T. Sakai, K. Matsunaga, K. Hoshinoo, ENRIT. Walter, Stanford .ppt

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1、T. Sakai, K. Matsunaga, K. Hoshinoo, ENRI T. Walter, Stanford University,Modeling Ionospheric Spatial Threat Based on Dense Observation Datasets for MSAS,ION GNSS 2008 Savannah, GA Sept. 16-19, 2008,ION GNSS 16-19 Sept. 2008 - ENRI,SLIDE 1,The ionospheric effect is a major error source for SBAS: The

2、 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 th

3、reat 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 availabil

4、ity also evaluated.,Introduction,ION GNSS 16-19 Sept. 2008 - ENRI,SLIDE 2,MSAS Status,Launch of MTSAT-1R (Photo: RSC),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 l

5、ocalization. Successfully certified for aviation use. IOC service since Sept. 27, 2007; Certified for Enroute to NPA operations; Approved for navigation use in Japanese FIR.,ION GNSS 16-19 Sept. 2008 - ENRI,SLIDE 3,Position Accuracy,Horizontal RMS 0.50m MAX 4.87m,Vertical RMS 0.73m MAX 3.70m,GPS,MSA

6、S,GPS,MSAS,Takayama (940058) 05/11/14 to 16 PRN129,Takayama (940058) 05/11/14 to 16 PRN129,ION GNSS 16-19 Sept. 2008 - ENRI,SLIDE 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 prov

7、iding horizontal navigation means to aviation users; Ionopsheric corrections may not be used; Achieves 100% availability of Enroute to NPA flight modes.,Concerns for MSAS,The major concern for vertical guidance is ionosphere: The ionospheric term is dominant factor of protection levels; Necessary to

8、 reduce ionospheric term to provide vertical guidance with reasonable availability.,ION GNSS 16-19 Sept. 2008 - ENRI,SLIDE 5,NPA Availability,100% Everywhere,MSAS Broadcast 08/1/17 00:00-24:00PRN129 (MTSAT-1R) Operational SignalContour plot for: NPA AvailabilityHAL = 556mVAL = N/A,100% Availability

9、for Enroute thru NPA.,ION GNSS 16-19 Sept. 2008 - ENRI,SLIDE 6,APV-I Availability,MSAS Broadcast 08/1/17 00:00-24:00PRN129 (MTSAT-1R) Operational SignalContour plot for: APV-I AvailabilityHAL = 40mVAL = 50m,Vertical guidance cannot be provided by the current MSAS.,ION GNSS 16-19 Sept. 2008 - ENRI,SL

10、IDE 7,Components of VPL,The ionospheric term (GIVE) is dominant component of Vertical Protection Level.,VPL,Clock & Orbit (5.33 sflt),Ionosphere (5.33 sUIRE),MSAS Broadcast 06/10/17 00:00-12:00 3011 TokyoPRN129 (MTSAT-1R) Test Signal,ION GNSS 16-19 Sept. 2008 - ENRI,SLIDE 8,Ionosphere Term: GIVE,Ion

11、ospheric 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

12、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

13、ionosphere is not captured by IPP samples; a function of spatial distribution (weighted centroid) of available IPPs.,ION GNSS 16-19 Sept. 2008 - ENRI,SLIDE 9,Planar Fit and GIVE,Developed for WAAS; MSAS employs the same algorithm;Assume ionospheric vertical delay can be modeled as a plane;Model para

14、meters are estimated by the least square fit;GIVE (grid ionosphere vertical error): Uncertainty of the estimation including spatial and temporal threats.,ION GNSS 16-19 Sept. 2008 - ENRI,SLIDE 10,Ionospheric Spatial Threat,Planar fit is performed with IPPs (ionospheric pierce points) measured by GMS

15、 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

16、 historical severe ionospheric storm data.,ION GNSS 16-19 Sept. 2008 - ENRI,SLIDE 11,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

17、between 0 and 2.5.,ION GNSS 16-19 Sept. 2008 - ENRI,SLIDE 12,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,

18、 it is possible to distinguish good and bad geometries of IPP distribution, and thus reduce undersampled threat term;For detail, see Ref. 11.,Weighted centroid of IPPs,ION GNSS 16-19 Sept. 2008 - ENRI,SLIDE 13,Methodology: Data Deprivation,Removes some IPPs (shown in red) for planar fit; They become

19、 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 resid

20、ual users may experience; MSAS employs annular (shown above) and three-quadrant deprivation (Ref. 10).,Threat Model,ION GNSS 16-19 Sept. 2008 - ENRI,SLIDE 14,Problems,Current methodology: Data deprivation; Annular and three-quadrant deprivation schemes; Problem A: Possibility that some irregularitie

21、s 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 se

22、vere 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

23、-19 Sept. 2008 - ENRI,SLIDE 15,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-s

24、tation datasets for simulating the current model; Domestic GMSs; 210-station datasets for oversampling.,(Blue triangle) 6-Station Datasets (Red circle) 210-Station Datasets,ION GNSS 16-19 Sept. 2008 - ENRI,SLIDE 16,Oversampling,Methodology: Planar fit is performed based on measurements at MSAS GMSs;

25、 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:,ION GNSS 16-19 Sept. 2008 - ENRI,SLIDE 17,Current Threat Model,The threat model created by the same method as t

26、he current MSAS.,Max Residual,Threat Model (Current Model),ION GNSS 16-19 Sept. 2008 - ENRI,SLIDE 18,Unsampled Threat: 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.,Max Residu

27、al,Threat Model (Safety Model),Detected Threat,ION GNSS 16-19 Sept. 2008 - ENRI,SLIDE 19,Threat Detected by Oversampling,6-Station Set provided only one IPP within the threat region; The threat was detected at the upper right corner of the threat region.,View from MSAS GMS (6-Station Set),Oversampli

28、ng (210-Station Set),ION GNSS 16-19 Sept. 2008 - ENRI,SLIDE 20,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; Th

29、e 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 real

30、istic conditions when creating a threat model.,ION GNSS 16-19 Sept. 2008 - ENRI,SLIDE 21,Threat Model Metrics,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（RM

31、D）：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,SLIDE 22,Threat Model (RCM),Malicious and missing station deprivation; Oversampled by 210 stations; Performance: Relationsh

32、ip between data coverage and the associate overbounding sigma value.,Threat Model (RCM Model),Performance,ION GNSS 16-19 Sept. 2008 - ENRI,SLIDE 23,Threat Model (RMD),Tabulated with respect to RMD metric; Sigma grows up quickly; RCM seems better metric.,Threat Model (RMD Model),Performance,ION GNSS

33、16-19 Sept. 2008 - ENRI,SLIDE 24,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.,ION GNSS 16-19 Sept. 2008 - ENRI,SLIDE 25,System Availability,MSAS Availability for APV-I Flight Mode

34、,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,SLIDE 26,Needs to develop a methodology to cre

35、ate 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; Evalu

36、ated 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.,Conclusion,