Data-Replay Analysis of LAASSafety during Ionosphere StormsYoung Shin Park, Godwin Zhang,Sam Pullen, and Per Enge

Stanford UniversityION GNSS 2007Session E1 Paper # 5September 26, 2007This research was supportedby the FAA LAAS Program Office

Iono. Anomaly from JPL IGS/CORS Data (20 Nov. 2003; 20:15 21:00 UT)11/20/2003, 20:15:00 UT11/20/2003, 21:00:00 UTCourtesy of Seebany Datta-BaruaSeverely anomalous ionosphere behavior poses a threat to LAAS user integrity.

Ionosphere Impact on a LAAS User Simplified Ionosphere Wave Front Model: a wave front ramp defined by the slope and the widthFront SpeedAirplane SpeedFront WidthLAAS Ground FacilityFront SlopeLGF IPP SpeedCourtesy of Jiyun Lee

SimulationIono. Anomaly DatabaseIonosphere AnomalyThreat ModelLAAS Impact Simulation5km DH SeparationPierce Point Plucker Method(Jiyun Lee, 2006) Wedge Sweeper Method(Ming Luo, 2005)Worst-Case VPE(MIEV)LAAS Mitigations CCD Monitor Sigma/P-value inflation (VPLinf > VAL)

Simulation vs. Data-Replay AnalysisIono. Anomaly DatabaseIonosphere AnomalyThreat ModelLAAS Impact Simulation5km DH separationPierce Point Plucker Method(Jiyun Lee, 2006) Wedge Sweeper Method(Ming Luo, 2005)Worst-Case VPE(MIEV)Pick Actual Iono. Datafor Pair of CORS Stations(> 23 km apart)LAAS Mitigations CCD Monitor Sigma/P-value inflation (VPLinf > VAL)Compute ActualUnsmoothedDGPS Range ErrorsCompute Histogramof VPE over Time andSubset GeometryWorst-Case VPEVPL > VAL

Validated Iono. Gradients on SVN 26 (20 Nov. 2003; 20:49 ~ 21:17 UT in OH-MI)150300240300360345ZOB1*425IonosphereFrontData from the Ohio/MichiganCluster of CORS stationson November 20, 2003;9 Pairs of CORS Stations

Data-Replay Analysis ProcedurePseudorange(One station:static LAAS user)Compute CorrectedPseudorangeCompute Vertical PositionError (VPE)Geometry Screeningby checking VPL_H0- VAL (far away) = 43.35 m- VAL (DH) = 10 mWorst-Case VPEAll viable subset geometries:All in view + N-1 + N-2Corrections from LGF(The other station: LAAS reference facility)Differential GPS (DGPS)

WOOS-GARF (74.5 km)150300240360345LGFUSERIonosphereFrontZOB1*425

Subset of OH/MI Stations that Saw Similar Ionosphere Behavior on 11/20/2003WAAS Time (minutes from 5:00 PM to 11:59 PM)Slant Iono Delay (m)Slant Iono Delay (m)Sharp falling edge; slant gradients ~ 300 - 330 mm/km from previous workValley with smaller (but still anomalous) gradientsInitial upward growth; slant gradients ~ 60 120 mm/kmData from 7 CORS Stations, SVN 38

241 data200 binsVPE for All-in-view (WOOS-GARF; 74.5 km)37 mVertical Position ErrorUT (hour)VPE (m)No. of OccurrenceSharp falling edge at 9 PM ; Max. error occurs when the max. gradient occurs, as expected.

VPE for All-in-view + N-1 + N-2(WOOS-GARF; 74.5 km, VAL = 43.35 m)91 m, VAL = 43.35 m, VAL = 43.35 mVertical Position ErrorUT (hour)VPE (m)No. of OccurrenceOne subset geometry that we cant get rid of with VAL = 43. 35 m

VPE for All-in-view + N-1 + N-2(WOOS-GARF; 74.5 km, VAL = 10 m)62 m, VAL = 10 m, VAL = 10 mVertical Position ErrorUT (hour)VPE (m)No. of OccurrenceThe worst-case VPE screened by VAL = 10 m is smaller than the one screened by VAL = 43.35 m, as expected.

ERLA-GALB (23.5 km)150300240300360345USERLGFIonosphereFront425Smaller maximum ionosphere gradient,Smaller separation

VPE for All-in-view (ERLA-GALB; 23.5 km)9 mVertical Position ErrorUT (hour)VPE (m)No. of Occurrence

VPE for All-in-view + N-1 + N-2 (ERLA-GALB; 23.5 km, VAL = 43.35 m)40 m, VAL = 43.35 m, VAL = 43.35 mVertical Position ErrorUT (hour)VPE (m)No. of OccurrenceBefore 9 PM;- Driven by a bad geometry- Big gap to worst-case VPE

VPE for All-in-view + N-1 + N-2(ERLA-GALB; 23.5 km, VAL = 10 m)18 m, VAL = 10 m, VAL = 10 mVertical Position ErrorUT (hour)VPE (m)No. of Occurrence

Summary: Worst-Case VPENov. 20, 2003 UT in OH-MISeparation from Reference to UserWorst-Case VPE vs. Separation, Nov. 20, 2003 in OH-MIWorst-Case VPEVAL = 43.35 mLS for VAL = 43.35 mVAL = 10 mLS for VAL = 10mVAL = 43.35 mLS fit for VAL = 43.35 mVAL = 10 mLS fit for VAL = 10 m

278256177IonosphereFrontValidated Iono. Gradients (29 Oct. 2003; 21:00 UT in NC)Data from the North CarolinaCluster of CORS stationson October 29, 2003;3 Pairs of CORS StationsRALRFAYRSNFDLILLLGFUSERLILL-RALR (45.9 km)

Ionosphere Behaviorbetween LILL and RALR (45.9 km)Ionosphere front is moving very fast.

VPE for All-in-view(LILL-RALR; 45.9 km)241 data200 bins8 mVertical Position ErrorUT (hour)VPE (m)No. of Occurrence

VPE for All-in-view + N-1 + N-2(LILL-RALR; 45.9 km, VAL = 43.35 m)18 mVertical Position ErrorUT (hour)VPE (m)No. of Occurrence

VPE for All-in-view + N-1 + N-2(LILL-RALR; 45.9 km, VAL = 10 m)18 mVertical Position ErrorUT (hour)VPE (m)No. of Occurrence

ConclusionsObjective of this workTo complement the results of worst-case simulationsTo provide an alternative depiction, based on actual data, of the impact of specific validated ionosphere anomalies on LAAS usersComparison with the result of simulationDespite very large separations between CORS stations and the lack of a moving user, worst-case VPE and VPE histograms are similar (to first order) to those given by simulation methodsRoughly linear increase of VPE versus separation is as expectedReduction in worst case VPE when VAL is reduced to 10 meters matches the impact of geometry screening when implemented in simulationOur Results, while not a one-to-one comparison, support the notion that Cat I iono. analysis is sober and is probably conservative

Backups

OutlineIntroductionProcedure for Data-Replay AnalysisDataResultsConclusion

Iono. Anomaly from JPL IGS/CORS Data (20 Nov. 2003; 20:15 21:00 UT)11/20/2003, 20:15:00 UT11/20/2003, 21:00:00 UTCourtesy of Seebany Datta-BaruaSeverely anomalous ionosphere behavior poses a threat to LAAS user integrity.

BackgroundsLocal Area Augmentation System (LAAS)Designed to insure the integrity of broadcast pseudorange corrections by monitoring of measured satellite pseudoranges within the LAAS Ground Facility (LGF).In order to maximize LAAS availability in the presence of ionosphere anomaliesMing Luo: Wedge Sweeper MethodologyM. Luo, et al, LAAS Study of Slow-Moving Ionosphere Anomalies and Their Potential Impacts, Proceedings of ION GNSS 2005, Long Beach, CA, Sept. 13-16, 2005, pp. 2337-2349. Jiyun Lee: Pierce Point Plucker MethodFixed Sigma_vig inflation, RealTime Sigma_vig inflationJ. Lee, M. Luo, et al, Position-Domain Geometry Screening to Maximize LAAS Availability in the Presence of Ionosphere Anomalies, Proceedings of ION GNSS 2006, Fort Worth, TX, Sept. 26-29 , 2006, pp. 393-408 .

SimulationIono. Anomaly DatabaseIonosphere AnomalyThreat ModelLAAS Impact Simulation5km DH SeparationPierce Point Plucker Method(Jiyun Lee, 2006) Wedge Sweeper Method(Ming Luo, 2005)Worst-Case VPE(MIEV)LAAS Mitigations CCD Monitor Sigma/P-value inflation (VPLinf > VAL)IonosphereFront

Data Used in Data-Replay AnalysisData from the Ohio/Michigan cluster of CORS stations on November 20, 2003Data from the North Carolina cluster of CORS stations on October 29, 2003

Over 10 independent station pairs with separations from 23 to 75 km

278256177IonosphereFrontData from the North CarolinaCluster of CORS stationson October 29, 2003;3 Pairs of CORS StationsRALRFAYRSNFDLILLLGFUSERLILL-RALR (45.9 km)

Summary: Worst-Case VPEOct. 29, 2003 20:00~22:00 UT in NCSeparation from Reference to UserWorst-Case VPE vs. Separation, Oct. 29, 2003 in NCWorst-Case VPEVAL = 43.35 mLS for VAL = 43.35 mVAL = 10 mLS for VAL = 10mOnly three points

Validated Iono. Gradients on SVN 26 (20 Nov. 2003; 20:49 ~ 21:17 UT in OH-MI)1502515010070130200300225240230300250360240345ZOB1*425Ionospherefront

SIDN-KNTN (59.1 km)1502515010070130200300225240230300250360240345ZOB1*425LGFUSERIonospherefront

VPE for All-in-view241 data200 bins20 m

VPE for All-in-view + N-1 + N-2 (VAL = 43.35 m)75 m

VPE for All-in-view + N-1 + N-2 (VAL = 10 m)47 m

N-2 VPE for GARF and GUST (75Km)Nov. 20, 2003 20:00 22:00Eliminated PRNs

GARF-GUST (75.3 km)1502515010070130200300225240230300250360240345LGFUSER

VPE for All-in-view241 data200 bins

VPE for All-in-view + N-1 + N-2VAL = 43.35 m

WOOS-GARF (74.5 km)1502515010070130200300225240230300250360240345LGFUSER

241 data200 binsVPE for All-in-view

VPE for All-in-view + N-1 + N-2VAL = 43.35 m

VPE for All-in-view + N-1 + N-2VAL = 10 m

ZOB1-GARF (51.2 km)1502515010070130200300225240230300250360240345ZOB1*425LGFUSER

VPE for All-in-view239 data200 bins

VPE for All-in-view + N-1 + N-2VAL = 43.35 m

VPE for All-in-view + N-1 + N-2VAL = 10 m

FREO-LSBN (73.6 km)1502515010070130200300225240230300250360240345ZOB1*425LGFUSER

VPE for All-in-view241 data200 bins

VPE for All-in-view + N-1 + N-2VAL = 43.35 m

VPE for All-in-view + N-1 + N-2VAL = 10 m

SIDN-KNTN (59.1 km)1502515010070130200300225240230300250360240345ZOB1*425LGFUSER

VPE for All-in-view241 data200 bins

VPE for All-in-view + N-1 + N-2VAL = 43.35 m

VPE for All-in-view + N-1 + N-2VAL = 10 m

COLB-MTVR (65.4 km)1502515010070130200300225240230300250360240345USERLGF

VPE for All-in-view

VPE for All-in-view + N-1 + N-2VAL = 43.35 m

VPE for All-in-view + N-1 + N-2VAL = 10 m

STKR-MCON (44.2 km)1502515010070130200300225240230300250360240345USERLGF

VPE for All-in-view

VPE for All-in-view + N-1 + N-2VAL = 43.35 m

VPE for All-in-view + N-1 + N-2VAL = 10 m

GALB-LEBA (29.8 km)1502515010070130200300225240230300250360240345USERLGF

VPE for All-in-view

VPE for All-in-view + N-1 + N-2VAL = 43.35 m

VPE for All-in-view + N-1 + N-2VAL = 10 m

ERLA-GALB (23.5 km)1502515010070130200300225240230300250360240345USERLGF

VPE for All-in-view

VPE for All-in-view + N-1 + N-2VAL = 43.35 m

VPE for All-in-view + N-1 + N-2VAL = 10 m

601067Missing Data @21:00278256177425086.15kmSNFD-RALR (58.5 km)

VPE for All-in-view241 data200 bins

VPE for All-in-view + N-1 + N-2VAL = 43.35 m

VPE for All-in-view + N-1 + N-2VAL = 10 m

601067Missing Data @21:00278256177425086.15kmFAYR-RALR (86.1 km)

VPE for All-in-view241 data200 bins

VPE for All-in-view + N-1 + N-2VAL = 43.35 m

VPE for All-in-view + N-1 + N-2VAL = 10 m

One of the residual errors that can build up for the user of a dGPS system like LAAS is the ionosphere spatial decorrelation error. This error is caused by the fact that two signals are passing through different region of the atmosphere and the ionospheric delays cannot be completely canceled out even after applying differential corrections. Such errors can grow specially under severe ionosphere storm and pose a threat to user integrity.To do data-replay analysis, we revisited Nov 2003 ionosphere storm where the highest gradients were seen. As you can see here in the snapshot of iono delay maps, this finger-shape stretched northwest across the U.S. in the local afternoon for longer than 45 mins.

the areas where we see sharp transitions between dark red and blue are the place we would observe sharp spatial gradients.

So we are interested in finding out what the error in the worst case conditions could be along with developing an adequate mitigation method.

The CONUS-wide search indicated that the OH/MI region saw the worst gradients in this time slot, so we focused on this dense cluster of CORS stations marked here with white circle and in this time period.

The highest gradients were seen in the 20 Nov 2003 storm, where TEC maps showed a filament stretching northwest across the U.S. in the local afternoon. A similar event was shown by John Foster (2004) to be related to plasmasphere plumes being erosion. We focus on the OH/MI region because of the density of stations.

I will attach Fosters paper in the email to you. If you get a chance, look at Figure 1; its remarkably similar to ours here, but for April 2001.To model such a event, we used simplified ionosphere front model with three parameters, which are front speed, slope and width, assuming the slope is linear and the front is moving with a constant speed. Then we try to estimate those parameters using data collected on ionosphere stormy days.

In this study,

We are interested in low elevation anomalies for the same reason we are interested in ionosphere anomalies at high elevations: An ionosphere anomaly can be parameterized by the things shown in the slide, and ---One of the largest ionosphere gradients was observed during the November 2003 ionosphere storm in OH/MI region. If we model the ionosphere front as a linear flying wedge with a constant speed, the estimated slope of the ionosphere front was about 300 mm/km moving with a speed of 200 m/s. Lets assume a stationary ionosphere front scenario i.e. the front and IPP of LGF move with the same speed and direction, the LGF cannot detect this event, and the resulting residual error is about 5.7 meters in range domain. A brief developing process of simulation is this. Iono anomaly database summarizes in ionosphere anomaly threat model. Current threat model is that the iono. slope is 375 in low elevation of satellite and 425 at high elevation. LAAS mitigations such as CCD monitor and sigma/P-value inflation are applied. Then, LAAS impact simulation at 5km decision height separation is performed to get the worst-case VPE. We have two simulation methods, Wedge sweeper method by Ming Luo and Peirce Point Plucker method by Jiyun Lee. Briefly speaking, Wedge sweeper method model the ionosphere front as a wedge and let it propagate to move through all the pierce point. On the other hand, Pierce Point Plucker method, which is current method in the ionosphere mitigation simulation, considers all the possible pairs. Therefore it is more conservative than Wedge Sweeper method. For the details, please reference their papers.A process known as data-replay analysis has been developed to demonstrate that the results obtained by simulation based on a conservative rendition of this threat model can reasonably approximate the result that is achieved by using observed anomalous data between two fixed WAAS or CORS reference-station locations that were also used to estimate the parameters of the ionosphere threat model.Therefore, from the same iono. Anomaly database, we picked actual iono data for pair of CORS stations whose separation is more than 23 km, and then computed actual unsmoothed DGPS Range Errors. Applying geometry screening, we computed histogram of VPE over time and subset geometry. Finally, the worst-case VPE is obtained and is compared to the worst-case VPE obtained by simulation in the rough manner.First, data from the Ohio/Michigan Cluster of CORS stations on November 20, 2003 is used to perform data-reply analysis for 9 independent station pairs with separations from 23 to 75 kilometers. From the ionosphere storm in OH/MI shown earlier, we know the ionosphere front moved in this way. To get the worst-case VPE, we treat one station which ionosphere front hits first, as a static LAAS user, and the other station which ionosphere front hits next, as a LAAS reference facility.Data-Replay analysis procedure is based on Differential GPS. Pseudorange from one station treated as a static LAAS user is corrected by corrections from the other station treated as a LGF. Then, We add all possible subset geometries from the set of visible and usable satellites that the aircraft theoretically might use by subtracting one satellite or two satellites. Then, computed VPE is screened by checking VPL_H0 to get rid of bad geometry whose VPL is higher than VAL shown here. We used two VALs. One, 43.35m, is VAL for a large separation of stations and the other, 10m, is commonly used in LAAS. Notice that we didnt apply any inflations, while LAAS usually do builtin geometry screening and then take advantage of inflation of sigma and P-value.Now, lets see the result of one of the pairs, WOOS-GARF. The separation of it is 74.5 kilometers. GARF is treated as a LAAS user since ionosphere front hits it first, and WOOS is treated as a LGF.These are the ionosphere delays as a function of time seen from 7 cors sataions in the OH/MI region to a high-elevation satellite, which is SVN 38. We can clearly see a rise as the lines of sight enter the finger-shape, followed by instability, and then a very sudden, sharp fall-off as they leave it. And this is the moment when the extreme gradient of 330 mm/km occurred. Approximately 9PM.Left plot is VPE versus time and the right plot is a histogram of VPE of all-in-view satellites, without adding any subsets of geometry. The peak error corresponds to the greatest sharp falling edge we found at 9pm. Maximum error, 37 m, occurs when the maximum gradient occurs, as expected.When we add all possible subset geometries by subtracting one or two satellites and apply far away VAL geometry screening, the maximum error increases to 91m. On the left plot, we can see the one subset geometry that we cant get rid of with this far away VAL.However, DH VAL screens out more bad geometries. We can see that bad subset geometry is gone. Maximum error is 62 meters. The worst-case VPE screened by VAL = 10m is smaller than the one screened by VAL = 43.35 m, as expected.Next pair of CORS station we will show in this briefing is ERLA and GALB. Notice this pair has smaller maximum ionosphere gradient and smaller separation. And the time ionosphere front hit this pair is after 9 pm.These three differences lead the max error to 9m which is much smaller and the max error occurs after 9 pm.Including subset geometries pushes max. error to 40 meters. It occurs early at arising point and there is big gap to max error on the histogram, which means it is driven by a bad geometry.Therefore, it does not pass the geometry screening using VAL of 10 meters.This is the summary of worst-case VPE vs separation on Nov. 20, 2003 in OH-MI. Red indicates resulting worst-case VPE with VAL = 43.35 m and blue indicates that with VAL = 10 m. Lines are least square fit. With this fit, we can say it is roughly linear increase, as expected.That is why we have 8m max error which is very small when we consider separation of 46 km and relatively higher ionosphere gradient. 280Including subset geometries pushes the max error to 18 meters.Applying 10m VAL screens out more bad geometries, but the max. error goes through the geometry screening. So we have the same max. error as the one with 43.35 m VAL.Data-replay analysis provides a different viewpoint on LAAS vulnerability that do the worst-case ionosphere simulations detailed in [1](Jiyun) because it is limited to events that actually occurred as opposed to worst-case extrapolations of threat-model parameters gleaned from those events.

The objective of this work is to complement the results of worst-case simulations and provide a more realistic depiction of the impact of specific validated ionosphere anomalies on LAAS users.To observe high gradient on low-elevation satellites, we revisited Nov 2003 ionosphere storm where the highest gradients were seen. As you can see here in the snapshot of iono delay maps, this finger-shape stretched northwest across the U.S. in the local afternoon for longer than 45 mins. the areas where we see sharp transitions between dark red and blue are the place we would observe sharp spatial gradients. The CONUS-wide search indicated that the OH/MI region saw the worst gradients in this time slot, so we focused on this dense cluster of CORS stations marked here with white circle and in this time period.

The highest gradients were seen in the 20 Nov 2003 storm, where TEC maps showed a filament stretching northwest across the U.S. in the local afternoon. A similar event was shown by John Foster (2004) to be related to plasmasphere plumes being erosion. We focus on the OH/MI region because of the density of stations.

I will attach Fosters paper in the email to you. If you get a chance, look at Figure 1; its remarkably similar to ours here, but for April 2001.Plucker : / Fixed Sigma_vig inflation, Real-Time Sigma_vig inflationThreat Model.Is used to perform..for