Enabling Techniques and Algorithms for Integrated Communication

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

218-10-2010 - Enabling Techniques and Algorithms for Integrated Communication and Navigation Satellite Systems

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


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


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)


Receiver Operations

18-10-2010 - Enabling Techniques and Algorithms for Integrated Communication and Navigation Satellite Systems 6

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


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


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


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

2

2

1

2

1

2

1

2

1

1

dd

dd

dd

dd

Kalman Filter


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


Aiding Techniques


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


INS INTEGRATION

Aiding techniques


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


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


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


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.)


Integration schemes


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


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


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


• 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


• 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


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


Range layer aiding


• 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


• 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


• 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


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


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)


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.


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


Enabling Techniques and Algorithms for Integrated Communication

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