mtg-inrps final presentation

ESTEC – MTG INR-PS Internal Presentation, 12 June 2009 page 1

ESTEC MTG INR-PS YGT Final Presentation

By Pedro J. Jurado Lozano


MTG INR-PS Internal Presentation Agenda

Content of the Presentation

1. Introduction2. MTG INR-PS General Architecture3. MTG INR-PS Scanning concept and internal torques calculation4. MTG INR-PS AOCS simulator5. MTG INR-PS LOS simulator6. MTG INR-PS observable selection7. MTG INR-PS navigation filter8. Results9. Conclusion & Future work


MTG INR-PS – Objectives

• MTG INR-PS stands for Meteosat Third Generation Image Navigation and Registration Performance Simulator.

• The main objectives of this tool are:– Estimation of the INR performances.– INR algorithmic design and assessment of filter parameters.– INR algorithmic verification.

• To fulfil those objectives, the INR-PS will:

– See how on-board observables (orbit & attitude estimation) allow geometrical restoration.

– See how on-ground observables (landmarks, horizons, etc…) allow geometrical restoration.

– Quantify the performance of this restoration depending on the Scanning geometry, AOCS configuration and accuracy, distribution and reliability of landmarks, number and configuration of ranging stations, …

– Quantify the geometrical image quality of the whole MTG system.– Allow detailed algorithmic studies in the frame of MTG project.


• The main principles are:– Capacity to model and simulate the geometry of an image including a

certain number of deformations– With “assumptions on how representative” the simulation is with respect

to MTG system characteristics.• The geometrical model can be used considering two different modes:

– IMAGE SIMULATION MODE (ISM)• Existing previous images are re-sampled to the geometrical modelling of MTG• Auxiliary data is simulated in order to distort the image with the real data simulated• INR processing with the landmarks detection and navigational filter.• Restoration of the image and comparison wrt the initial one.

– LANDMARKS SIMULATION MODE (LSM)• The performance estimation implies statistical computation required a great

number of image cases. E2E simulation including pixel image data may be very heavy, even impossible to manage due to the lack of source image data, increasing the computation time.

• SOLUTION: to insert a landmarks model which represent landmarks performances in terms of availability, accuracy and false detection.

• The subsequent navigation filtering and overall performance estimation can then be done nominally.

MTG INR-PS – General Principles I


MTG INR-PS – General Principles II

• 2 different types of correction are usually applied to minimize distortions– systematic correction, relies on image acquisition models taking into account

satellite orbit and attitude, sensor characteristics, platform sensor relationship, and terrain models. But it is very difficult to determine exact location within an image using only ancillary data

– precision correction, is feature-based, starting from the results of the systematic correction (usually accurate within a few pixels), and refining the geolocation or relative registration to subpixel precision

• Two approaches can be taken for combining systematic and precision correction

– Precision correction (or image registration) is performed after systematic correction.

– Systematic and precision corrections are integrated in a feedback loop to iteratively refine the navigation model.

• The first approach has been chosen; the navigation model will be constantly updated using different telemetry (AOCS provided knowledge) and therefore the estimated attitude information is used to calculate the residuals over the landmarks. An iterative optimization method, such as Kalman filter, is applied to the task of continually refining the knowledge of all the parameters required to accurately navigate and register images at the sub pixel level. For the sake of simplicity on the filtering, it must be emphasized that two different filters have been used for each, EW angle and NS angle, although we are dealing with a cross-coupled problem in the yaw angle.


MTG INR-PS General Architecture (1)

• The INR Performance Simulator is composed of three main modules:

• The INR On-board simulation module: – It’s the forward modelling part of the simulator. – It’s in charge of image observation, AOCS estimation with the on-board

measurements, image restoration with this AOCS information.

• The INR On-ground simulation module:– It’s the inverse modelling part of the simulator. – It’s in charge of observables selection, on-ground estimation, image

restoration with the accurate estimation.

• The INR Performance Extraction module:– It’s the output result modelling part of the simulator. – It’s in charge of the performance analysis, calculating different figures of

merit.


MTG INR-PS General Architecture (2)


MTG INR-PS – LSM Architecture


MTG INR-PS – ISM Architecture


MTG INR-PS – Scanning Concept

• A large number of scanning concepts can be envisaged• This simulator is limited to the 2-axes gimballed systems• The geometry of the gimballed system is fully determined by the

definition of 3 vectors: two scan axis and the telescope optical axis• The number of possibilities is reduced considering the following

assumptions and constraints– The fast scan direction is set along east west– The scan mirror is fixed on the fast scan mechanism– The SN scan axis shall be perpendicular to the EW scan axis for the

Nadir pointing direction– The incidence angle on the scan mirror shall be minimized in order to

reduce the polarization effect. Two possibilities • Incidence angle of 45o usual GEO configuration (GOES,SEVIRI)

• Incidence angle of 22.5o not lower for telescope accommodation constraints

– In terms of instrument accommodation, two configuration are studied:• Telescope & scan assembly on the horizontal plane• Telescope & scan assembly on the vertical plane


• Calculation based on:

– S/C position conforms to a perfect geosynchronous orbit– Instrument attitude is perfectly aligned to the orbit – Instrument scan mirror control is perfect

• Scanning pattern defined by the user using

– Total acquisition time– Swath change duration– Retrace duration– Percentage overseen– Initial NS LOS angle– Integration time

• Scanning mirror gimbals angles calculated taking into account the scanning concept

MTG INR-PS – Scanning Mirror Law Calculation


• With the model previously defined, the scanning law can be calculated

• Once the scanning mirror is perfectly defined kinematically, we need to characterised it dynamically with the help of the inertia matrix. Then, the internal torque in IRF is calculated.

MTG INR-PS – Scanning Mirror Angles and Torques


MTG INR-PS – AOCS simulator

• Simulink and Matlab• S/C in Geostationary orbit• Particularised for MTG-I Dual Wing (Astrium) and GEO-Oculus• Simulator main modules:

External Environment and Disturbances Internal Disturbances

ACOS (Sensors, OBSW and actuators)

Spacecraft Dynamics


MTG INR-PS – AOCS simulator: Spacecrafts

MTG-I Dual Wing (Astrium) GEO-Oculus

2

1723.8 ( )1.72540.00370.025.8

2182.8 12.9 198.112.9 2851.0 89.3 .198.1 89.3 2267.3

Mass kg BOL

CoG m

I kg m

=

⎛ ⎞⎜ ⎟= ⎜ ⎟⎜ ⎟−⎝ ⎠

− −⎛ ⎞⎜ ⎟= −⎜ ⎟⎜ ⎟−⎝ ⎠

2

1858 ( )2.0020

0.00.0354

3645.37 0 16.790 3727.91 0 .

16.79 0 2176.68

Mass kg BOL

CoG m

I kg m

=

⎛ ⎞⎜ ⎟= ⎜ ⎟⎜ ⎟−⎝ ⎠

⎛ ⎞⎜ ⎟= ⎜ ⎟⎜ ⎟⎝ ⎠

StarTracker

IRES Sensors

PDT Antenna

Solar Array

• Particularized elements

MTG-I Dual Wing GEO-Oculus

FCI scanning activated FCI scanning deactivated

Different amplitude of RW microvibrations

ACTUATORS Rockwell Collin’s RW0.075Nm 68Nms

Rockwell Collin’s Teldix15 Nms BBW

OBSW Different tuning for Gyro-Stellar Estimator and Control Law

SPACECRAFT Different CoG, Inertia Matrix, Mass and 3D model M-file

INTERNAL DISTURBANCES


MTG INR-PS – AOCS: Interface with INR

FCI Internal torqueT_FCIBRF

• IN_INR:– Estimate eurler angle (E123):

1) Estimate pitch2) Estimatee roll3) Estimate yaw

– Real euler angle (E123)4) Real pitch5) Real roll6) Real yaw

– 7) Time– Estimate (measured) position

8) Estimate r9) Estímate latitud10) Estimate longitud

– Real position11) Real r12) Real latitud13) Real longitud

AOCSSimulator IN_INR

INR Output

AOCS Input AOCS Output

INR Input


MTG INR-PS – AOCS simulator: ENVDIST module

• External Environment and Distrubances (force IRF and torque BRF)– Earth Gravity Field force and torque (EGM-96 Model) (1)– Sun pressure force and torque (2)– Third body effect force (Moon and Sun) (3)– Earth Magentic Field torque (IGRF95 Model) (4)– Atmospheric Drag and Solar cycle variation neglected

(1)

(2)

(3)

(4)


MTG INR-PS – AOCS simulator: INTDIST module

• Internal Distrubances (torque BRF):– SADM :Harmonics order 10 with 17.7 Hz nominal fr. (1)– Reaction Wheels (White Noise) (2)– Cryocoolers White Noise (3)– FCI disturbance torque (4)– SA flexible model (5)

(3)

(1)

(2)

(3)

(4)

(5)

2 2

2 2

20

0 0

2( )( )( ) 22

1,2

,

s s s

d

s s

sT sH ss s

J J mL

KK natural dampingm Km

frecuency coefficient

J JJ J

ξ σ σθ ξσ σ

σ ξ

σ σ ξ ξ

+ += =

+ +

= +

= =

= =

Marcel J. Sidi, Spacecraft Dynamics and Control: A practical Engineering Approach,Cambridge Aerospace Series 1997, page 292


MTG INR-PS – AOCS simulator: Spacecraft Dynamics

• Spacecraft Dynamics :– Linear dynamics (1) or orbit propagator (2) →s/c position and velocity IRF– Rotational dynamics (3) →h and w BRF; and qIRF2BRF– Orbital frame LORF (4) (qIRF2ORF and wORF)– Auxiliar quaternion (5)– Euler angles (6)– Geodetic coordinates (7)

(1)

(2)

(3)

(4)

(5)

(6)

(7)


MTG INR-PS – AOCS simulator: AOCS module

• AOCS :– Sensors (Start Tracker (APS) and Gyro (Astrix 200)) (1)– OBSW (Referenge generator, GSE and Control Law) (2)– Acutators (Reaction Wheel) (3)

(2)

(1)

(3)


MTG INR-PS – AOCS simulator: OBSW

AOCS (OBSW) :

• Reference generator:

LORF (Local Orbital Reference Frame):

• Control Law:

-5

( 0)

0

7.299215.100

ref BRF tω ω =

⎡ ⎤⎢ ⎥

= = ⎢ ⎥⎢ ⎥⎣ ⎦

1

2

2 ( 0) 2 ( 0). 2

ˆ .

ˆ ˆrefE

IRF BRF

ref IRF BRF t IRF ORF t ORF BRF

q q q Quaternion error

q q EstimateQuaternionq q q q

−

= =

=

== =

( 0)

ˆ

ˆ ˆE ref

BRF

ref BRF t

Estimate Angular rate

ω ω ω

ω ωω ω =

= −

==

, ,x IRF z IRF y x ze v e r e e e− ×

2

0010

ORF BRFq

⎡ ⎤⎢ ⎥⎢ ⎥=⎢ ⎥⎢ ⎥⎣ ⎦

c p E d ET K q K ω= − −


MTG INR-PS – AOCS simulator: OBSW

AOCS (OBSW) :

• Gyro-Stellar Estimator

State vector:

State model:

Measurement model

( )( )

( )q t

x tb t

⎡ ⎤= ⎢ ⎥

⎣ ⎦

1,2,3,4

11

2

1 1, ,

2 2, ,

4 3 2

3 4 1

2 1 4

ˆ ˆ0.5 ( )0

ˆ ˆ0.5 ( ) 0.5 ( )0 0

00

( ) ; ( )0

0

i

k

i x y z

i x y z

z y x

z x y

y x z

x y z

f qf

f fq Q q

Ff f

q

q q qq q q

Q qq q q

ωφ

ω ω

ω

ω ω ωω ω ω

ωω ω ωω ω ω

=

+

⋅Ω ⋅⎡ ⎤ ⎡ ⎤= =⎢ ⎥ ⎢ ⎥

⎣ ⎦⎣ ⎦∂ ∂⎡ ⎤

⎢ ⎥∂ ∂ ⋅Ω − ⋅⎡ ⎤⎢ ⎥= = ⎢ ⎥⎢ ⎥∂ ∂ ⎣ ⎦⎢ ⎥∂ ∂⎣ ⎦

− −⎡ ⎤⎢ ⎥− −⎢ ⎥Ω = =⎢ ⎥− − −⎢ ⎥− − − −⎢ ⎥⎣ ⎦ 1 2 3q q q

⎡ ⎤⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥− −⎣ ⎦

AIAA-82-0070 E.J. Lefferts and F.D. Markely, Kalman Filtering for Spacecraft Attitude Estimation

mqy

uu bω

⎡ ⎤= ⎢ ⎥

⎣ ⎦= −

1

2

1

2

1( ) ( ( ) ( ) ( )) ( )2

( ) ( )

( )( )( ) 0( ) 0

d q t u t b t n t q tdtd b t n tdtu t measured angular rateb t drift ratebiasn t drift rate noisen t drift rate ramp noise

= Ω − −

=

== −= − ≈= − ≈

State estimate vector:

State estimate model:

Extended Kalman Filter

ˆ ˆˆˆ ;ˆq

x u bb

ω⎡ ⎤

= = −⎢ ⎥⎣ ⎦

ˆ ˆˆ0.5 ( ) 0ˆ ˆ0 0q q

bb

ω⎡ ⎤ ⎡ ⎤⋅Ω⎡ ⎤⎢ ⎥ = ⎢ ⎥⎢ ⎥⎢ ⎥ ⎣ ⎦ ⎣ ⎦⎣ ⎦

&

&


MTG INR-PS – AOCS simulator: Actuators

• AOCS (Actuator) :

3 Reaction Wheels:• 3RW: 1RW→1 AXIS

– Bias– Noise– Scale Factor– Misaligment– Quantisation

4 Reaction Wheels:• 4RW (3 working + 1cold redundance)

Z – Earth / Yaw

X – Flight/Antiflight / Roll

Y / Pitch

20°

Reaction Wheels Torque / Spin Axes

RW1

RW3

RW4

RW572°

RW2

20°

5 Reaction Wheels:• 5RW (4 working + 1 cold redundance)

1

2

3

4

c 1 0 1 00 1 0 11 1 1 1

cx

cxcy

cy

czcz

TT

TTTT c T

T T Ts

β

β

β

⎡ ⎤⎡ ⎤⎢ ⎥⎡ ⎤ −⎡ ⎤ ⎢ ⎥⎢ ⎥⎢ ⎥ ⎢ ⎥ ⎢ ⎥⎢ ⎥= = −⎢ ⎥ ⎢ ⎥ ⎢ ⎥⎢ ⎥⎢ ⎥ ⎢ ⎥ ⎢ ⎥⎢ ⎥ ⎣ ⎦⎣ ⎦ ⎣ ⎦⎢ ⎥

⎣ ⎦

)

)

)

1

2

3

4

5

c 11 1 1 1 10

20º , 18º , 54º

cx

cxcy

cy

czcz

T TTT s s s s

T TT cTT c c c cTTs

β α γ γ α

βα γ γ α

β

β α γ

⎡ ⎤ ⎡ ⎤⎢ ⎥ ⎢ ⎥⎡ ⎤ − − −⎡ ⎤⎢ ⎥ ⎢ ⎥⎢ ⎥ ⎢ ⎥⎢ ⎥ ⎢ ⎥= =⎢ ⎥ ⎢ ⎥⎢ ⎥ ⎢ ⎥⎢ ⎥ ⎢ ⎥− −⎢ ⎥ ⎣ ⎦ ⎢ ⎥⎣ ⎦⎢ ⎥ ⎢ ⎥⎣ ⎦⎣ ⎦

= = =

)

)

)


MTG INR-PS – AOCS simulator

• Simulation Results :

0 100 200 300 400 500 600-1.5

-1

-0.5

0

0.5

1 FCI

Time [s]

TFC

IBR

F [N

m]

TxTyTz

0 100 200 300 400 500 600-1.6

-1.4

-1.2

-1

-0.8

-0.6

-0.4

-0.2

0 Euler angels

Time [s] [r

ad]

e1e2e3

0 100 200 300 400 500 600-2

0

2

4x 107 Position

Time [s]

Pos

ition

[m]

xyz

0 100 200 300 400 500 600-3000

-2000

-1000

0

1000 Velocity

Time [s]

Vel

ocity

[m/s

]

VxVyVz

0 100 200 300 400 500 600-0.2

0

0.2

0.4

0.6 hBRF

Time [s]

Ang

ular

Mom

entu

m

hxhyhz

0 100 200 300 400 500 600-1

0

1

2

3x 10-4 wBRF

Time [s]

Ang

ular

Vel

ocity

[rad

/s]

wxwywz

0 100 200 300 400 500 6000

0.2

0.4

0.6

0.8 QIRF2BRF

Time [s]

q

q1q2q3q4

0 100 200 300 400 500 600-0.5

0

0.5

1qORF2BRF

Time [s]

q

q1q2q3q4


MTG INR-PS – LOS simulator

• General navigation equation

Point on earth on ECEF

coordinates

S/C position on ECEF coordinates

Earth surface computation

( ) ( ) ( ) ( ) ( )[ ] [ ] ( ) ( ) ( )[ ] ( ) ( ) ( )[ ] ( )( )jtinstrumentiUtttRtRtPtYRRttRtdisttSjitT IRFZYXZYXZYXZYX ,,,,,,2,2,00,,,, 123 ⋅⋅⋅⋅−⋅+= ϕϕϕππθλ

S/C angular position

ORBIT MODEL

Intermediate transformation

Attitude information

Instrument orientation wrt

satellite

Instrument auxiliary measurements +

instrument scan model +

instrument focal plane definition

Sensing element number

INSTRUMENT TELEMETRY

LOS in IRFCalculation inside

instrument

LOS in BRFLOS in LORFLOS in ECLFLOS in ECEF


MTG INR-PS – Focal plane & telescope axis definition

• GENERAL MODEL: LOS simulator is ready to receive a file with the position of each detector in telescope axis for each time (X(i,t),Y(i,t),Z(i,t))

• SIMPLIFIED MODEL:– The two focal planes tilts are considered second order effects and they are taken

into account with the movement of the optical axis.– The detectors are rigidly connected– Rotation around telescope axis is considered– Optical axis that intersect the focal plane in OOA=(YOA,ZOA)

( )( )00

00

_sin_cos

0

NNpixdZZNNpixdYY

X

−⋅⋅−=−⋅⋅+=

=

θθ

( )21 realrealthe ρξρρ ⋅−⋅=

⎟⎟⎠

⎞⎜⎜⎝

⎛ −=

+=⇒⎥

⎦

⎤−=−=

real

realreal

realrealreal

OAreal

OAreal

YZarctg

ZY

ZZZYYY

θ

ρ 22

OAthe

OAthe

realthethe

realthethe

ZZZYYY

ZY

+=+=

⇒⎥⎦

⎤⋅−=

⋅=''

coscos

θρθρ

( )⎟⎟⎟

⎠

⎞

⎜⎜⎜

⎝

⎛

−−⋅

++=

''

''1

2220

ZYf

ZYfU TF


MTG INR-PS – Scan geometry

• The objective is to compute the LOS in IRF (ULOS)IRF after reflection onto the scan mirror. Example for the baseline V45/YZ67.5/X

• By convention, the Nadir direction is pointed for NS=EW=0o.• First step is obtain (U0)IRF. Here, some misalignments between telescope

and scan assembly occurs. At first order

• Second step is calculate the mirror normal considering a misalignment β corresponding to a non perpendicularity between rotation axis

• Last step is compute the mirror reflection equation

( ) ( )TFIRF Upq

prqr

U 00

11

1

0sincos0cossin100

⋅⎟⎟⎟

⎠

⎞

⎜⎜⎜

⎝

⎛

−−

−⋅

⎟⎟⎟

⎠

⎞

⎜⎜⎜

⎝

⎛

−=

αααα

( ) ( ) ( ) ( )( ) ( )IRFmirrorIRFIRFmirrorIRFIRFLOS NUNUU ∧∧⋅−= 00 2

( )⎟⎟⎟

⎠

⎞

⎜⎜⎜

⎝

⎛⋅

⎟⎟⎟⎟⎟⎟

⎠

⎞

⎜⎜⎜⎜⎜⎜

⎝

⎛

−

=0

cossin

2cos

2sin0

2sin

2cos0

001ββ

αα

ααIRFEWR

( ) ( )( ) ( )( ) ( )( ) ( )( ) ( )( ) ( )( )IRFNSIRFEWIRFnadirIRFEWIRFNSIRFmirrorIRFmirror RNSQREWQNQREWQRNSQNQN ,,,,,, ⋅−⋅⋅⋅=⇒ ππ

( ) ( )

( ) ⎟⎠⎞

⎜⎝⎛ −=

=

2cos

2sin0

001

ααIRFnadir

IRFNS

N

R


MTG INR-PS – Optical bed & orbit

• Transform from IRF to BRF– 3 Euler angle (ϕ1(t), ϕ2(t), ϕ3(t))– Constant at first order– Thermo elastic deformation

• Transform form BRF to LORF– Attitude information must be used.

• Transform form LORF to ECLF– It’s a mathematical transformation

and therefore it’s constant.• Transform form ECLF to ECEF

– Orbit information must be used

• At this point, we have a LOS simulator taking into account– Focal plane definition errors (with tilts considered as distortion on the optical axis)– Misalignment between the telescope and the scan assembly– Misalignment corresponding to non perpendicularity between rotation axes– Misalignment between satellite and optical bed– AOCS data from simulation with all the onboard hardware and software.

( )( )

( )( )

( )( )

1222

222

222

2

−⎟⎠⎞

⎜⎝⎛

+−

+⎟⎠⎞

⎜⎝⎛

+−

+⎟⎠⎞

⎜⎝⎛

+−

=

+⋅−

++

⋅−+

+⋅−

=

⎟⎠⎞

⎜⎝⎛

++⎟

⎠⎞

⎜⎝⎛

++⎟

⎠⎞

⎜⎝⎛

+=

⋅−−−=

hBzz

hAyy

hAxxCC

hBzzz

hAyyy

hAxxxBB

hBz

hAy

hAxAA

AACCAABBBBdist

OSOSOS

EOSEOSEOS

EEE( ) ( )( )( )

( )ECEFLOS

SSS

TTT

EEEECEFLOS

UdistOSOT

zyxOS

zyxOT

zyxU

⋅+=⇒⎥⎥⎥

⎦

⎤

=

=

=


• The relation between image coordinates and geographical coordinates is determined by the concatenation of two functions in each direction:

• PROJECTION: The normalized geostationary projection is used• SCALING: linear relationship between intermediate coordinates (x,y)

and the image coordinates (c,l)

MTG INR-PS – Projection and Scaling functions


MTG INR-PS – Observables selection

• Four types of on-ground observables can be simulated:– Landmarks

• Provide 2D information EW and NS directions• Good landmark catalogue with well defined information

– Ground Station ranging• Provide 3D information range + elevation + azimuth• Ranging stations database including the location of Darmstadt, Kourou,

Maspalomas, etc…– Stars

• Provide 2D information EW and NS directions• Star database built from Hipparcos catalogue filtered with declination

between ±10o and SNR higher than 3.– Horizons

• Provide 1D information along Earth radii.• The Earth can be modelled as the WGS84 ellipsoid.• The Greenwich hour angle can be assumed to be zero at the beginning of the

simulation


MTG INR-PS – Landmark database I

• The landmark database to be used has been built by LOGICA CMGwith the intention to meet the following goals:– Good spatial resolution– Good temporal visibility– Good temporal stability

• Info from International Satellite Cloud Climatology Project ISCCPalong with experience gained form operational landmarks in METEOSAT project was used to help determine appropriate detection probabilities


MTG INR-PS – Landmark database II


MTG INR-PS – Landmark database III


• Inverse navigation with the real state vector • Gaussian white noise application in EW and NS position to be

representative of the performance of the landmark determination algorithm.

• A given ratio of these landmarks can be simulated as wrong landmarks, and for those randomly selected ones, a higher noise is applied.

• The problem is not unique-defined.

MTG INR-PS – Landmark position determination


MTG INR-PS – Inverse Navigation I

• This function finds the detector and time corresponding to a point on Earth (landmark).But the overlaps between scan lines must be managed. Non unique defined problem, for point within an overlap area, we get two possible results.

• ASTRIUM SOLUTION – The inverse navigation is computed considering a “virtual focal plane” including sufficient

number of pixels to cover the whole Earth. If the real detector contains N sample, the inverse navigation function returns a number that can be compared with the real range [0,N-1]:

• if inside the range, the points is observed by the current scan line.• if outside, the point is observed by another scan line. Then, the process consists :

1. predict the scan line considering GEOS coordinates of the point on Earth2. Perform the inverse navigation function considering the predicted scan line3. While the predicted scan line is not the correct one, correct the prediction and iterate on 2)4. Once the scan line is found to be correct, set the sample coordinate as a correct value5. Perform once more the inverse navigation function over the nearest neighbouring scan line6. If the point is also observed during this second scan line, set the sample pixel coordinates as a second correct value (in this case, we are

within an overlap area).

• CURRENT SOLUTION – Considering small excursions of the state vector wrt ideal one, a first inverse navigation pre-

computation considering this ideal state vector is done. – Later on, a final inverse navigation is computed with the real state vector over the scan line

pre-computed and the two adjacent ones.– The algorithm is completely analytical. The point on Earth LOS projection on the focal plane is

calculated all over the time and a final intersection of this projection with the array of detectors gives us the detector and the time in which this location on the Earth is seen.


MTG INR-PS – Inverse Navigation II


MTG INR-PS – Inverse Navigation III


• For each landmark, a viewing direction residual is computed.• The residual at the date of observation is the angle between

– The estimated LOS from attitude, orbit and focal plane geometry raw knowledge as given by the AOCS

– The objective LOS given by direction from S/C orbital position, as given by the AOCS data, to the landmark ground location.

• At each landmark, the residual angles is decomposed in term of azimuth and elevation. Those values are given as input on the navigation filter

MTG INR-PS – Landmark residual computation


MTG INR-PS – Navigation Filtering

• The filter only estimates tiny deviations of the states with respect to the best knowledge: all macroscopic, zero-order deviation are already removed from the residual.

• So, the system dynamics for this INR application is ideally linear– The attitude has only small angles excursions– Thermal distortions are very small angles too– Scan misalignments are small angles– Orbital errors are in μradians range when express in angular errors

• This guarantees the validity of a purely LINEAR KALMAN FILTER

• The filter provides the estimated states and the estimation covariance for each state.


MTG INR-PS – Filter model

• Difficult problem. How to define the complete model?• It’s better to begin with a filter implementation decoupled in both EW

and NS directions. This model has several problems.• State vector compound by: The state evolution

– Pointing state – Linear drift state– Orbital position error state– Scan misalignment error– Scan misalignment drift error

• The landmark observation– Direct observation of the attitude state – Indirect observation of linear drift effect

via a parallax sensitivity . – Indirect observation of the scan misalignment error

via the sensitivity factor , which is directly the value of the scan angle on the other axis

• The propagation with the model

• The observable updating

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MTG INR-PS Results

• These results correspond to a detail analysis of the FCI nominal case 1Km in a sequence of 21 FDC images.

• This case is considered the nominal case for performance evaluations

• On top of that, it must be pointed out that:– All contributors to the navigation error were included apart from the yaw

external misalignment because yaw was not estimated by the navigation filter as stated before.

– The less populated landmark database was used.– The misalignment given by the thermal files were those corresponding to

equinox (Sun eclipse around midnight)

• Due to these facts, this case can be considered as a worst case for INR although no cloud coverage statistics have been considered and the 442 landmarks on the rougher database were used.


MTG INR-PS Results: Landmark residuals I


MTG INR-PS Results: Landmark residuals II


MTG INR-PS Results: Azimuth and Elevation Navigation Errors I


MTG INR-PS Results: Azimuth and Elevation Navigation Errors II


MTG INR-PS Results: State vector parameters


MTG INR-PS Results: Covariance analysis


Conclusions & Future Work


Conclusions

• A simulator giving the MTG navigation performances is available with some unavoidable assumptions and restrictions.

• Some elements of the simulator are appropriate enough, some others need to be improved.

• But a first step towards a more accurate simulations is there

• A straightforward application to GEO-Oculus is also there taking into account

– More simplicity because we get rid of scanning– More complexity because the inverse navigation has to be modified on

the intersection between projection on the focal plane and array of detectors.


Future Work

• Introduction of cloud coverage statistics and false detection (done but not applied yet)

• Fixed-lag filter formulation (on-going)• Introduction of virtual ground points on the overlap area (landmarks

without error) to obtain their values on the filtering and compare later. Initial idea to get the Inter-swath registration (discussed with Donny)

• Possible temporal pre-processing of the landmark observables (idea from Pieter van den Braembussche)

• AOCS errors generated directly to better characterized the INR-PS software.

• Related to the previous point, introduction of sharp manoeuvres outside the nominal stabilized status. Temporal response studies

• POSSIBLE SOLUTION Use of least-square formulation supposed we have the state vector parameter functions.

mtg-inrps final presentation

Documents