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. Introduction
2. MTG INR-PS General Architecture
3. MTG INR-PS Scanning concept and internal torques calculation
4. MTG INR-PS AOCS simulator
5. MTG INR-PS LOS simulator
6. MTG INR-PS observable selection
7. MTG INR-PS navigation filter
8. Results
9. 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 ( )
1.7254
0.0037
0.025.8
2182.8 12.9 198.1
12.9 2851.0 89.3 .
198.1 89.3 2267.3
Mass kg BOL
CoG m
I kg m
=
⎛ ⎞
⎜ ⎟
= ⎜ ⎟
⎜ ⎟−⎝ ⎠
− −⎛ ⎞
⎜ ⎟
= −⎜ ⎟
⎜ ⎟−⎝ ⎠
2
1858 ( )
2.0020
0.0
0.0354
3645.37 0 16.79
0 3727.91 0 .
16.79 0 2176.68
Mass kg BOL
CoG m
I kg m
=
⎛ ⎞
⎜ ⎟
= ⎜ ⎟
⎜ ⎟−⎝ ⎠
⎛ ⎞
⎜ ⎟= ⎜ ⎟
⎜ ⎟
⎝ ⎠
Star
Tracker
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 RW
0.075Nm 68Nms
Rockwell Collin’s Teldix
15 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 torque
T_FCIBRF
• IN_INR:
– Estimate eurler angle (E123):
1) Estimate pitch
2) Estimatee roll
3) Estimate yaw
– Real euler angle (E123)
4) Real pitch
5) Real roll
6) Real yaw
– 7) Time
– Estimate (measured) position
8) Estimate r
9) Estímate latitud
10) Estimate longitud
– Real position
11) Real r
12) Real latitud
13) Real longitud
AOCS
Simulator
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
2
0
0 0
2( )
( )
( ) 2
2
1
,
2
,
s s s
d
s s
sT s
H s
s s
J J mL
KK
natural damping
m Km
frecuency coefficient
J J
J J
ξ σ σ
θ 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
7.299215.10
0
ref BRF tω ω =
⎡ ⎤
⎢ ⎥
= = ⎢ ⎥
⎢ ⎥
⎣ ⎦
1
2
2 ( 0) 2 ( 0). 2
ˆ .
ˆ ˆ
refE
IRF BRF
ref IRF BRF t IRF ORF t ORF BRF
q q q Quaternion error
q q EstimateQuaternion
q q q q
−
= =
=
=
= =
( 0)
ˆ
ˆ ˆ
E ref
BRF
ref BRF t
Estimate Angular rate
ω ω ω
ω ω
ω ω =
= −
=
=
, ,x IRF z IRF y x ze v e r e e e− ×
2
0
0
1
0
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 t
b t
⎡ ⎤
= ⎢ ⎥
⎣ ⎦
1,2,3,4
1
1
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
0
0
( ) ; ( )
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 q
f
f f
q Q q
F
f f
q
q q q
q q q
Q q
q q q
ω
φ
ω ω
ω
ω ω ω
ω ω ω
ω
ω ω ω
ω 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
mq
y
u
u bω
⎡ ⎤
= ⎢ ⎥
⎣ ⎦
= −
1
2
1
2
1
( ) ( ( ) ( ) ( )) ( )
2
( ) ( )
( )
( )
( ) 0
( ) 0
d
q t u t b t n t q t
dt
d
b t n t
dt
u t measured angular rate
b t drift ratebias
n t drift ratenoise
n t drift rateramp noise
= Ω − −
=
=
= −
= − ≈
= − ≈
State estimate vector:
State estimate model:
Extended Kalman Filter
ˆ
ˆˆˆ ;
ˆ
q
x u b
b
ω
⎡ ⎤
= = −⎢ ⎥
⎣ ⎦
ˆ ˆˆ0.5 ( ) 0
ˆ ˆ0 0
q 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 0
0 1 0 1
1 1 1 1
cx
cx
cy
cy
cz
cz
T
T
T
TT
T
c T
T T T
s
β
β
β
⎡ ⎤
⎡ ⎤⎢ ⎥
⎡ ⎤ −⎡ ⎤ ⎢ ⎥⎢ ⎥
⎢ ⎥ ⎢ ⎥ ⎢ ⎥⎢ ⎥= = −⎢ ⎥ ⎢ ⎥ ⎢ ⎥⎢ ⎥
⎢ ⎥ ⎢ ⎥ ⎢ ⎥⎢ ⎥ ⎣ ⎦⎣ ⎦
⎣ ⎦⎢ ⎥
⎣ ⎦
)
)
)
1
2
3
4
5
c
1
1 1 1 1 1
0
20º, 18º, 54º
cx
cx
cy
cy
cz
cz
T T
TT s s s s
T
TT
c
TT c c c cT
Ts
β
α γ γ α
β
α 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]
TF
CIBRF[Nm]
Tx
Ty
Tz
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]
[rad]
e1
e2
e3
0 100 200 300 400 500 600
-2
0
2
4
x 10
7 Position
Time [s]
Position[m]
x
y
z
0 100 200 300 400 500 600
-3000
-2000
-1000
0
1000
Velocity
Time [s]
Velocity[m/s]
Vx
Vy
Vz
0 100 200 300 400 500 600
-0.2
0
0.2
0.4
0.6
hBRF
Time [s]
AngularMomentum
hx
hy
hz
0 100 200 300 400 500 600
-1
0
1
2
3
x 10
-4 wBRF
Time [s]
AngularVelocity[rad/s]
wx
wy
wz
0 100 200 300 400 500 600
0
0.2
0.4
0.6
0.8
QI
RF2BRF
Time [s]
q
q1
q2
q3
q4
0 100 200 300 400 500 600
-0.5
0
0.5
1
qO
RF2BRF
Time [s]
q
q1
q2
q3
q4

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 IRF
Calculation 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
NNpixdZZ
NNpixdYY
X
−⋅⋅−=
−⋅⋅+=
=
θ
θ
( )2
1 realrealthe ρξρρ ⋅−⋅=
⎟⎟
⎠
⎞
⎜⎜
⎝
⎛ −
=
+=
⇒⎥
⎦
⎤
−=
−=
real
real
real
realrealreal
OAreal
OAreal
Y
Z
arctg
ZY
ZZZ
YYY
θ
ρ 22
OAthe
OAthe
realthethe
realthethe
ZZZ
YYY
Z
Y
+=
+=
⇒⎥
⎦
⎤
⋅−=
⋅=
'
'
cos
cos
θρ
θρ
( )
⎟
⎟
⎟
⎠
⎞
⎜
⎜
⎜
⎝
⎛
−
−⋅
++
=
'
'
''
1
2220
Z
Y
f
ZYf
U 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
U
pq
pr
qr
U 00
1
1
1
0sincos
0cossin
100
⋅
⎟
⎟
⎟
⎠
⎞
⎜
⎜
⎜
⎝
⎛
−
−
−
⋅
⎟
⎟
⎟
⎠
⎞
⎜
⎜
⎜
⎝
⎛
−
=
αα
αα
( ) ( ) ( ) ( )( ) ( )IRFmirrorIRFIRFmirrorIRFIRFLOS NUNUU ∧∧⋅−= 00 2
( )
⎟
⎟
⎟
⎠
⎞
⎜
⎜
⎜
⎝
⎛
⋅
⎟
⎟
⎟
⎟
⎟
⎟
⎠
⎞
⎜
⎜
⎜
⎜
⎜
⎜
⎝
⎛
−
=
0
cos
sin
2
cos
2
sin0
2
sin
2
cos0
001
β
β
αα
αα
IRFEWR
( ) ( )( ) ( )( ) ( )( ) ( )( ) ( )( ) ( )( )IRFNSIRFEWIRFnadirIRFEWIRFNSIRFmirrorIRFmirror RNSQREWQNQREWQRNSQNQN ,,,,,, ⋅−⋅⋅⋅=⇒ ππ
( ) ( )
( ) ⎟
⎠
⎞
⎜
⎝
⎛
−=
=
2
cos
2
sin0
001
αα
IRFnadir
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.
( )
( )
( )
( )
( )
( )
1
222
222
222
2
−⎟
⎠
⎞
⎜
⎝
⎛
+
−
+⎟
⎠
⎞
⎜
⎝
⎛
+
−
+⎟
⎠
⎞
⎜
⎝
⎛
+
−
=
+
⋅−
+
+
⋅−
+
+
⋅−
=
⎟
⎠
⎞
⎜
⎝
⎛
+
+⎟
⎠
⎞
⎜
⎝
⎛
+
+⎟
⎠
⎞
⎜
⎝
⎛
+
=
⋅−−−
=
hB
zz
hA
yy
hA
xx
CC
hB
zzz
hA
yyy
hA
xxx
BB
hB
z
hA
y
hA
x
AA
AA
CCAABBBB
dist
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 CMG
with the intention to meet the following goals:
– Good spatial resolution
– Good temporal visibility
– Good temporal stability
• Info from International Satellite Cloud Climatology Project ISCCP
along 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 Earth
2. Perform the inverse navigation function considering the predicted scan line
3. 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 value
5. Perform once more the inverse navigation function over the nearest neighbouring scan line
6. 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

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