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

1. Introduction The ionospheric effect is a major error source for SBAS:
ION GNSS Sept ENRI
Introduction
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.

2. MSAS Status All facilities installed:
ION GNSS Sept ENRI
MSAS Status
All facilities installed:
2 GEOs: MTSAT-1R (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-1R (Photo: RSC)

3. Position Accuracy GPS GPS MSAS MSAS Horizontal RMS 0.50m MAX 4.87m
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Position Accuracy
@Takayama (940058)
05/11/14 to 16 PRN129
@Takayama (940058)
05/11/14 to 16 PRN129
GPS
GPS
MSAS
MSAS
Horizontal
RMS 0.50m MAX 4.87m
Vertical
RMS 0.73m MAX 3.70m

4. Concerns for MSAS The current MSAS is built on the IOC WAAS:
ION GNSS Sept ENRI
Concerns for MSAS
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.

5. NPA Availability MSAS Broadcast 08/1/17 00:00-24:00 PRN129 (MTSAT-1R)
ION GNSS Sept ENRI
NPA Availability
MSAS Broadcast
08/1/17 00:00-24:00
PRN129 (MTSAT-1R)
Operational Signal
Contour plot for:
NPA Availability
HAL = 556m
VAL = N/A
100% Everywhere
100% Availability for Enroute thru NPA.

6. APV-I Availability MSAS Broadcast 08/1/17 00:00-24:00
ION GNSS Sept ENRI
APV-I Availability
MSAS Broadcast
08/1/17 00:00-24:00
PRN129 (MTSAT-1R)
Operational Signal
Contour plot for:
APV-I Availability
HAL = 40m
VAL = 50m
Vertical guidance cannot be provided by the current MSAS.

7. ION GNSS Sept ENRI
Components of VPL
VPL
Ionosphere
(5.33 sUIRE)
Clock & Orbit
(5.33 sflt)
MSAS Broadcast
06/10/17 00:00-12:00
3011 Tokyo
PRN129 (MTSAT-1R)
Test Signal
The ionospheric term (GIVE) is dominant component of Vertical Protection Level.

8. Ionosphere Term: GIVE Ionospheric component: GIVE:
ION GNSS Sept ENRI
Ionosphere Term: GIVE
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 that the significant structure of ionosphere is not captured by IPP samples; a function of spatial distribution (weighted centroid) of available IPPs.

9. ION GNSS Sept ENRI
Planar Fit and GIVE
Cutoff Radius
Vertical Delay
Fit Plane
IPP
IGP
Developed for WAAS; MSAS employs the same algorithm;
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
Temporal Threat
Spatial Threat Model

10. Ionospheric Spatial Threat
ION GNSS Sept ENRI
Ionospheric Spatial Threat
Irregularity
IGP
IPP for fit
User IPP
Rfit
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;
Spatial threat model created based on the historical severe ionospheric storm data.

11. Example of Spatial Threat Model
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Example of Spatial Threat Model
Max Residual
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.

12. ION GNSS Sept ENRI
The Second Metric: RCM
RCM (Relative Centroid Metric) is used as the second metric of the threat model; The first one is fit radius;
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].
Rfit
IGP
dcent
Weighted centroid of IPPs

13. Methodology: Data Deprivation
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Methodology: Data Deprivation
Rfit R1 R2
IGP
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]).

14. Problems Current methodology: Proposal 1 (Problem A): Oversampling:
ION GNSS Sept ENRI
Problems
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.

15. Datasets for Oversampling
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Datasets for Oversampling
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) 6-Station Datasets
(Red circle) 210-Station Datasets

16. Oversampling Methodology: Storm Datasets:
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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 9
Storm 2
03 / 11 / 20 – 22 9- 3
04 / 7 / 25 – 27 4
04 / 11 / 8 – 10
Strom 5
06 / 12 / 5 – 7
Solar Flare

17. Threat Model (Current Model)
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Current Threat Model
Max Residual
Threat Model (Current Model)
The threat model created by the same method as the current MSAS.

18. Unsampled Threat: Safety Model
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Unsampled Threat: Safety Model
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.

19. Threat Detected by Oversampling
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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.

20. Alternative Deprivation
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Alternative Deprivation
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.

21. Threat Model Metrics Rfit IGP dcent Rfit IGP dmin IGP MSA
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Threat Model Metrics
Rfit
IGP
dcent
Rfit
IGP
dmin
IGP
MSA
RCM (Used by MSAS)
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.

22. Threat Model (RCM Model)
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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.

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

24. Threat Model (MSA Model)
ION GNSS Sept ENRI
Threat Model (MSA)
Threat Model (MSA Model)
Performance
Tabulated with respect to MSA metric;
Sigma stays below 0.7m for half of trials; The best metric among three.

25. MSAS Availability for APV-I Flight Mode
ION GNSS Sept ENRI
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.

26. Conclusion Needs to develop a methodology to create threat model:
ION GNSS Sept ENRI
Conclusion
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.