region.

ION GNSS 16 -19 Sept. 2008 - ENRI Alternative Deprivation 20 • Malicious deprivation (Ref. [16]): – If storm detector trips, remove an IPP which has the largest residual from the plane; Repeat until storm detector does not trip; – Compute and tabulates residuals of removed IPPs; – The number of removed IPPs is limited up to 2 for this study. • Missing station deprivation (Ref. [11]): – Remove IPPs associate with a GMS; Repeat for every GMSs; – Remove IPPs associate with a satellite; Repeat for every satellites; – Compute and tabulates residuals of removed IPPs. • These schemes provide realistic conditions when creating a threat model.

ION GNSS 16 -19 Sept. 2008 - ENRI 21 Threat Model Metrics IGP dcent IGP MSA dmin Rfit RCM (Used by MSAS) • • IGP Rfit RMD MSA The candidate metrics as the second metric of threat models; Relative Centroid Metric（RCM）：Distance to centroid divided by fit radius; Relative Minimum Distance（RMD）：Distance to the nearest IPP divided by fit radius; Minimum Separation Angle（MSA）：Maximum angle between adjacent IPPs divided by 360 degrees.

ION GNSS 16 -19 Sept. 2008 - ENRI 22 Threat Model (RCM) Threat Model (RCM Model) Performance • Malicious and missing station deprivation; Oversampled by 210 stations; • ‘Performance’: Relationship between data coverage and the associate overbounding sigma value.

ION GNSS 16 -19 Sept. 2008 - ENRI 23 Threat Model (RMD) Threat Model (RMD Model) • Tabulated with respect to RMD metric; • Sigma grows up quickly; RCM seems better metric. Performance

ION GNSS 16 -19 Sept. 2008 - ENRI 24 Threat Model (MSA) Threat Model (MSA Model) Performance • Tabulated with respect to MSA metric; • Sigma stays below 0. 7 m for half of trials; The best metric among three.

ION GNSS 16 -19 Sept. 2008 - ENRI 25 System Availability MSAS Availability for APV-I Flight Mode Safety Model MSA Model (Proposed) • Evaluated system availability with the proposed threat model of MSA metric; • Availability is improved from safety model; However not enough for service of vertical guidance flight modes.

ION GNSS 16 -19 Sept. 2008 - ENRI Conclusion 26 • Needs to develop a methodology to create threat model: – Investigating ionosphere algorithms to improve the performance of MSAS; – Any new algorithms need the associate spatial threat model. • Proposed methodology to create a threat model: – – – The current methodology: Data deprivation; Oversampling and alternative deprivation are proposed; Evaluated candidates of threat model metric; MSA metric works well with the proposed methodology. • Further investigations: – Investigate ionospheric algorithms and operational parameters which minimizes the associate threat model; – Consider other candidates of threat model metric; – Temporal variation and scintillation effects.