T. Sakai, K. Matsunaga, K. Hoshinoo, K. Ito, ENRI T. Walter, Stanford University T. Sakai, K. Matsunaga, K. Hoshinoo, K. Ito, ENRI T. Walter, Stanford

T. Sakai, K. Matsunaga, K. Hoshinoo, K. Ito, ENRIT. Walter, Stanford University

T. Sakai, K. Matsunaga, K. Hoshinoo, K. Ito, ENRIT. Walter, Stanford University

Mitigating Ionospheric ThreatUsing a Dense Monitoring Network

Mitigating Ionospheric ThreatUsing a Dense Monitoring Network

ION GNSS 2007ION GNSS 2007Fort Worth, TXFort Worth, TX

Sept. 25-28, 2007Sept. 25-28, 2007

ION GNSS 25-28 Sept. 2007 - ENRIION GNSS 25-28 Sept. 2007 - ENRI

SSLIDELIDE 22

• The ionospheric effect is a major error source for SBAS:The ionospheric effect is a major error source for SBAS:– The ionospheric term is dominant factor of protection levels;The ionospheric term is dominant factor of protection levels;

– Necessary to reduce GIVE values not only in the storm condition but alsNecessary to reduce GIVE values not only in the storm condition but als

o in the nominal condition to improve availability of vertical guidance.o in the nominal condition to improve availability of vertical guidance.

• The problem is caused by less density of IPP samples:The problem is caused by less density of IPP samples:– The current planar fit algorithm needs inflation factor (Rirreg) and undersThe current planar fit algorithm needs inflation factor (Rirreg) and unders

ampled threat model to ensure overbounding residual error;ampled threat model to ensure overbounding residual error;

– Solution: integrating the external network such as GEONET and CORS;Solution: integrating the external network such as GEONET and CORS;

– Developed a GIVE algorithm suitable to such a situation.Developed a GIVE algorithm suitable to such a situation.

• Evaluated a new GIVE algorithm with GEONET:Evaluated a new GIVE algorithm with GEONET:– 100% availability of APV-II (VAL=20m) at most of Japanese Airports;100% availability of APV-II (VAL=20m) at most of Japanese Airports;

– Still protects users; No HMI condition found.Still protects users; No HMI condition found.

IntroductionIntroduction


SSLIDELIDE 33MSAS StatusMSAS Status

Launch of MTSAT-1R (Photo: RSC)Launch of MTSAT-1R (Photo: RSC)

• All facilities installed:All facilities installed:

– 2 GEOs: MTSAT-1R (PRN 129) and 2 GEOs: MTSAT-1R (PRN 129) and

MTSAT-2 (PRN 137) on orbit;MTSAT-2 (PRN 137) on orbit;

– 4 GMSs and 2 RMSs connected with 4 GMSs and 2 RMSs connected with

2 MCSs;2 MCSs;

– IOC WAAS software with localization.IOC WAAS software with localization.

• Successfully certified for aviation use:Successfully certified for aviation use:

– Broadcast test signal since summer 2Broadcast test signal since summer 2

005 with Message Type 0;005 with Message Type 0;

– Certification activities: Fall 2006 to SCertification activities: Fall 2006 to S

pring 2007.pring 2007.

• Began IOC service on Sept. 27 JST (15:0Began IOC service on Sept. 27 JST (15:0

0 Sept. 26 UTC).0 Sept. 26 UTC).


SSLIDELIDE 44Position AccuracyPosition Accuracy

HorizontalHorizontalRMS 0.50m MAX 4.87mRMS 0.50m MAX 4.87m

VerticalVerticalRMS 0.73m MAX 3.70mRMS 0.73m MAX 3.70m

GPSGPS

MSASMSAS

GPSGPS

MSASMSAS

@Takayama (940058)05/11/14 to 16 PRN129

@Takayama (940058)05/11/14 to 16 PRN129


SSLIDELIDE 55

• The current MSAS is built on the IOC WAASThe current MSAS is built on the IOC WAAS::– As the first satellite navigation system developed by Japan, the design teAs the first satellite navigation system developed by Japan, the design te

nds to be conservative;nds to be conservative;

– The primary purpose is providing horizontal navigation means to aviation The primary purpose is providing horizontal navigation means to aviation

users; Ionopsheric corrections may not be used;users; Ionopsheric corrections may not be used;

– Achieves 100% availability of Enroute to NPA flight modes.Achieves 100% availability of Enroute to NPA flight modes.

Concerns for MSASConcerns for MSAS

• The major concern for vertical guidanThe major concern for vertical guidan

ce is ionospherece is ionosphere::– The ionospheric term is dominant factor The ionospheric term is dominant factor

of protection levels;of protection levels;

– Necessary to reduce GIVE to provide veNecessary to reduce GIVE to provide ve

rtical guidance with reasonable availabilitrtical guidance with reasonable availabilit

y.y.


SSLIDELIDE 66APV-I Availability of IOC MSASAPV-I Availability of IOC MSAS

MSAS Broadcast06/10/17 00:00-24:00

PRN129 (MTSAT-1R)Test Signal

Contour plot for:APV-I Availability HAL = 40m VAL = 50m

Note: 100% availability of Enroute through NPA flight modes.


SSLIDELIDE 77Components of VPLComponents of VPL

• The ionospheric term is dominant component of Vertical Protection Level.The ionospheric term is dominant component of Vertical Protection Level.

VPLVPL

Clock & OrbitClock & Orbit(5.33 (5.33 fltflt))

IonosphereIonosphere(5.33 (5.33 UIREUIRE))

MSAS Broadcast06/10/17 00:00-12:003011 Tokyo

PRN129 (MTSAT-1R)Test Signal


SSLIDELIDE 88Problem: Less Density of IPPProblem: Less Density of IPP

• Ionospheric component: GIVE:Ionospheric component: GIVE:– Uncertainty of estimated vertical ionospheric delay;Uncertainty of estimated vertical ionospheric delay;

– Broadcast as 4-bit GIVEI index.Broadcast as 4-bit GIVEI index.

• Current algorithm: ‘Planar Fit’:Current algorithm: ‘Planar Fit’:– Vertical delay is estimated as parameters of planar ionosphere model;Vertical delay is estimated as parameters of planar ionosphere model;

– GIVE is computed based on the formal variance of the estimation.GIVE is computed based on the formal variance of the estimation.

• The formal variance is inflated by:The formal variance is inflated by:– Rirreg: Inflation factor based on chi-square statistics handling the worst cRirreg: Inflation factor based on chi-square statistics handling the worst c

ase that the distribution of true residual errors is not well-sampled; a funcase that the distribution of true residual errors is not well-sampled; a func

tion of the number of IPPs; Rirreg = 2.38 for 30 IPPs;tion of the number of IPPs; Rirreg = 2.38 for 30 IPPs;

– Undersampled threat model: Margin for threat that the significant structurUndersampled threat model: Margin for threat that the significant structur

e of ionosphere is not captured by IPP samples; a function of spatial diste of ionosphere is not captured by IPP samples; a function of spatial dist

ribution (weighted centroid) of available IPPs.ribution (weighted centroid) of available IPPs.


SSLIDELIDE 99Using External NetworkUsing External Network

• Integrating the external network to the SBAS:Integrating the external network to the SBAS:– Increase the number of monitor stations and IPP observations dramaticaIncrease the number of monitor stations and IPP observations dramatica

lly at very low cost;lly at very low cost;

– Just for ionospheric correction; Clock and orbit corrections are still generJust for ionospheric correction; Clock and orbit corrections are still gener

ated by internal monitor stations because the current configuration is enated by internal monitor stations because the current configuration is en

ough for these corrections;ough for these corrections;

– Input raw observations OR computed ionospheric delay and GIVE from tInput raw observations OR computed ionospheric delay and GIVE from t

he external network; loosely-coupled systems.he external network; loosely-coupled systems.

• Necessary modifications:Necessary modifications:– A new algorithm to compute vertical ionospheric delay and/or GIVE is neA new algorithm to compute vertical ionospheric delay and/or GIVE is ne

cessary because of a great number of observations;cessary because of a great number of observations;

– Safety switch to the current planar fit with internal monitor stations when Safety switch to the current planar fit with internal monitor stations when

the external network is not available.the external network is not available.


SSLIDELIDE 1010Available Network: GEONETAvailable Network: GEONET

• GEONET (GPS Earth Observation GEONET (GPS Earth Observation

Network)Network)::

– Operated by Geographical Survey IOperated by Geographical Survey I

nstitute of Japan;nstitute of Japan;

– Near 1200 stations all over Japan;Near 1200 stations all over Japan;

– 20-30 km separation on average.20-30 km separation on average.

• Open to publicOpen to public::

– 30-second sampled archive is avail30-second sampled archive is avail

able as RINEX files.able as RINEX files.

• Realtime connectionRealtime connection::

– All stations have realtime datalink tAll stations have realtime datalink t

o GSI;o GSI;

– Realtime raw data stream is availabRealtime raw data stream is availab

le via some data providers.le via some data providers.

GEONET station

MSAS station


SSLIDELIDE 1111Sample IPP DistributionSample IPP Distribution

• A snap shot of all IPPs A snap shot of all IPPs

observed at all GEONEobserved at all GEONE

T stations at an epoch;T stations at an epoch;

• GEONET offers a great GEONET offers a great

density of IPP observatidensity of IPP observati

ons;ons;

• There are some Japan-There are some Japan-

shape IPP clusters; eacshape IPP clusters; eac

h cluster is correspondih cluster is correspondi

ng to the associated satng to the associated sat

ellite.ellite.


SSLIDELIDE 1212New AlgorithmsNew Algorithms

(1) Residual Bounding:(1) Residual Bounding:– An algorithm to compute GIVE for given vertical delays at IGPs;An algorithm to compute GIVE for given vertical delays at IGPs;

– Vertical delays are given; For example, generated by planar fit;Vertical delays are given; For example, generated by planar fit;

– Determine GIVE based on observed residuals at IPPs located within 5 dDetermine GIVE based on observed residuals at IPPs located within 5 d

egrees from the IGP; Not on the formal variance of estimation;egrees from the IGP; Not on the formal variance of estimation;

– Improves availability of the system.Improves availability of the system.

(2) Residual Optimization:(2) Residual Optimization:– An algorithm to optimize vertical delays at IGPs;An algorithm to optimize vertical delays at IGPs;

– Here ‘Optimum’ means the condition that sum square of residuals is miniHere ‘Optimum’ means the condition that sum square of residuals is mini

mized;mized;

– GIVE values are generated by residual bounding;GIVE values are generated by residual bounding;

– Improves accuracy of the system.Improves accuracy of the system.


SSLIDELIDE 1313Residual Bounding (1)Residual Bounding (1)

• An algorithm to compute GIVE for given vertical delays at IGPs:An algorithm to compute GIVE for given vertical delays at IGPs:– The MCS knows ionospheric correction function (bilinear interpolation) uThe MCS knows ionospheric correction function (bilinear interpolation) u

sed in user receivers, sed in user receivers, IIv,broadcastv,broadcast((,,)), for given vertical delays at IGPs broa, for given vertical delays at IGPs broa

dcast by the MCS itself;dcast by the MCS itself;

– Residual error between the function and each observed delay at IPP, Residual error between the function and each observed delay at IPP, IIv,Iv,I

PPiPPi, can be computed;, can be computed;

– Determine GIVE based on the maximum of residuals at IPPs located witDetermine GIVE based on the maximum of residuals at IPPs located wit

hin 5 degrees from the IGP.hin 5 degrees from the IGP.

Vertical delay for userVertical delay for user

Observed delay at IPPObserved delay at IPP


SSLIDELIDE 1414Residual Bounding (2)Residual Bounding (2)

Interpolated planefor users

Largest residual

Confidence boundOverboundinglargest residual

IGP i IGP i+1

VerticalDelay

Location

IPP measurements

• Determine GIVE based on the maximum of residuals at IPPs located within 5 Determine GIVE based on the maximum of residuals at IPPs located within 5

degrees from the IGP.degrees from the IGP.


SSLIDELIDE 1515Residual OptimizationResidual Optimization

• An algorithm to optimize vertical delays at IGPs:An algorithm to optimize vertical delays at IGPs:– Vertical delays at IGPs can also be computed based on IPP observationVertical delays at IGPs can also be computed based on IPP observation

s as well as GIVE values;s as well as GIVE values;

– Again, define residual error between the user interpolation function and Again, define residual error between the user interpolation function and

each observed delay at IPP, each observed delay at IPP, IIv,IPPiv,IPPi;;

– The optimum set of vertical delays minimizes the sum square of residualThe optimum set of vertical delays minimizes the sum square of residual

s; GIVE values are minimized simultaneously;s; GIVE values are minimized simultaneously;

– The optimization can be achieved by minimizing the energy function (oftThe optimization can be achieved by minimizing the energy function (oft

en called as cost function) following over IGP delays (See paper):en called as cost function) following over IGP delays (See paper):

Function of IGP delaysFunction of IGP delays


SSLIDELIDE 1616Number of Available IPPsNumber of Available IPPs

• The histogram of the nuThe histogram of the nu

mber of IPPs available mber of IPPs available

at each IGP (located witat each IGP (located wit

hin 5 deg from the IGP);hin 5 deg from the IGP);

• For 68% cases, 100 or For 68% cases, 100 or

more IPPs are availablmore IPPs are availabl

e;e;

• Exceeds 1000 for 27% Exceeds 1000 for 27%

cases.cases.


SSLIDELIDE 1717GIVE by Residual Bounding (1)GIVE by Residual Bounding (1)

• Histogram of computed Histogram of computed

GIVE values in typical iGIVE values in typical i

onospheric condition for onospheric condition for

two algorithms;two algorithms;

• Residual bounding with Residual bounding with

GEONET offers significGEONET offers signific

antly reduced GIVE valantly reduced GIVE val

ues;ues;

• Blue lines indicate quanBlue lines indicate quan

tization steps for GIVEI.tization steps for GIVEI.

Planar FitPlanar Fit

Residual BoundingResidual Bounding(All GEONET sites)(All GEONET sites)


SSLIDELIDE 1818GIVE by Residual Bounding (2)GIVE by Residual Bounding (2)

• Histogram of computed Histogram of computed

GIVE values in severe GIVE values in severe

storm condition for two storm condition for two

algorithms;algorithms;

• The result is not so The result is not so

different from case of different from case of

typical condition.typical condition.




SSLIDELIDE 1919Reduction of GIVEIReduction of GIVEI

• Histogram of 4-bit Histogram of 4-bit

GIVEI index broadcast GIVEI index broadcast

to users;to users;

• Lower limit of GIVEI is Lower limit of GIVEI is

10 for planar fit;10 for planar fit;

• Residual bounding can Residual bounding can

reduce GIVEI as well as reduce GIVEI as well as

GIVE values.GIVE values.




SSLIDELIDE 2020Comparison with FOC WAASComparison with FOC WAAS

• FOC WAAS: Dynamic FOC WAAS: Dynamic

Rirreg, RCM, multi-statRirreg, RCM, multi-stat

e storm detector, and Ce storm detector, and C

NMP;NMP;

• GIVE values derived by GIVE values derived by

residual bounding are stresidual bounding are st

ill smaller than FOC WAill smaller than FOC WA

AS algorithms.AS algorithms.

Planar FitPlanar Fit(FOC WAAS)(FOC WAAS)



SSLIDELIDE 2121Residual OptimizationResidual Optimization

• Histogram of difference Histogram of difference

of IGP delays with and of IGP delays with and

without residual without residual

optimization;optimization;

• Adjustment of IGP Adjustment of IGP

delay stays 0.052m;delay stays 0.052m;

• In comparison with In comparison with

quantization step of quantization step of

0.125m, the effect is 0.125m, the effect is

little.little.


SSLIDELIDE 2222User Position AccuracyUser Position Accuracy

• User vertical position erUser vertical position er

ror at Tokyo in typical ioror at Tokyo in typical io

nospheric condition;nospheric condition;

• Residual bounding imprResidual bounding impr

oves user position accuoves user position accu

racy, while residual optiracy, while residual opti

mization is not effective mization is not effective

so much.so much.

Residual OptimizationResidual Optimization(RMS = 1.10m)(RMS = 1.10m)

Residual BoundingResidual Bounding(RMS = 1.10m)(RMS = 1.10m)

Planar FitPlanar Fit(RMS = 1.47m)(RMS = 1.47m)


SSLIDELIDE 2323Evaluation by Prototype SBASEvaluation by Prototype SBAS

• Prototype SBAS software developed by ENRI (NTM 2006):Prototype SBAS software developed by ENRI (NTM 2006):– Computer software running on PC or UNIX;Computer software running on PC or UNIX;

– Generates the complete 250-bit SBAS messages every seconds;Generates the complete 250-bit SBAS messages every seconds;

– Simulates MSAS performance with user receiver simulator;Simulates MSAS performance with user receiver simulator;

– Available as an MSAS testbed; Measures benefit of additional monitor Available as an MSAS testbed; Measures benefit of additional monitor stations and evaluates new candidate algorithms.stations and evaluates new candidate algorithms.

• Integration with the proposed algorithms:Integration with the proposed algorithms:– Scenario of vertical ionospheric delay and GIVE is generated based oScenario of vertical ionospheric delay and GIVE is generated based o

n GEONET archive data with application of the proposed algorithms;n GEONET archive data with application of the proposed algorithms;

– The prototype generated augmentation messages with ionospheric coThe prototype generated augmentation messages with ionospheric corrections induced as the scenario;rrections induced as the scenario;

– Tested for typical ionospheric condition (July 2004) and severe storm Tested for typical ionospheric condition (July 2004) and severe storm condition (October 2003).condition (October 2003).


SSLIDELIDE 2424User ProtectionUser Protection

PPWAD Simulation03/10/29-313011 Tokyo

Condition:Severe Storm

Algorithm:Residual Bounding(All GEONET sites)(All GEONET sites)

• Users are still protected by this algorithm during the severe storm.


SSLIDELIDE 2525System AvailabilitySystem Availability

PPWAD Simulation04/7/22-24

Condition:Typical Ionosphere

Algorithm:Residual Bounding(All GEONET sites)(All GEONET sites)

Contour plot for:APV-II Availability HAL = 40m VAL = 20m


SSLIDELIDE 2626

• Introduced new algorithms and usage of the external network to Introduced new algorithms and usage of the external network to

mitigate ionospheric threats:mitigate ionospheric threats:– Algorithms for bounding ionospheric corrections based on optimization of Algorithms for bounding ionospheric corrections based on optimization of

residual error measured by dense monitoring network;residual error measured by dense monitoring network;– Integration of GEONET as an external network.Integration of GEONET as an external network.

• Evaluation by prototype SBAS software:Evaluation by prototype SBAS software:– Reduced GIVEI enables 100% availability of APV-II flight mode (VAL=20Reduced GIVEI enables 100% availability of APV-II flight mode (VAL=20

m) at most of Japanese airports;m) at most of Japanese airports;– No integrity failure (HMI condition).No integrity failure (HMI condition).

• Further investigations:Further investigations:– Consideration of threats against the proposed algorithms;Consideration of threats against the proposed algorithms;– Reduction of the number of stations required for residual bounding;Reduction of the number of stations required for residual bounding;– Temporal variation and scintillation effects.Temporal variation and scintillation effects.

ConclusionConclusion


SSLIDELIDE 2727AnnouncementAnnouncement

• Ionospheric delay database will be available shortly:Ionospheric delay database will be available shortly:– The datasets used in this study; andThe datasets used in this study; and– Recent datasets generated daily from August 2007;Recent datasets generated daily from August 2007;– Each dataset is a file which consists of slant delays observed at all availEach dataset is a file which consists of slant delays observed at all avail

able GEONET stations with 300-second interval; Hardware biases of satable GEONET stations with 300-second interval; Hardware biases of satellites and receivers are removed;ellites and receivers are removed;

Access to URL:Access to URL:

http://www.enri.go.jp/sat/pro_eng.htmhttp://www.enri.go.jp/sat/pro_eng.htm

Documents

T. Sakai, K. Matsunaga, K. Hoshinoo, K. Ito, ENRI T. Walter, Stanford University T. Sakai, K. Matsunaga, K. Hoshinoo, K. Ito, ENRI T. Walter, Stanford