ingestion of ionospheric scintillation skymaps into gnss … · 2019. 5. 31. · 17-mar-2014 –...

Ingestion of Ionospheric Scintillation Skymaps into GNSS Algorithms

Matthieu Lonchay

PhD Defense

ULiège

Friday 24 May 2019


Matthieu Lonchay

PhD Defense

ULiège

Friday 24 May 2019?

AnalysisGNSS Algorithms and Ionosphere

GeostatisticsIonospheric Scintillation Skymaps

PrototypesMitigation Strategies

GNSS positioning is based on the principle of multilateration between a GNSS receiver and several GNSS satellites orbiting the Earth

GNSS positioning is based on the principle of multilateration

GNSS algorithms exploit code pseudorange and carrier phase measurements performed on several frequencies in order to estimate the instantaneous navigation solution of the receiver

GNSS algorithms exploit code pseudorange and carrier phase measurements related to several frequencies



The Standard Point Positioning (SPP) algorithm is based on single-frequency code pseudorange measurements

• Light implementation

• Least-squares adjustment

• Precision of 5 m-10 m




The Precise Point Positioning (PPP) algorithm is based on dual-frequency code pseudorange and carrier phase measurements combined with regional and global corrections

• Complex implementation

• Corrections required from a provider

• Kalman filter

• Initial integer ambiguities to estimate

• Centimetre-decimetre precision level


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N[m]

-3 -1 0 1 3

E [m]

Brussels14-May-2014

UTC00:00:0023:59:30

Horizontal Error

RMSE= 1.97 mRMSE= 0.07 m

SPP PPP



The Precise Point Positioning (PPP) algorithm is based on dual-frequency code pseudorange and carrier phase measurements combined with regional and global corrections

The ionosphere is the part of the upper atmosphere of the Earth where sufficient ionisation can exist to affect the propagation of radio waves

The ionosphere is responsible for refraction effects on GNSS signals which result in delays to be considered in the mathematical model of GNSS algorithms

The ionosphere is the part of the upper atmosphere of the Earth where sufficient ionisation can exist to affect the propagation of radio waves


Small-scale irregularities in the ionospheric free-electron density involve diffraction and scattering effects of GNSS signals

Small-scale irregularities in the ionospheric free-electron density cause diffraction and scattering effects on GNSS signals leading to rapid fluctuations of the amplitude and phase of the signals


Small-scale irregularities in the ionospheric free-electron density involve diffraction and scattering effects of GNSS signals

Scattered signals reach the receiver via multiple paths resulting in a diffraction pattern with destructive and constructive interferences of the scattered signals

Intense ionospheric scintillations of GNSS signals severely threaten GNSS positioning performances and can make GNSSs totally in inoperable

Intense ionospheric scintillations of GNSS signals severely threaten GNSS positioning performances and can make GNSSs totally in inoperable

SPP

10

20

ε3D[m]

PPP

10

20

30

ε3D[m]

22h 00h 02h 04h 06h

UTC

Inconfidentes17-Mar-2014 – 18-Mar-2014

UTC21:50:0006:10:00

E2Episode E2

WS01

SPP PPP WS01b (IDW)

SPP

PPP

A network of Ionospheric Scintillation Monitoring Receivers (ISMRs) located in Brazil is exploited to generate high-density ionospheric scintillation skyplots

45° S

30° S

15° S

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15° N

60° W 45° W 30° W

MAC2

MAN2

PALM

POAL

PRU2

SJCU

FORT

INCO

UFBA

Magnetic Equator

Ionospheric

Scintillation

Monitoring

Receiver

Each station of the network is equipped with an ISMR to monitor low-latitude ionospheric scintillations

𝑆𝑆4 =𝐼𝐼2 − 𝐼𝐼 2

𝐼𝐼 2 .3.6

1

0


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Azimuth

Elevation…

A skyplot is a specific type of map resulting from the projection of the satellite locations from the satellite hemisphere (horizontal coordinates) to a plane centred on the user’s location


Ionospheric Pierce Points (IPPs) associated to satellite-receiver lines of sight related to a network of ISMRs can be exploited to generate high-density skyplots


Ionospheric Pierce Points (IPPs) associated to satellite-receiver lines of sight related to a network of ISMRs can be exploited to generate high-density skyplots

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Inconfidentes16-Mar-2014

UTC00:00:00


Geometric Component

The locations of the IPPs depend on the GNSS orbits and the geographic coordinates of the user’s location

Attribute Component

The values of the 𝑆𝑆4 index for every IPP depends on the instantaneous state of the ionosphere

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UTC00:00:00

S4 [-]

0.0 0.2 0.4 0.6 0.8 1.0

Spatial Autocorrelation constitutes a measure of how similar are nearby objects

Positive SAC is essential to compute interpolated maps

Detection Scaling Tracking

Spatial Autocorrelation indices can be calculated and statistically tested to detect the presence of significantly positive spatial autocorrelation in a given scatter plot

𝐼𝐼 =𝑁𝑁𝑆𝑆0

∑𝑖𝑖=1𝑁𝑁 ∑𝑗𝑗=1𝑁𝑁 𝑤𝑤𝑖𝑖𝑗𝑗 (𝑧𝑧𝑖𝑖− ̅𝑧𝑧)(𝑧𝑧𝑗𝑗− ̅𝑧𝑧)∑𝑖𝑖=1𝑁𝑁 𝑧𝑧𝑖𝑖2

𝐶𝐶 =(𝑁𝑁 − 1)

2 𝑆𝑆0

∑𝑖𝑖=1𝑁𝑁 ∑𝑗𝑗=1𝑁𝑁 𝑤𝑤𝑖𝑖𝑗𝑗 (𝑧𝑧𝑖𝑖−𝑧𝑧𝑗𝑗)2

∑𝑖𝑖=1𝑁𝑁 (𝑧𝑧𝑖𝑖− ̅𝑧𝑧)2

𝐺𝐺 =∑𝑖𝑖=1𝑁𝑁 ∑𝑗𝑗=1𝑁𝑁 𝑤𝑤𝑖𝑖𝑗𝑗 𝑧𝑧𝑖𝑖 𝑧𝑧𝑗𝑗

∑𝑖𝑖=1𝑁𝑁 𝑧𝑧𝑖𝑖 𝑧𝑧𝑗𝑗

𝑆𝑆0 = �𝑖𝑖=1

𝑁𝑁

�𝑗𝑗=1

𝑁𝑁

𝑤𝑤𝑖𝑖𝑗𝑗

𝑤𝑤𝑖𝑖𝑗𝑗 = �1𝑑𝑑𝑖𝑖𝑗𝑗2

, 𝑖𝑖 ≠ 𝑗𝑗

0, 𝑖𝑖 = 𝑗𝑗

SAC indices are based on the geometric and attribute components of Ionospheric Scintillation Skyplots

SAC indices consider all possible pairs of entities

SAC indices are based on a weight function based on the interdistance related to each pair of entities

Moran’s I Index

Geary’s C Index

Getis-Ord General G Statistic

Ionospheric scintillation skyplots show significantly positive spatial autocorrelation during active ionospheric scintillation conditions at the station of Inconfidentes, Brazil

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[-]


Active Dataset

00h 24h 48h 72h 96h 120h 144h 168h

UTC

0.1

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[-]

*Moran's I

0.0

0.5*I[-]

*Geary's C

1

2*C[-]

*Getis-Ord General G (Z-Score)

036

*ZG[-]

00h 24h 48h 72h 96h 120h 144h 168h

UTC


Active Dataset

I P99

C P99

ZG

P99

Global SAC Indices have specific characteristics which highlight their complementarity:

Moran: Global SACGeary: Global index with high sensitivity to Local SACGetis-Ord: Clustering Index


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UTC01:25:00

= 0.16

Active Dataset D1

(a)

CS1

ZI= 12.57

ZC

= -2.52Z

G= 11.33

S4 [-]

0.0 0.2 0.4 0.6 0.8 1.0

0°

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60°

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90°270°

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UTC00:39:00

= 0.19

Active Dataset D1

(c)

CS3

ZI= 9.75

ZC

= -3.04Z

G= 10.06

S4 [-]

0.0 0.2 0.4 0.6 0.8 1.0

0°

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UTC00:00:00

= 0.26

ZI= 2.04

ZC

= -4.94Z

G= -1.07

S4 [-]

0.0 0.2 0.4 0.6 0.8 1.0

CS1Moran’s I Index

CS2Geary’s C Index

CS3Getis-Ord General G Statistic



The tracking of spatial features impacting the global spatial autocorrelation is performed by exploiting local versions of the spatial autocorrelation indices

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UTC00:00:00

= 0.26

ZI= 2.04

ZC

= -4.94Z

G= -1.07

S4 [-]

0.0 0.2 0.4 0.6 0.8 1.0

Size of the convolution window

20°

Significance level95%

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UTC01:25:00 α= 0.05

dk= 20°

Moran's I

High/High Low/Low High/Low Low/High

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UTC00:39:00 α= 0.05

dk= 20°

Getis-Ord's G*

High/High Low/Low

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UTC00:00:00 α= 0.05

dk= 20°

Geary's C





CS3Getis-Ord Gi* Index

𝐺𝐺𝑖𝑖∗(𝑑𝑑𝑘𝑘) =∑𝑗𝑗=1𝑁𝑁 𝑤𝑤𝑖𝑖𝑗𝑗 𝑧𝑧𝑗𝑗∑𝑗𝑗=1𝑁𝑁 𝑧𝑧𝑗𝑗



𝑤𝑤𝑖𝑖𝑗𝑗 = �1𝑑𝑑𝑖𝑖𝑗𝑗2

, 𝑖𝑖 ≠ 𝑗𝑗

0, 𝑖𝑖 = 𝑗𝑗


0°

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UTC01:25:00 α= 0.05

dk= 20°

Moran's I


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UTC00:00:00 α= 0.05

dk= 20°

Geary's C








UTC00:39:00 α= 0.05

dk= 20°

Getis-Ord's G*

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ZG

[-]

-3 0 3 6

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UTC01:25:00 α= 0.05

dk= 20°

Moran's I


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UTC00:00:00 α= 0.05

dk= 20°

Geary's C


Local and global spatial autocorrelation in ionospheric scintillation skyplots can also be exploited by specific techniques to generate interpolated maps

TSI – Trend Surface Interpolation

Local and global spatial autocorrelation in ionospheric scintillation skyplots can also be exploited by specific techniques to generate interpolated maps

IDW – Inverse Distance Weighting

Spatial analysis techniques applied to ionospheric scintillation skyplots can be exploited to generate three types of spatial products / ionospheric scintillation skymaps

GOS skymapsTSI skymaps IDW skymaps

ZG

[-]

-3 0 3 6


The prototyping of mitigation strategies against ionospheric scintillations is grafted to the post-processing GNSS software RTKLIB

Workspace

Post-processing GNSS Software RTKLIB

SPP and PPP Algorithms

Multiple Constellations (GPS, GLONASS, Galileo)

Open-Source code (C language)

RTKLIB

Workspace


ISMR station of Inconfidentes, Brazil

Experimental Episodes E1/E2/E3/E4

PPP

2

4

6

8

ε3D[m]

22h 00h 02h 04h

UTC


UTC20:50:0005:10:00

E1

Episode E1

(a)

OriginalRMSE= 0.46 m

SR= 35.93 %

SR0.5

= 29.34 %

Original

PPP

2

4

6

8

ε3D[m]

22h 00h 02h 04h 06h

UTC


UTC21:50:0006:10:00

E2

Episode E2

(b)


SR= 57.88 %

SR0.5

= 41.72 %

Original

PPP

2

4

6

8

ε3D[m]

00h 02h 04h 06h

UTC


UTC22:50:0007:10:00

E3

Episode E3

(c)


SR= 83.63 %

SR0.5

= 30.94 %

Original

PPP

2

4

6

8

ε3D[m]

22h 00h 02h 04h

UTC


UTC21:20:0005:40:00

E4

Episode E4

(d)


SR= 33.13 %

SR0.5

= 24.95 %

Original

Mitigation strategies against ionospheric scintillations are benchmarked at the ISMR station of Inconfidentes, Brazil, during four specific 6h-long ionospheric scintillation episodes

The benchmark of the prototype mitigation strategies against ionospheric scintillations requires the definition of several performance criteria

Workspace




Performance Criteria

PPP

2

4

6

8

ε3D[m]

22h 00h 02h 04h

UTC


UTC20:50:0005:10:00

E1

Episode E1

(a)


SR= 35.93 %

SR0.5

= 29.34 %

Original

PPP

2

4

6

8

ε3D[m]

22h 00h 02h 04h 06h

UTC


UTC21:50:0006:10:00

E2

Episode E2

(b)


SR= 57.88 %

SR0.5

= 41.72 %

Original

PPP

2

4

6

8

ε3D[m]

00h 02h 04h 06h

UTC


UTC22:50:0007:10:00

E3

Episode E3

(c)


SR= 83.63 %

SR0.5

= 30.94 %

Original

PPP

2

4

6

8

ε3D[m]

22h 00h 02h 04h

UTC


UTC21:20:0005:40:00

E4

Episode E4

(d)


SR= 33.13 %

SR0.5

= 24.95 %

Original

Accuracy Root-Mean-Square Error (RMSE)

Continuity Success Rate (SR)

Reliability Success Rate for a given accuracy (SRXX)

Processing Time

Prototype mitigation strategies target the stochastic modelling stage and the integrity monitoring stage of the SPP and PPP algorithms

Workspace




Performance Criteria

Targets

Mathematical Model

StochasticModel

Parameter Estimation

Process

Integrity Monitoring

Stochastic Modelling

Integrity Monitoring

SPP/PPP Algorithms

𝑄𝑄𝑙𝑙 = 𝜎𝜎2𝐼𝐼𝑚𝑚 =

𝜎𝜎12

𝜎𝜎22

⋱𝜎𝜎𝑚𝑚2

Two approaches are considered to customise the stochastic model of the SPP and PPP Algorithms

Weighting Scheme (WS)

Spatial Masks (SM)

The stochastic model of the SPP/PPP algorithms is represented by the covariance matrix of the observations which contains information related to the precision of the individual measurements

Covariance Matrix of the Observations

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V[-]

G20 G03 G31 S33 S26


UTC23:39:00

E1

Episode E1

WS02 (GOS)

α= 0.05d

k= 20°

Getis-Ord's G*

ZGi*

[-]

-3 0 3 6

WS02a (PGOS

)

WS02b (DGOS

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G20

G03

G31S33

S26

𝜎𝜎2 = 𝐹𝐹𝐺𝐺𝑁𝑁𝐺𝐺𝐺𝐺2 𝑅𝑅2 𝑎𝑎2 +𝑏𝑏2

𝑠𝑠𝑖𝑖𝑠𝑠 𝜂𝜂𝑠𝑠∗ 𝑉𝑉 + ⋯

Tracking Variance

StochasticFactor

A Calibration Process based on the actual tracking variance (Conker Model) is exploited in order to defined the variation of the Stochastic Factors according to variables measurable in skymaps

The Weighting Scheme (WS) of the stochastic model can be tuned by adjusting calibrated stochastic factors based on ionospheric scintillation skymaps to deweightculprit observations

SPP

10

20

ε3D[m]


UTC21:50:0006:10:00

E2Episode E2

WS02

22h 00h 02h 04h 06h

UTC

Original WS02a (PGOS) WS02b (DGOS)


SPP

10

20

ε3D[m]

PPP

10

20

30

ε3D[m]

22h 00h 02h 04h 06h

UTC


UTC21:50:0006:10:00

E2Episode E2

WS02




Spatial Masks (SM) are designed according to ionospheric scintillation skymaps in order to exclude potentially culprit observations from the parameter estimation process of the SPP and PPP algorithms


UTC00:07:00

E3

Episode E3

GOS

α= 0.05d

k= 20°

Getis-Ord's G*

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90°270°

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330°

ZGi*

[-]

-3 0 3 6


UTC00:07:00

E3

Episode E3

SM01b (AZEL)

α= 0.05d

k= 20°

Getis-Ord's G*

S4 [-]

0.0 0.2 0.4 0.6 0.8 1.0

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𝑄𝑄𝑙𝑙 = 𝜎𝜎2𝐼𝐼𝑚𝑚 =

𝜎𝜎12

𝜎𝜎22

⋱𝜎𝜎𝑚𝑚2

Ionospheric Scintillation Skymaps are exploited to design specific masks based on various criteria.

SPP

10

20

30

ε3D[m]


UTC22:50:0007:10:00

E3Episode E3

SM03

00h 02h 04h 06h

UTC

Original SM03a (D01) SM03b (D05) SM03c (T10)


SPP

10

20

30

ε3D[m]

PPP

2

4

6

ε3D[m]

00h 02h 04h 06h

UTC


UTC22:50:0007:10:00

E3Episode E3

SM03




All prototype mitigation strategies increase the continuity of the SPP algorithm with a Success Rate ranging between 95% and 100%

95

96

97

98

99

100

SR[%]

10 10.5 11 11.5

RMSE [m]

SPP

(a)

Original WS SM

Original

SM01a

SM03c

WS02b

WS02a

SM03bSM01b

SM02aSM02b

SM03a

WS01a

WS01b


Experimental Episodes

E1-E2-E3-E4

95

96

97

98

99

100

SR[%]

10 10.5 11 11.5

RMSE [m]

SPP

(a)

Original WS SM

Original

SM01a

SM03c

WS02b

WS02a

SM03bSM01b

SM02aSM02b

SM03a

WS01a

WS01b



E1-E2-E3-E4

This mitigation strategy is based on a spatiotemporal

buffer applied to GOS skymaps


95

96

97

98

99

100

SR[%]

10 10.5 11 11.5

RMSE [m]

SPP

(a)

Original WS SM

Original

SM01a

SM03c

WS02b

WS02a

SM03bSM01b

SM02aSM02b

SM03a

WS01a

WS01b



E1-E2-E3-E4

The continuity of the SPP algorithm is always improved


Almost all prototype mitigation strategies increase the accuracy of the SPP algorithm with an RMSE dropping from ~11m down to ~10m

95

96

97

98

99

100

SR[%]

10 10.5 11 11.5

RMSE [m]

SPP

(a)

Original WS SM

Original

SM01a

SM03c

WS02b

WS02a

SM03bSM01b

SM02aSM02b

SM03a

WS01a

WS01b



E1-E2-E3-E4

Only those two strategies tend to decrease the accuracy of the SPP algorithm in case of

ionospheric scintillations


95

96

97

98

99

100

SR[%]

10 10.5 11 11.5

RMSE [m]

SPP

(a)

Original WS SM

Original

SM01a

SM03c

WS02b

WS02a

SM03bSM01b

SM02aSM02b

SM03a

WS01a

WS01b



E1-E2-E3-E4


The accuracy of the SPP algorithm is improved by almost all the prototype mitigation strategies

Abusive spatial masks tend to decrease the quality of the satellite geometry

Almost all prototype mitigation strategies increase the accuracy of the SPP algorithm with an RMSE dropping from ~11m down to ~10m

Prototype mitigation strategies based on GOS skymaps provide better performanceswhich indicates a higher suitability of GOS skymaps for GNSS positioning

95

96

97

98

99

100

SR[%]

10 10.5 11 11.5

RMSE [m]

SPP

(a)

Original WS SM

Original

SM01a

SM03c

WS02b

WS02a

SM03bSM01b

SM02aSM02b

SM03a

WS01a

WS01b



E1-E2-E3-E4

All those strategies are based on GOS

skymaps




95

96

97

98

99

100

SR[%]

10 10.5 11 11.5

RMSE [m]

SPP

(a)

Original WS SM

Original

SM01a

SM03c

WS02b

WS02a

SM03bSM01b

SM02aSM02b

SM03a

WS01a

WS01b



E1-E2-E3-E4




GOS Skymaps provide better results for GNSS positioning in terms of accuracy and continuity

Prototype mitigation strategies based on GOS skymaps provide better performanceswhich indicates a higher suitability of GOS skymaps for GNSS positioning

50

55

60

65

70

SR[%]

1.0 1.5 2.0 2.5 3.0

RMSE [m]

PPP

(b)

Original WS SM

Original

WS01a

WS01b

WS02a=WS02b

SM01a

SM01b

SM02a

SM02b

SM03a

SM03b

SM03c



E1-E2-E3-E4

All prototype mitigation strategies increase the continuity of the PPP algorithm with a Success Rate ranging between ~52% and ~68%

50

55

60

65

70

SR[%]

1.0 1.5 2.0 2.5 3.0

RMSE [m]

PPP

(b)

Original WS SM

Original

WS01a

WS01b

WS02a=WS02b

SM01a

SM01b

SM02a

SM02b

SM03a

SM03b

SM03c



E1-E2-E3-E4

The continuity of the PPP Algorithm is always improved but it remains too low for demanding applications


50

55

60

65

70

SR[%]

1.0 1.5 2.0 2.5 3.0

RMSE [m]

PPP

(b)

Original WS SM

Original

WS01a

WS01b

WS02a=WS02b

SM01a

SM01b

SM02a

SM02b

SM03a

SM03b

SM03c



E1-E2-E3-E4


The accuracy performances of the PPP algorithm are improved but not to the expected level for the PPP algorithm


50

55

60

65

70

SR[%]

1.0 1.5 2.0 2.5 3.0

RMSE [m]

PPP

(b)

Original WS SM

Original

WS01a

WS01b

WS02a=WS02b

SM01a

SM01b

SM02a

SM02b

SM03a

SM03b

SM03c



E1-E2-E3-E4



All those strategies are based on Spatial

Masks

For the PPP algorithm, prototype mitigation strategies based on spatial masks provide better performances than strategies based on the tuning of the weighting scheme

50

55

60

65

70

SR[%]

1.0 1.5 2.0 2.5 3.0

RMSE [m]

PPP

(b)

Original WS SM

Original

WS01a

WS01b

WS02a=WS02b

SM01a

SM01b

SM02a

SM02b

SM03a

SM03b

SM03c



E1-E2-E3-E4



For the PPP algorithm, designing spatial masks leads to better positioning performances than tuning the weighting

scheme

For the PPP algorithm, prototype mitigation strategies based on spatial masks provide better performances than strategies based on the tuning of the weighting scheme

All those strategies are based on Spatial

Masks

The integrity monitoring stage of the SPP and PPP algorithms consists in statistically verifying the validity of the computed navigation solution

Statistical Validation Test (RTKLIB)

𝑣𝑣𝑟𝑟𝑠𝑠 =𝑃𝑃𝑟𝑟𝑠𝑠 − (�𝐷𝐷𝑟𝑟𝑠𝑠 + 𝑐𝑐 �𝑑𝑑𝑑𝑑𝑟𝑟 − 𝑐𝑐 𝑑𝑑𝑑𝑑𝑠𝑠 + 𝐼𝐼𝑟𝑟𝑠𝑠 + 𝑇𝑇𝑟𝑟𝑠𝑠)

𝜎𝜎𝑃𝑃𝑟𝑟𝑠𝑠

𝑣𝑣 = (𝑣𝑣𝑟𝑟1,𝑣𝑣𝑟𝑟2, … ,𝑣𝑣𝑟𝑟𝑛𝑛)𝑇𝑇

𝑣𝑣𝑇𝑇 𝑊𝑊 𝑣𝑣𝜎𝜎02

< 𝜒𝜒𝛼𝛼2(𝑠𝑠 −𝑚𝑚 − 1)







RAIM-FDE

Receiver Autonomous Integrity Monitoring – Fault Detection Exclusion

1 2 3 4

? ? ? ? Best of 1/2/3/4


RAIM-FDE

Receiver Autonomous Integrity Monitoring – Fault Detection Exclusion

1 2 3 4

? ? ? ? Best of 1/2/3/4

Three Approaches to design prototype mitigation strategies related to the integrity monitoring stage of the SPP and PPP Algorithms.

Original RTKLIB RAIM-FDE Technique (IM00)

Extended Technique (IM01)

Spatial Technique (IM02)

Hybrid Solution (IM03)


Advanced integrity monitoring is the key towards higher PPP performances in case of ionospheric scintillations but it requires a valid stochastic model

PPP

0

5

10

15

ε3D[m]

Processing Time

5

10

Time[s]

22h 00h 02h 04h

UTC


UTC21:20:0005:40:00

E4Episode E4

IM Original IM00 IM01a IM02b IM03i

PPP

0

5

10

15

ε3D[m]

Processing Time

5

10

Time[s]

22h 00h 02h 04h

UTC


UTC21:20:0005:40:00

E4Episode E4

IM Original IM00 IM01a IM02b IM03i

Advanced integrity monitoring is the key towards higher PPP performances in case of ionospheric scintillations but it requires a valid stochastic model

IM00IM01aIM01bIM02aIM02bIM03aIM03bIM03cIM03dIM03eIM03fIM03gIM03hIM03i

0.0 0.5 1.0 1.5 2.0 2.5 3.0

ε3D [m]


Episodes E1-E2-E3-E4

PPP

40% 50% 60% 70% 80% 90%

IM00IM01aIM01bIM02aIM02bIM03aIM03bIM03cIM03dIM03eIM03fIM03gIM03hIM03i

0 4 8 12 16

ε3D [m]


Episodes E1-E2-E3-E4

SPP

20% 40% 60% 80%



E1-E2-E3-E4

Hybrid Mitigation Strategies

Several exclusions systematically

=very resource

consuming

Hybrid prototype mitigation strategies targeting both the stochastic modelling and the integrity monitoring stages lead to the highest results for the SPP and PPP algorithms

50 55 60

SR10 [%]

35 40 45 50

SR0.5 [%]



E1-E2-E3-E4SPP

Orig

inal

WS0

1aW

S01b

WS0

2a

WS0

2b

SM01

a

SM01

b

SM02

a

SM02

b

SM03

a

SM03

b

SM03

c

IM00

IM01

aIM

01b

IM02

aIM

02b

IM03

a

IM03

b

IM03

c

IM03

d

IM03

e

IM03

f

IM03

g

IM03

h

IM03

i

PPP

Orig

inal

WS0

1a

WS0

1b

WS0

2a

WS0

2b

SM01

aSM

01b

SM02

a

SM02

b

SM03

a

SM03

bSM

03c

IM00

IM01

a

IM01

b

IM02

a

IM02

b

IM03

a

IM03

b

IM03

c

IM03

dIM

03e

IM03

fIM

03g

IM03

h

IM03

i

Original WS SM Extended Spatial Hybrid

Original (SM/IM) SM (WS) SM (SM) IM (Extended) IM (Spatial) IM (Hybrid)

Hybrid Mitigation Strategies

Spatiotemporal Mask

SPP

PPP

Improved PPP performances but

still below requirements

Hybrid prototype mitigation strategies targeting both the stochastic modelling and the integrity monitoring stages lead to the highest results for the SPP and PPP algorithms

Spatial analysis techniques are applicable to GNSS/ISMR measurements collected in a network of stations located near the magnetic equator in order to produce real-time ionospheric scintillation skymaps (H1)

• Detection, scaling and tracking of spatial autocorrelation

• Real-time ionospheric scintillation skymaps

Mitigation strategies based on real-time ionospheric scintillation skymaps improve the performances of the SPP and PPP algorithms in case of low-latitude ionospheric scintillations (H2)

• General improvement of the accuracy, continuity and reliability of the SPP and PPP algorithms

• Higher reliability of the GOS skymaps

• SPP performances improved by tuning the weighting scheme of the stochastic model• PPP performances improved by applying spatial masks

• Integrity monitoring can bring the performances to the next level in case of ionospheric scintillations

• Hybrid mitigation strategies are very powerful

• PPP performances remain below requirements during intense ionospheric scintillations at low latitudes SR0.5 = 55% max and at the cost of a heavy computational load

…this research approach constitutes an encouraging breakthrough but there is still room for improvement!

Perspectives exist to extend this research further and reach even better performances for GNSS positioning in case of ionospheric scintillations

• High-density ionospheric scintillation skymaps (space + time)

• Alternative variables to measure the impact of ionospheric scintillations on GNSS signals

• Multiple GNSS constellations

• High-density GNSS/ISMR networks

• Smartphone-based crowdsourcing

• Advanced RAIM-FDE technology combined with adaptive exclusion process (pre-processing) and stochastic model

• GOS skymaps

• Individual testing

• Phase/Code/Doppler validation tests

• Adaptive computational load

• High-latitude ionospheric scintillations

• Application of the approach in other harsh tracking environment (jamming, multipath, etc.)


Matthieu Lonchay

PhD Defense

ULiège

Friday 24 May 2019?


Matthieu Lonchay

[email protected]

PhD Defense

ULiège

Friday 24 May 2019

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