phd defence airportnav
TRANSCRIPT
Ecole Nationale
de l’Aviation civile
LTST
LETA
Hybrid Deterministic-Statistical GPS
Multipath Simulator for Airport Navigation
Adrien Chen
PhD Defence – Dec 17th, 2010
Thesis funded by Airbus
Supervised by
LTST and LETA ENAC, France
Navigation Department Airbus Operations SAS
Reviewers
Pr. Emmanuel Duflos Ecole Centrale Lille, France
Pr. Michael Braasch Ohio University, USA
Pr. Fernando Perez-Fontan University of Vigo, Spain
Thesis Director
Dr. Christophe Macabiau ENAC, France
Supervisors
Dr. Alexandre Chabory ENAC, France
Dr. Anne-Christine Escher ENAC, France
André Bourdais Airbus Operations SAS
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Ecole Nationale
de l’Aviation civile LTST
LETA
• In civil aviation, satellite navigation is taking precedence over classical
radionavigation means.
It is more independent from the ground infrastructure of each airport,
It may provide a positioning solution for all the flight phases since it has a worldwide
coverage.
• The use of satellite navigation in airport environments raises issues related
to the proximity of the airport buildings
Parasite scattering of the direct signal coming from the satellite called multipath
appears.
• Mutipath may cause positioning errors of the aircraft within the airport, when
taxiing or standing at parking places.
Context of the study (1/2)
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Ecole Nationale
de l’Aviation civile LTST
LETA
Airbus context
• Satellite navigation is particularly interesting for airport navigation
Onboard Airport Navigation System (OANS) developed by THALES makes use of the GPS
satellites,
Deployed on Airbus A380,
Provides an acceptable indication of the position (situation awareness) despite of multipath.
• For higher requirements levels, such as guidance, a better understanding of these
phenomena is necessary.
Historical context
• Existing prediction tools of multipath
They can be deterministic, statistical or hybrid deterministic statistical,
Most of them are dedicated to telecommunication applications.
• To our knowledge, few models in the literature combine
Context of the study (2/2)
Multipath prediction + Signal processing of the GNSS receiver Realistic estimation of the range error
It is within this context that Airbus has funded this Ph.D.
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Ecole Nationale
de l’Aviation civile LTST
LETA
Objective of this Ph.D.
2 research goals
1. To develop a deterministic tool for an efficient prediction of the GNSS error due
to multipath in airport environments.
2. To add a statistical component in order to account for the limits of a pure
deterministic prediction.
Objective of the study
To develop a flexible simulation tool to analyze the error due to
multipath in the context of airport navigation with GNSS.
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Ecole Nationale
de l’Aviation civile LTST
LETA
I. Multipath in GNSS context
II. Comparisons of modeling strategies for the estimation of the
transmission channel
III. PO-based prediction of the transmission channel
IV. Application of the deterministic model to the GNSS context
V. Statistical model
VI. Comparisons with measurements
Conclusion and future works
Presentation Outline
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Ecole Nationale
de l’Aviation civile LTST
LETA
• Civil aviation context
GPS L1 C/A is currently used from en-route navigation to non-precision approach
We only consider GPS L1 C/A signal.
• Structure of the received GPS L1 C/A signal
• We also ensure the compatibility of the tool we develop with new
constellations
Imminent deployment of new GNSS constellations e.g. Galileo
I. Multipath in GNSS context Civil aviation signal
𝑠𝐶/𝐴 𝑡 = 2𝑃𝑑 𝑡 − 𝜏 𝑐 𝑡 − 𝜏 cos 2𝜋𝑓𝐿1𝑡 + 𝜙
Binary navigation
message
Code delay
Binary code Carrier
frequency
Carrier phase
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Ecole Nationale
de l’Aviation civile LTST
LETA
Definition of the transmission channel
• Satellite antenna
Emits the GPS signal with a RHCP polarization
• Ionosphere and tropospheric effects
Direct signal and multipath are affected by the same ionospheric and tropospheric effects,
Multipath can be studied independently.
I. Multipath in GNSS context Channel Modeling
Multipath
Receiver antenna
GPS receiver We focus on
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Ecole Nationale
de l’Aviation civile LTST
LETA
Wideband modeling
• Sum of delayed echoes
I. Multipath in GNSS context Transmission channel Modeling
𝑡 = 𝑎𝑚𝛿 𝑡 − 𝜏𝑚 𝑒𝑗 2𝜋𝑓𝑚𝐷 𝑡+𝜙𝑚
𝑀
𝑚=0
Multipath Parameters
𝑎𝑚 the amplitude of the 𝑚th multipath,
𝜏𝑚 the propagation delay of the 𝑚th multipath,
𝜙𝑚 the phase of the 𝑚th multipath,
𝑓𝑚𝐷 the Doppler-shift of the 𝑚th
multipath
Impulse response Spread out over a time delay
range
Frequency response frequency-selective fading
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Ecole Nationale
de l’Aviation civile LTST
LETA
• Effective height of the antenna
Link between the electromagnetic (EM) fields and the signal at the output of the antenna,
Takes into account the electric field and the direction of arrival to determine the signal phase
and amplitude.
I. Multipath in GNSS context Receiver antenna modeling
At the output of the antenna
𝑎𝑚 =𝐸 𝑚 𝜃𝑚 , 𝜑𝑚 . 𝑙 (𝜃𝑚 ,𝜑𝑚 ),
𝑉0
𝜙𝑚 = Arg 𝐸 𝑚 𝜃𝑚 , 𝜑𝑚 . 𝑙 (𝜃𝑚 , 𝜑𝑚 ) .
(𝜑𝑚 , 𝜃𝑚) azimuth, elevation of the echo,
𝐸 𝑚 𝜃𝑚 , 𝜑𝑚 electric field of the mth echo arriving at the antenna,
𝑉0 a reference voltage
The delays 𝜏𝑚 are the geometric delays of the echoes. For the direct signal, 𝜏0
is the geometric delay between the satellite and the receiver.
𝑙 (𝜃𝑚 ,𝜑𝑚)
Echo
antenna
𝑙
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Ecole Nationale
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LETA
• The GPS receiver performs a correlation of the incoming signal with a local replica
The local replica is generated with a delay error .
• Two main loops use the correlation outputs to adjust the replica
The delay lock loop (DLL) tracks the delay of the received signal,
The phase lock loop (PLL) tracks the carrier phase of the incoming signal.
• The simulator we use is a correlator output simulator
Available at the ENAC,
We do not process real GPS signals but only the outputs of the correlators (possible since we
know the GPS L1 C/A autocorrelation function),
Good trade-off between realism and efficiency.
휀𝜏 the delay estimation error,
𝐾 휀𝜏 the autocorrelation function of GPS L1 C/A
1 𝐶𝑖𝑝 = 1 (1023. 106)𝑠
The lock-loop condition is reached when
𝐾𝐸𝑎𝑟𝑙𝑦 = 𝐾𝐿𝑎𝑡𝑒 ⇒ 휀𝜏 = 0,
i.e. for a correct delay estimation.
I. Multipath in GNSS context GPS signal processing
휀𝜏
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Ecole Nationale
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LETA
• We assume that the received signal is only composed of the direct signal and one
echo
• If the echo varies slowly with respect to the time-loop constant, the resulting
correlation function is modified and becomes
𝑠 𝑡 = 𝑎0𝑑 𝑡 − 𝜏0 𝑐 𝑡 − 𝜏0 cos 2𝜋𝑓𝐿1𝑡 − 𝜙0 𝑑𝑖𝑟𝑒𝑐𝑡 𝑠𝑖𝑔𝑛𝑎𝑙
+ 𝑎1𝑑 𝑡 − 𝜏1 𝑐 𝑡 − 𝜏1 cos(2𝜋𝑓𝐿1𝑡 − 𝜙1) 𝑒𝑐𝑜 𝑠𝑖𝑔𝑛𝑎𝑙
𝐾𝑀 𝜖𝜏 = 𝐾 𝜖𝜏 + 𝛼1𝐾 𝜖𝜏 + ∆𝜏1 𝛼1 = 𝑎1/𝑎0 the relative amplitude of the multipath,
∆𝜏1 = 𝜏1 − 𝜏0 the relative delay difference.
𝛼1 = 0.5
∆𝜏1 = 0.5 𝑐𝑖𝑝
The lock-loop condition is reached when
𝐾𝐸𝑎𝑟𝑙𝑦 = 𝐾𝐿𝑎𝑡𝑒 ⇒ 휀𝜏 ≠ 0,
i.e. for an incorrect delay estimation.
Multipath error
(Note that 1 Chip 293m )
I. Multipath in GNSS context Multipath impact
≅
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Ecole Nationale
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LETA
• Mono channel prediction (1 satellite)
• 2 main blocks
The multipath generator predicts the electromagnetic fields and the multipath parameters,
The GPS receiver simulator predicts the GPS range error associated with one satellite.
• Inputs
The satellite position can be defined by its (azimuth, elevation) or derived from the almanacs,
The receiver can be static or dynamic.
• Output
The GPS range error due to multipath
I. Multipath in GNSS context General modeling
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17/12/2010
Ecole Nationale
de l’Aviation civile LTST
LETA
I. Multipath in GNSS context
II. Comparisons of modeling strategies for the estimation of the
transmission channel
III. PO-based prediction of the transmission channel
IV. Application of the deterministic model to the GNSS context
V. Statistical model
VI. Comparison with measurements
Conclusion and future works
Presentation Outline
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Ecole Nationale
de l’Aviation civile LTST
LETA
• One of the main issues of the development of a deterministic multipath
prediction is the choice of an EM computation theory.
• Available EM computation theories
– Method of moments (MoM)
– Geometrical optics (GO)
– Uniform theory of diffraction (UTD)
– Physical optics (PO)
• Strategy
MoM requires too much computation resources MoM
II. Comparison of modeling strategies of the transmission channel Choice of an Electromagnetic theory
Asymptotic
methods
Exact method
High-frequency
Approximations
GO
UTD
PO
?
Comparison of the channel parameters Use of MUSICA (available at the ENAC) for GO and UTD
Development of a PO prediction tool
Comparison of the EM fields Numerical validations with MoM for canonical configurations
Comparison from measurements
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Ecole Nationale
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LETA
II. Comparison of modeling strategies of the transmission channel Large object
UTD: 1 direct path, 1 reflected, 4 diffracted
GO: 1 direct path, 1 reflected
PO: 1 direct path, many multipath
1 - Time domain comparison
Metallic facade of an ENAC campus building
Satellite
incidence
Receiver antenna
Physically correct but no possible comparison
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Ecole Nationale
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LETA
II. Comparison of modeling strategies of the transmission channel Large object
Criterion
Computation of the RMS difference of the transfer functions
PO and UTD
match except very
near the reflector
2 - Frequency domain comparison
Metallic facade of an ENAC campus building
Computation on the (x,y) plane
Satellite
incidence
Large differences
between PO and GO
near the reflector and
also near the shadow
boundaries
For large objects
Comparison PO-GO: significant differences
Comparison PO-UTD: good agreement
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Ecole Nationale
de l’Aviation civile LTST
LETA
II. Comparison of modeling strategies of the transmission channel Small object (3.3m x 1.6m)
EM fields comparison
Computation of the RMS difference of the transfer functions
UTD
PO: 1/r expected decreasing
Near-field limit
GO and UTD cannot be used in the simulator
We choose PO since it is physically acceptable
GO
Criterion
Comparison of the fields reflected by the small metallic plate
For small objects
PO and UTD do not match
Results are non-physical
in the far-field
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17/12/2010
Ecole Nationale
de l’Aviation civile LTST
LETA
I. Multipath in GNSS context
II. Comparisons of modeling strategies for the estimation of the
transmission channel
III. PO-based prediction of the transmission channel
IV. Application of the deterministic model to the GNSS context
V. Statistical model
VI. Comparison with measurements
Conclusion and future works
Presentation Outline
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Ecole Nationale
de l’Aviation civile LTST
LETA
• The incident field is a RHCP spherical wave with origin the satellite.
• Definitions
The incident field is the field in the absence of objects,
The scattered field takes into account all the phenomena induced by the
presence of objects,
is the total field.
• Consequence
where the satellite is not in direct visibility.
𝐸𝑖
III. PO-based prediction of the transmission channel Incident / scattered field
𝐸𝑡 = 𝐸𝑖 + 𝐸𝑆
𝐸𝑠
𝐸𝑡
𝐸𝑡 = 0
No a-priori detection of shadowed areas
where the incident field is blocked.
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Ecole Nationale
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LETA
• First-order interaction
Satellite -> facets -> antenna
• PO computation
We use the Lee and Mittra fast computation technique [Lee et al., IEE AP, vol. 31,1983]
The environment has to be modeled in polygonal facets
• Only the illuminated facets scatter field
Detection: a direct path has to exist between the satellite and the facet.
III. PO-based prediction of the transmission channel First-order interaction
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Ecole Nationale
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LETA
• The environment is modelled either in oriented rectangular or triangular facets
• The facets can be either metallic or dielectric
III. PO-based prediction of the transmission channel Scene modeling
Initial rectangular meshing Refined meshing
Initial triangular meshing Refined meshing
When modeled as dielectric, a facet is modeled
as a multilayer slab of 𝑁 layers with constant
thicknesses. 𝑑𝑛 = thickness
휀𝑟𝑛 = dielectric coefficient
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Ecole Nationale
de l’Aviation civile LTST
LETA
III. PO-based prediction of the transmission channel Illustration of the first-order interaction
Satellite incidence
Scattered electric field
Total field = incident field + scattered field
Shadow region
Specular regions
Field scattered
behind
Field scattered in
front
Field scattered by
the roof
Metallic building
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Ecole Nationale
de l’Aviation civile LTST
LETA
• The simulator can compute multiple interactions up to order 2
For metallic and dielectric multilayer facets.
• PO-based technique
The objective is to reduce the computation load,
Illumination of the facets at the second order via ray methods in order to reduce the
number of facets illuminated at the 2nd order.
III. PO-based prediction of the transmission channel Second-order interaction
Propagation is computed as in ray
methods
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Ecole Nationale
de l’Aviation civile LTST
LETA
III. PO-based prediction of the transmission channel Illustration of the second-order interaction
Satellite
incidence
Scattered field
Total field = incident field + scattered field
Shadow region
Specular regions
Metallic building
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Ecole Nationale
de l’Aviation civile LTST
LETA
• The impact of the ground is taken into account in the simulator up to second-
order.
• Airport environment context
The ground may be assumed as planar and infinite,
An image source is placed below the ground plane to account for reflections from the
ground plane.
III. PO-based prediction of the transmission channel Ground modeling
1. Satellite -> ground -> antenna
2. Satellite -> ground -> facet -> antenna
3. Satellite -> facet -> ground -> antenna
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Ecole Nationale
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LETA
• We compare our PO-based prediction with MoM via FEKO in order to
validate our EM prediction with a reference method.
III. PO-based prediction of the transmission channel Numerical validation
Satellite
incidence
Satellite
incidence
First-order interaction Second-order interaction
Results are in good agreement
Our PO-based prediction method can be considered as a reference method in our context
(fundamental for the statistical component)
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17/12/2010
Ecole Nationale
de l’Aviation civile LTST
LETA
I. Multipath in GNSS context
II. Comparisons of modeling strategies for the estimation of the
transmission channel
III. PO-based prediction of the transmission channel
IV. Application of the deterministic model to the GNSS context
IV.1 Antenna mounted on the aircraft
IV.2 Description of the environment
IV.3 Prediction optimization
IV.4 Analysis of the deterministic error
IV.5 Application to Toulouse airport
Presentation Outline
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Ecole Nationale
de l’Aviation civile LTST
LETA
• Main issue
Should we model the aircraft on which the antenna is mounted as a 3D element in the scene?
• Measurement campaign performed with an airbus Aircraft [Steingass and al, 2004]
The only source of multipath is the fuselage,
Relative delays are too short to be taken into account: multipath merge with the direct signal.
• Chosen solution
The aircraft on which the antenna is mounted is not modeled as a 3D element in the scene,
The coupling between the antenna pattern and the aircraft structure is taken into account via the
modified gain pattern.
IV. Application of the deterministic model to the GNSS context Antenna mounted on an aircraft (1/2)
Schematic representation of cases where multipath could affect the antenna mounted on the airplane
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Ecole Nationale
de l’Aviation civile LTST
LETA
• We use measurements of a GPS antenna situated on an Airbus A319
Measurements have been performed for both circular polarizations
IV. Application of the deterministic model to the GNSS context Antenna mounted on an aircraft (2/2)
RHCP polarization pattern LHCP polarization pattern
(dB)
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Ecole Nationale
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LETA
IV. Application of the deterministic model to the GNSS context Determination of an appropriate description of the environment (1/2)
1. Influence of few-meters elements (windows)
2. Influence of material characteristics
Elements of few-meters have a
significant influence
Different dielectric characteristics for
a same material impact the prediction
of the range error
Satellite
incidence
Segment
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Ecole Nationale
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LETA
IV. Application of the deterministic model to the GNSS context Determination of an appropriate description of the environment (2/2)
3. Minimal size of objects in the scene
PO prediction limit :
We assess the influence of such objects when isolated
When large objects are present in the 3D scene, isolated objects of size
below 0.8m can be neglected.
20dB
4𝜆 ≃ 80cm
15m
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Ecole Nationale
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LETA
• Maximal order of interaction
Important issue since the computation of each interaction adds an important work load
IV. Application of the deterministic model to the GNSS context Prediction optimization
Computation of interactions only up to order 2
Inferior to -20dB
Amplitude of the reflected field
as a function of the interaction
order for a concrete wall of
thickness 30cm. ~
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Ecole Nationale
de l’Aviation civile LTST
LETA
• Multipath reduction process
Each illuminated facet generates a multipath: important computation load in case of 3D
scenes
• We group adjacent multipath in order to reduce their number
Via justified criteria relative to their delays, phase and Doppler,
No impact on the transfer function of the channel (numerically validated).
IV. Application of the deterministic model to the GNSS context Prediction optimization
The number of multipath is drastically reduced
Computation in front of a
70m x 16m facade
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Ecole Nationale
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LETA
• Multipath area
Area outside which the environment will not affect the position estimation
• The maximum relative delay of multipath which impacts the GPS receiver is known
• Simplification of the problem starting from another geometrical aspect
IV. Application of the deterministic model to the GNSS context Prediction optimization
~ 440m in the worst case,
This does not mean that an object
further than 440 m from the receiver
will not affect the positioning.
We compute the maximum first order
reflection illumination range in function
of the height of a building.
Radii of the multipath area 𝐷𝑚𝑎𝑥 = 460𝑚
Maximum height 𝐻𝑚𝑎𝑥 = 40𝑚
Mask angle 𝛼 = 5°
𝐷𝑚𝑎𝑥 = 𝐻𝑚𝑎𝑥
tan (𝛼)
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Ecole Nationale
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LETA
• Spatial variation of the range error in static
IV. Application of the deterministic model to the GNSS context Analysis of results
Satellite
incidence
The range error is highly
dependent on the
position of the receiver
𝜆
The error is null after 180m
as expected
Significant amplitude
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Ecole Nationale
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LETA
• Static vs. Dynamic
• With dynamic trajectories
IV. Application of the deterministic model to the GNSS context Analysis of results
Static case
Dynamic case
20m/s
10m/s
The receiver behaves as a low-
pass filter,
The range error is not so much
dependent on the receiver position,
The computation time decreases
(we do not wait at each point the
receiver convergence).
Specular region
19 000 facets
4 hours
19 000 facets
30 min
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• Presentation of the scene
Airbus access to Toulouse-Blagnac (France) airport
IV. Application of the deterministic model to the GNSS context Analysis of results
Group 1
Group 2
Group3
Weighting
Facility
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Ecole Nationale
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• Development of 3D modeling of the airport scene
Buildings and aircraft are modeled with rectangular facets
IV. Application of the deterministic model to the GNSS context Analysis of results
3D Model of the weighting facility 3D Model of aircraft
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Ecole Nationale
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• Large-scale studies
• Small-scale studies
Computation of the error on a segment,
Once the risky areas have been identified by means of a large-scale study it gives precisions on
the range error evolution within these areas.
• Dynamic trajectory studies
Simulation of a trajectory within the airport,
Prediction of the range error affecting a moving aircraft,
Allows comparing simulation results with collected measurements on a moving aircraft.
Satellite
incidence
Risky areas
IV. Application of the deterministic model to the GNSS context Suitable representation of the deterministic prediction
Mapping the range error within the airport,
Determination of risky areas for aircraft
navigation (where the range error is
significant),
It is not possible to display the high
frequency variations of the range error. 132 000 facets
12 hours
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17/12/2010
Ecole Nationale
de l’Aviation civile LTST
LETA
I. Multipath in GNSS context
II. Comparisons of modeling strategies for the estimation of the
transmission channel
III. PO-based prediction of the transmission channel
IV. Application of the deterministic model to the GNSS context
V. Statistical model
VI. Comparison with measurements
Conclusion and future works
Presentation Outline
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17/12/2010
Ecole Nationale
de l’Aviation civile LTST
LETA
• Illustration of the limits of a pure deterministic prediction
• We compute the GPS range error along the segment
First case: the building is at its initial position,
Second case: the building center is slightly modified of +5cm along the y-axis.
• As expected the range error is highly dependent on the relative position receiver-building
V. Statistical model Limits of the deterministic modeling
Moving the building center of few
centimeters modifies greatly the multipath
phase and thus the GPS range error
Necessity of a statistical component
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Ecole Nationale
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LETA
• Objective
To obtain the statistical moments of the range error (mean and standard deviation).
• It has been shown that the deterministic prediction could be considered as a
reference method in our context (validation with MoM)
The statistical model may be based on the deterministic model.
• Thus, the accuracy of the deterministic model depends on the uncertainties in the
scene data
A 3D description of a scene, even realistic, may lack of precision.
V. Statistical model Nature of the statistical component
The statistical prediction takes its origins in the
defective scene description
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Ecole Nationale
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• We base the statistical simulator on the deterministic simulator via Monte
Carlo (MC) simulations
the uncertainties in the configuration are taken into account via adding a
statistical variability to the 3D scene.
V. Statistical model General principle
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Ecole Nationale
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• We have identified eight parameters that we consider as statistical in the Monte Carlo
simulation:
the buildings positions in the horizontal plane,
the buildings orientations in the horizontal plane,
the buildings heights,
the buildings materials,
the buildings materials thicknesses,
the ground material
the height of the antenna.
• These parameters are independent for each building, in order to insure the non-correlation
of the complete geometry.
V. Statistical model Scene generation
Buildings position/height
Buildings/ground materials
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• Small-scale studies
• Parameters set to realistic values
V. Statistical model Analysis of the statistical error
𝜎𝑃 = 1𝑚 (building position)
𝜎𝜃 = 1° (building orientation)
𝜎𝐻 = 5% (building height)
Deterministic prediction is only a realization of the possible error
Statistical prediction gives a more realistic representation of the error.
Deterministic prediction
Statistical prediction
19 000 facets
12 hours
19 000 facets
30min
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Ecole Nationale
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LETA
• Large-scale studies
• Dynamic trajectory studies
Prediction of the statistical moments of the range error affecting a moving aircraft,
Allows comparing simulation results with collected measurements on a moving aircraft.
Satellite
incidence
V. Statistical model Analysis of the statistical error
Standard deviation of the predicted range error
Indication of the same risky areas as for the
deterministic predictions,
More complete information:
the variance value takes into account the
important variations of the error
132 000 facets
7 days
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17/12/2010
Ecole Nationale
de l’Aviation civile LTST
LETA
I. Multipath in GNSS context
II. Comparisons of modeling strategies for the estimation of the
transmission channel
III. PO-based prediction of the transmission channel
IV. Application of the deterministic model to the GNSS context
V. Statistical model
VI. Comparison with measurements
Conclusion and future works
Presentation Outline
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17/12/2010
Ecole Nationale
de l’Aviation civile LTST
LETA
VI. Comparison with measurements Objectives
• For one trajectory of an aircraft within Toulouse Blagnac airport
We have extracted the range error due to multipath (plus noise).
• We compare one measurement with statistical predictions
Comparison of predicted statistical moments for each point of the trajectory with the
measured range error due to multipath,
Remind that one measurement is one measured realization.
• A rigorous validation of our statistical prediction would require having access to
the statistical moments of the measurements for each point of the trajectory,
This requires to have many measurements on exactly the same trajectory and for the
same position of satellite,
Requires to have and to process a huge database.
• Synthesis
Such a comparison does not validate the simulator,
It illustrates its relevance in regards to measurements.
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17/12/2010
Ecole Nationale
de l’Aviation civile LTST
LETA
• From the measurement database we have the satellite position, the receiver
position, and the range errors.
• We do not have information regarding the presence of other aircraft in the scene
at the date at which the measurements were collected
VI. Comparison with measurements Collected measurements
Satellite
incidence
17°
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17/12/2010
Ecole Nationale
de l’Aviation civile LTST
LETA
• The magnitude of the predicted sigma is of the same order of the measured range error
(excepted in the pointed regions).
• The predicted mean is almost null for the complete trajectory
• The predicted variance remains almost constant of value around 0.2m
VI. Comparison with measurements Results without aircraft in the scene
Unpredicted error Unpredicted peak error
According to our predictions, the buildings and the ground seem to
have a weak influence on the range error in this particular scenario.
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17/12/2010
Ecole Nationale
de l’Aviation civile LTST
LETA
• We introduce 10 aircraft in the scene at realistic parking places
VI. Comparison with measurements Results with 10 aircraft in the scene
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17/12/2010
Ecole Nationale
de l’Aviation civile LTST
LETA
• The introduction of the aircraft in the scene impacts the variance prediction
The presence of aircraft in proximity impacts the range error due to multipath.
Prediction and measurement fit better.
• For such a trajectory for which multipath reaching the receiver antenna are essentially due
to the presence of other aircraft, it is important to have information regarding the location of
surrounding aircraft.
VI. Comparison with measurements Results with aircraft in the scene
Predicted error
Predicted peak error
The perturbation is
predicted at the right place
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17/12/2010
Ecole Nationale
de l’Aviation civile LTST
LETA
I. Multipath in GNSS context
II. Comparisons of modeling strategies for the estimation of the
transmission channel
III. PO-based prediction of the transmission channel
IV. Application of the deterministic model to the GNSS context
V. Statistical model
VI. Comparison with measurements
Conclusion and future works
Presentation Outline
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17/12/2010
Ecole Nationale
de l’Aviation civile LTST
LETA
• Regarding the deterministic prediction
Development of a deterministic tool for the prediction of GNSS multipath error for airport
navigation,
Geometrical optics (GO) and the uniform theory of diffraction (UTD) cannot be employed
for multipath prediction in GNSS context,
The use of physical optics (PO) for GNSS multipath prediction is numerically validated by
means of comparison with the method of moments (MoM),
The prediction of the interactions with the scene up to order 2 is sufficient.
• Regarding the addition of a statistical component
We have developed a hybrid deterministic-statistical tool based on the deterministic
prediction combined with Monte-Carlo simulations.
Conclusion Main results (1/2)
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17/12/2010
Ecole Nationale
de l’Aviation civile LTST
LETA
• In order to ensure the efficiency of the prediction tool, we have proposed
Criteria for a suitable modeling of a 3D environment,
Techniques to improve the computation efficiency of the GNSS errors due to multipath
in environments as large as an airport.
• We have proposed suitable deterministic and statistical descriptions of the
GNSS error due to multipath in environments at the scale of an airport
• We have shown that for an aircraft maneuvering at the surface of an airport, the
presence of other aircraft in proximity may be a source of GNSS positioning
error.
• By means of comparisons with measurements we have illustrated the results of
the hybrid deterministic-statistical model and found coherent results.
Conclusion Main results (2/2)
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17/12/2010
Ecole Nationale
de l’Aviation civile LTST
LETA
• Conduction of studies regarding the impact of periodic small elements of size below
0.8m which repeat themselves a great number of times
E.g. metallic window frames in a façade or corrugated metal walls of a building beyond PO limits.
• Assessing in details the impact of the temporal variability of the scene
Influence of meteorological phenomena,
A statistical description of the presence of mobiles in the scene, e.g. aircraft, could also be
proposed.
• Computation of the statistical moments of the multipath error observed within airports in
order to perform a complete validation of our tool
More measurements should be extracted.
• Efforts of simplification into a more high level model
Development of a simpler prediction model of the multipath error which settings would be based
on simulations performed with our tool,
Thesis launched by Airbus on GNSS singular effects including multipath in airports.
Conclusion Future works (1/2)
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17/12/2010
Ecole Nationale
de l’Aviation civile LTST
LETA
• The multipath prediction simulator may be used for other applications than airport
navigation with GNSS
Other signals e.g. mobile telecommunications,
Other environments e.g. urban environment.
Conclusion Future works (2/2)
Position of the emitter
Movement of the receiver
Achieved in the framework of a student project
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17/12/2010
Ecole Nationale
de l’Aviation civile LTST
LETA
Thanks for your attention
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17/12/2010
Ecole Nationale
de l’Aviation civile LTST
LETA
Urban Environment PDP
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Multipath Delay
Multip
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