Enabling Techniques and Algorithms for Integrated … fine... · 2012-09-03 · Global Navigation...
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Enabling Techniques and Algorithms for Integrated Communication
and Navigation Satellite Systems
Lina Deambrogio
Supervisor: Giovanni E. Corazza
Overview
• Global Navigation Satellite Systems
• Aiding Techniques• GNSS/INS integration
• Cooperative positioning
• International Projects
• Publications
• Short courses
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Global Navigation Satellite Systems (GNSS)
• Provide autonomous geo-spatial positioning with global coverage
• Active systems
• US Global Positioning System (GPS)
• European System Galileo
• Other systems
• The Russian GLONASS
• The Chinese Compass system
• The Indian Regional Navigational Satellite System (IRNSS)
• The Japanese Quasi-Zenith Satellite System (QZSS)
• All these systems will operate independently and without
interfering with each other
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System overview: Galileo
• Galileo is the European global navigation satellite
system
• Highly accurate, guaranteed global positioning
service under civilian control
• Interoperable with GPS and GLONASS (frequency
bands, waveforms)
• Constellation: 30 satellites (27 operational + 3
active spares), positioned in three circular Medium
Earth Orbit (MEO) planes at 23 222 km altitude
above the Earth
• High system reliability is of primary importance
• Especially for operation in critical environments, like
for air navigation and SoL services
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GNSS Positioning
• Estimate the user position• Triangulation based on the knowledge of the distance between user
and known points (satellites)
• Distances are computed as the time needed for the signal transmitted by the satellites to reach the user• Additional time uncertainty due to non ideal synchronization
between clocks• Not ranges but pseudoranges• At least 4 satellites are needed
User Receiver (xR , yR , zR)
i-Satellite (xsi , ysi , zsi)
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Receiver Operations
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Data Demodulation
Code Tracking
Carrier Tracking
AcquisitionPseudorange Calculation
Incoming signal
Code Synchronization Phase:• Acquisition
• Coarse timing and frequency estimation
•Tracking• Fine timing and frequency estimation
Position computation:• Ephemeris extraction• Pseudorange calculation• Navigation system solution
Navigation system
Code Acquisition
• Goal: Identify the Code Epoch (t) and the Frequency Offsets (fe) of a specific satellite signal
• Uncertainty Region (UR) is discretized into time slots t , and the frequency domain is discretized into frequency bins f:
• Two-dimensional matrix must be scanned to find Correct Hypothesis (H1)
• A large number of Incorrect Hypotheses (H0)
• Two different strategies:• Serial Search
• Parallel Search
18-10-2010 - Enabling Techniques and Algorithms for Integrated Communication and Navigation Satellite Systems
0 1-1
Peak =1
Normalized Offset
p
Hyp H1 Hyp H0Hyp H0
0.5-0.5 d
H1 H0H0
Peak =1BOC(1,1)
Normalized Offset
Code Tracking
• Refine the coarse values of code phase and frequency
• Keep track of the signal properties over time
• Necessary for pseudorangeestimation
• Code tracking
• usually based on a feedback scheme
identified as Delay Lock Loop (DLL)
• Carrier tracking
• either tracking the phase or the
frequency
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Local
Oscillator
90°
Primary Code
Generator
Incoming
Signal
E
E
L
L
IE
IL
QE
QL
Position Estimation
• Estimate the user position• Triangulation based on the knowledge of:
• The distance between user and known points (satellites)
• Distances are computed as the time needed for the signal transmitted by the satellites to reach the user• Additional time uncertainty • Not ranges but pseudoranges
• At least 4 satellites are needed
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ettczzyyxxPR
ettczzyyxxPR
ettczzyyxxPR
ettczzyyxxPR
SRRSRSRS
S
R
SRRSRSRS
S
R
SRRSRSRS
S
R
SRRSRSRS
S
R
)()()()(
)()()()(
)()()()(
)()()()(
4
2
4
2
4
2
4
4
3
2
3
2
3
2
3
3
2
2
2
2
2
2
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1
2
1
2
1
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1
1
dd
dd
dd
dd
Kalman Filter
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System
Measuring device
Kalman Filter
System error sources
Controls
System state (unknown)
Measurement error sources
Observed measurements
Optimal estimation of system states
Process Model• Known user trajectory•Uncertainties are modelled as Gaussian random variables of variance Q
Measurement Model•Measurements are affected by noise •Noise can be modelled as random Gaussian with variance R
)ˆ(ˆˆ kkkkk xHzKxx
Types of Aiding
Physical
Level
Positioning
Level
AIDING
•Assist Acquisition•Visible satellites•Doppler frequency
•AIM: Reduce TTFF• Keep tracking circuits locked
•AIM: help reacquisition
•Provide missing information during signal outages
•AIM: enhance positioning availability
1118-10-2010 - Enabling Techniques and Algorithms for Integrated Communication and Navigation Satellite Systems
Aiding Techniques
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A-GNSS P2P COOPERATION
AIDING TECHNIQUES
INS INTEGRATION
Exploit direct
communication
links
Peers are
equipped with
GNSS receivers
Different types of
receivers
Positioning: aiding scenario
• GNSS needs visibility of at least 4 satellites• Guaranteed in open sky scenarios• Not guaranteed in scenarios where the LOS between receiver and
satellite is often obstructed• Indoor• Urban canyons• Shadowed environments
• GNSS must rely on aiding information
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INS INTEGRATION
Aiding techniques
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Inertial Navigation System (INS)
• Processor and motion sensors • Accelerometers (motion sensor)• Gyroscopes (rotation sensor)
• Continuously calculate the position, attitude, and velocity• Works without the need for external references
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Estimated Position
Velocity
AttitudeComputer
Initial Attitude
abod
y
tt
t
dt)(
ωbody
Gravity and Coriolis
Correction
Navigation Computer
Resolution
Strapdown IMU
Acc
GyrosEstimated Attitude
tt
t
dt)(
Initial Velocity Initial Position
Earth Rotation
Earth Rotation
ae reve
INS Errors
• INS provide an accuratesolution for a shorttime because of theerrors affecting themeasurements:• Fixed Bias ba and bω
• White Noise wa and wω
• Colored Noise ca and cω
• Scale Factor SFa and SFω
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0 20 40 60 80 100 120 140 160 180 2000
1
2
3
4
5
6
7
8
9
10
Time [sec]
Err
or
[m]
INS error - x axis -
0.70070.7007
0.70070.7007
0.70070.7007
0.70070.7007
0.70070.7007
0.1404
0.1404
0.1404
0.1404
0.1404
0.1404
0.6972
0.6972
0.6972
0.6972
0.6972
0.6972
0.6972
0.6972
0.6972
0.6972
0.6972
X-axis
3D trajectory
Y-axis
Z-a
xis
INS Estimated Trajectory (200 seconds)
REAL User Position (4469178, 895750, 4446582)
GNSS/INS Integration
• Based on the fusion of information coming from the two navigation systems• Exploits the systems complementarities
• INS are self-contained (un-jammable) • GNSS depend on satellite signal detection (subject to outages)
• Balances out the systems errors• INS accuracy degrades with time• GNSS have bounded errors
• GNSS/INS integration:• Enhances accuracy
• GNSS can be used to calibrate the INS
• Achieves improved availability• INS estimates can substitute GNSS measurements during signal
drop-outs
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INS/GNSS integration levels
• Different integration types can be identified depending on the information made available by the GNSS
• Loose Integration• Fuses data at the position level
• position is computed indipendently by the two systems
• INS computes the position from the data coming from the sensors
• GNSS resolves the navigation system by using pseudorange measurements
• Data is blended in a linear Kalman filter
• Visibility to at least four satellites is necessary
• Tight Integration• Fuses data together at the pseudorange level
• INS computes the position from the data coming from the sensors
• GNSS provides pseudorange measurements to the visible satellites
• Data is combined through an extended Kalman filter
• Integration is possible even with less than four visible satellites
• Ultra-tight Integration• Fuses data at the signal level
• INS information is used inside the GNSS receiver tracking loops
• Ideal for challenging scenarios (as with weak-signals, interference, multipath, etc.)
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Integration schemes
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IMU
ACQ TrackingNavigation
Processor
PVT
Navigation
Computer
Inertial Navigation System
Kalman
Filtering
GNSS Receiver
Pseudo-Ranges
PVT
Position
&
Velocity
Tight
Integration Loose
Integration
PVT
Autonomous
INS Solution
Autonomous
GNSS Solution
(4 satellites )
Integrated
Solution
Front-End
Loose integration performance
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0 50 100 150 200 250-20
-10
0
10
20
30
40
time [sec]
Z E
CE
F [m
]
Loose Integration Z -axis INS-GNSS C/No=[45 35 25 25] Medium quality INS
Real Trajectory
Integrated Trajectory
INS Trajectory
GNSS LSM Trajectory
Tight integration performance
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0 50 100 150 200 250-20
-10
0
10
20
30
40
time [sec]
Z E
CE
F [
m]
Tight Integration Z -axis INS-GNSS, 3 visible satellites CN0=[45 35 35] Medium quality INS
Real Trajectory
Integrated Trajectory
INS Trajectory
Ultra-tight integration: GAUSS scheme
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• The Gaussian AUtocorrelation Scaled Sum (GAUSS) is an innovative ultra-tight integration technique
• A completely artificial autocorrelation peak is generated from the information provided by the INS
• The synthetic peak is synthetized through a Gaussian function
• The synthetic correlation is caluculated at two points corresponding to
2
2
2
)(
22
1)( INS
INSt
INS
etG
delay estimated by the INS
variance selected according to the early-late spacing
)(
)(
GNSSL
GNSSE
GG
GG
t
t
GAUSS scheme
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• The two sets of autocorrelation sets are summed non-coherently each scaled by its Mean Square Error (MSE)
Local
Oscillator
90°
Primary Code
Generator
Incoming
Signal
E
E
L
L
IE
IL
QE
QL
Estimated Delay
From INSGaussian
Generator
GE
GL
Discriminator
σ2GNSS σ2
INS
DGAUSS
INSGNSS
INSGNSSGNSSINSGAUSS
MSEMSE
DMSEDMSED
• The artificial peak retains its significance even in case of satellite blockage
• It wipes off distorsions in the autocorrelation function
• It can be straightforwardly inserted in the GNSS receiver before the navigation processor
P2P COOPERATIVE POSITIONING
Aiding Techniques
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Motivation
• The P2P paradigm:• Peers belonging to the same network
have positions and velocities that arecorrelated to each other
• Exploitation of directcommunication links among nodesin a network used to exchange aidinginformation
• Increased positioning capabilitiesespecially in those scenarios wheretriangulation would be impossiblefor a single user
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Range layer aiding
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• Range layer aiding:• Allows the exchange of information
between the peers • Provides the missing equations in the
PVT system
Different techniques have been investigated:
• GNSS data only method• using the pseudorange between aiding
peers and satellites out of user visibility
• Hybrid methods• using terrestrial measurements of the
distance betwwen aiding and aided peers
• Impact of high-end receivers
GNSS-data only Algorithm
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• The heart of the system is the Extended Kalman Filter
• Kalman Filter receives as inputs :
• Direct pseudorange measurements to the satellites in visibility
• Additional pseudoranges of neighboring peers to non visibility satellites
• To set correctly the matrices in the Kalman the coordinates of the additional satellites must be passed on to the user
This first aiding method aims at helping receivers with scarce satellite visibility to get coarse position estimation.
No correction of the exchanged pseudorangesand no information on the aiding peer position areexchanged
Hybrid Algorithm
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• Kalman Filter receives as inputs :
• Direct pseudorange measurements to the satellites in visibility
• Terrestrial ranging information from the peers
• To set correctly the matrices in the Kalman the coordinates of the additional satellites must be passed on to the user.
The presence of professional receiversthat have more accurate knowledge oftheir position is beneficial to the solutionof the navigation equations.
GNSS-data only performance
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895690
895700
895710
895720
895730
895740
895750
895760
4469140 4469150 4469160 4469170 4469180 4469190
Y-ec
ef [m
eter
]
X-ecef [meter]
GNSS-Data Only Exchange
Real Mass-Market AidingPeer Position
Calculated UserPosition
Real User Position
Hybrid performance
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895680
895690
895700
895710
895720
895730
895740
895750
895760
895770
4469130 4469140 4469150 4469160 4469170 4469180 4469190
Y-ec
ef [m
eter
]
X-ecef [meter]
Terrestrial Ranging Exchange
Calculated Mass-Market AidingPeer Position
Real Mass-MarketAiding PeerPosition
Calculated UserPosition
Real User Position
E[ex] (m) E[ey] (m) σx (m) σy (m)
1.3 -0.8 6.3 4.3
International Projects
• ESA-TT&C-I (Spread Spectrum Codes suitable for Mono-mode TT&C Transponders)
• ESA-P2P (Distributed and cooperative positioning innovative GNSS applications)
• EU-GAMMA-A (Galileo Receiver for Mass Market Applications in the Automotive Area)
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Publications
• [1] F. Bastia, L. Deambrogio, C. Palestini, M. Villanti, R. Pedone, and G.E. Corazza -“Hierarchical Code Acquisition for Dual Band GNSS Receivers”, Position Location and Navigation Symposium (PLANS2010), May 3-6, 2010, Palm Springs, California.
• [2] C. Palestini, L. Deambrogio, F. Bastia, and G. E. Corazza - “An Insider View on Tracking Loops: a Novel Ultra-Tight GNSS/INS Hybridization Approach”, Position Location and Navigation Symposium (PLANS2010), May 3-6, 2010, Palm Springs, California.
• [3] I. Thibault, G. E. Corazza and L. Deambrogio - “Random, Deterministic, and Hybrid Algorithms for Distributed Beamforming”, Advanced Satellite Multimedia Systems Conference (ASMS/SPSC 2010), September 13-15, 2010, Cagliari, Italy.
• [4] L. Deambrogio, C. Palestini, F. Bastia, G. Gabelli and G. E. Corazza “Impact of High-End Receivers in a Peer-To-Peer Cooperative Localization System”, Ubiquitous Positioning, Indoor Navigation and Location-Based Service (UPINLBS 2010), October 14-15, 2010, Kirkkonummi, Finland.
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Short Courses
• NEWCOM++ Winter School, 2-6 Febbraio 2009, Aachen, Germania
• Short-Range Positioning Systems: Fundamentals and AdvancedResearch Results with Case Studies, 5-6 March 2009, Bologna
• GPS/INS Multisensor Kalman Filter Navigation, 10-14 Maggio 2009,Parigi, Francia
• SatNEx Summer School, 27-31 Luglio 2009, Salisburgo, Austria
• Heterogeneous wireless networks: architectures, Qos performance andapplications, 9 e 11 Settembre 2009, Bologna
• Aster DOC, 5-10 Luglio 2010, Bologna
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