© dlr 2010 | project overview page 1 | 03.03 · © dlr 2010 | project overview page 3 | 03.03.2010...


Motivation

» New Galileo signals:» Improved accuracy, integrity and authentication» Massive wave of new applications in key

downstream markets

» Total market of upstream and downstream European GNSS based industry: About €300bn in 2020 [L.E.K. Consulting]

» Now time to build a successful European GNSS industry

» R&D in GRAMMAR: Boost Galileo downstream industry by providing IP for future Galileo mass market receivers


Galileo and GPS Radio Front-End

» Follow-up of FP6 GREAT results E1-E5a switching receiver chipset



» Custom RF FE measurements with laboratory (CW) and real signals

Gain Range

-70

-60

-50

-40

-30

-20

-10

0

10

20

30

0,E+00 5,E+06 1,E+07 2,E+07 2,E+07 3,E+07

Frequency [Hz]

Out

put P

ower

[dB

m]

Gain Range

-70

-60

-50

-40

-30

-20

-10

0

10

20

30

0,E+00 5,E+06 1,E+07 2,E+07 2,E+07 3,E+07

Frequency [Hz]

Out

put P

ower

[dB

m]



» E1-E5a Switching receiver single chip integration

» Two LNAs (L1/E1 & L5/E5a)» Reconfigurable down-conversion and IF sections» Digital control» Functional integration (no global area

optimization)



» Dual Channel low power E1-E5a receiver


Galileo and GPS Baseband:TUTGNSS Receiver Architecture

» Dual-Frequency GNSS Receiver» Web Server Debugging Interface» FPGA Altera Stratix II with Nios II softcore processor


Galileo and GPS Baseband:Navigation Software

» Dual-Frequency/Dual-System Navigation Software

» LS/EKF Algorithms Implemented


Galileo and GPS Advanced Baseband Algorithms:Tracking and Multipath Mitigation

» Multi-correlator-based structure» A huge number of correlators: 50-00 correlators» Reduced Search Space Maximum Likelihood (RSSML) algorithm

Block diagram of multi-correlator based DLL structure


Galileo and GPS Advanced Baseband Algorithms:Tracking and Multipath Mitigation

» DLR suburban (car) channel model» Comparison with traditional NEML

DLL» Correlator spacing: 0.025 chips» Infinite radio front-end BW

» RSSML » Good amount of memory» 81 complex correlators» Very good performance at the

cost of expensive hardware design


Galileo and GPS Advanced Baseband Algorithms:E5 Signal Simulink Model

» Transmitter: AltBOC(15,10) modulated E5 signal, IF signal (fIF= 26 MHz)


Galileo and GPS Advanced Baseband Algorithms:Acquisition

» Complex E5a reference signal» 1 ms integration

Example of time frequency correlation outputafter 1ms, C/N0=100dB-Hz

-1.5 -1 -0.5 0 0.5 1 1.50

0.5

1

1.5

2

2.5

3

3.5

4

4.5

x 104

chips

AC

F

Auto-Correlation Function at zero delay


Galileo and GPS Advanced Baseband Algorithms: Ionospheric Corrections and Range Estimation

» Traditional» Two pseudorange measurements for two

unknowns: Range and ionospheric delay» Linear Least Squares method (LLS)

» Minimize square difference between observed and modeled data

» Enhanced methods» Constrained Linear Least Squares (CLS):

» Impose constraints to avoid physical violations (negative ionospheric delay)

» Constrained Iterative Mean (CIM)» Constrained iterative range estimation

via measurement averaging» Simple implementation, less complex

than CLS

RMSE [m] versus standard deviation of measurement error [chips] for E1-E5a frequency combination


Hybrid Data Fusion:WLAN Positioning

Generation of radio map

Two approaches:•PDF approximations for probabilistic methods•RSS averages for pattern recognition

…

Access points

Samples

Coordinates

Calibration data (RSS)

…Probabilistic Methods:• Histograms• Coordinates

Pattern recognition:• Pattern vector• Coordinates

Summarize



Offline phase:Generation of radio map (= fingerprint database)•Received signal strengths•As a function of location

Online phase:Position estimation using• Radio map •Current RSS measurements

0 10 20 30 400

10

20

30

40

-87.56

-79.13

-70.69

-62.25

-53.81

-45.38AP#17

RSS(dBm)



Performance of positioning

Average error distance •A function of location

Average error (m)

0 10 20 30 400

10

20

30

40

2.81

5.63

8.44

11.25

14.06

16.88


Hybrid Data Fusion:Integration of GNSS/WLAN

» Positioning Handover

» Outdoor-to-Indoor or Indoor-to-Outdoor Environments


» 3GPP LTE: Cramer-Rao lower bound

Hybrid Data Fusion: 3GPP Long Term Evolution (LTE)


» 3GPP LTE-OFDM transmitter platform

Hybrid Data Fusion: 3GPP Long Term Evolution (LTE)


Hybrid Data Fusion: GNSS and 3GPP Long Term Evolution (LTE)

» 3GPP LTE OFDM receiver platform

State-of-the-artGNSS Receiver

GRAMMARGNSS RX Platform

GRAMMAR3GPP LTE

TDOA Platform

Ground truthTime stamp

Internal FPGABoard

Stratix IV GX

PCIe

HostPC

SSDA/DRadio Front End


Hybrid Data Fusion: GNSS and 3GPP Long Term Evolution (LTE)

» Test scenario

TE02

TE01

© dlr 2010 | project overview page 1 | 03.03 · © dlr 2010 | project overview page 3 | 03.03.2010...

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