Bouncing off Walls and Trees: Multipath Channel Modeling for ...

Bouncing off Walls and Trees: Multipath Channel Modelingfor Satellite Navigation from the Samples’ Point of View

F. M. Schubert

German Aerospace Center (DLR)Institute for Communications and Navigation

The Multipath Problem Channel Modeling Simulation Summary

ESA Networking/Partnering Initiative (NPI)Partner institutions

ESA’s Networking and Partnering Initiative

connects ESA to universities through PhD exchange

Partner institutions

German Aerospace Center (DLR)

Institute for Communications and Navigation

European Space Agency (ESA/ESTEC/TEC-EEP)

European Space Research and Technology Center

Aalborg University

Navigation and Communications Section

TUM Navigation Colloquium 2 GNSS Channel Modeling and its Application in Simulation


GPS C/A code correlation function

Satellites send spreading codes

Receiver correlates rx signal with locally generated code replica

Correlation function R(τ) = 1Tc

∫ Tc

0 c(t)x(t − tsp/2− τ)dt

−101

C/A code, Prompt

Cod

e

−101

Early

Cod

e

0 2 4 6 8 10

−101

Late

Cod

e

Time [µs]−2 −1 0 1 2

−2

−1

0

1

2

3C/A code ACF, chip spacing 1

Time delay [chips]

Cor

rela

tion

early−latemultipath contribution (t = 0.4) sum



GPS Receiver Tracking Loop Structure



Effects of Multipath Propagation on GNSS ReceiversTwo-Ray Model

Two-Ray example, receiver reads

line-of-sight signal (LOS)

one additional ray

Example error envelope fordifferent delays

−5 0 5 10 15 20 25 30

0

0.2

0.4

0.6

0.8

1

1.2

delay/m

|P|

ray 1ray 2 (real)ray 2 (imag)

wave propagation effects in urban and rural areas lead tostrong multipath reception

reflection, scattering, diffraction on buildings, trees, etc.

many echoes impinge within few nanoseconds after LOS



GNSS performance in diffcult, high-multipath environmentsHow to Analyse Multi-Path Disturbances?

multipath propagation and shadowing are dominant errorsourcesanalysis

analytically: works only for single echo (error envelope)GNSS measurements in position domain: only sum of effectsvisible, not the respective contributionssample-level simulation using a channel model

computationally expensive due to high signal bandwidths→ new simulator for fast processing of channel data needed



DLR Land Mobile Satellite Channel SoundingMeasurementsMeasurement campaign

To get to know the GNSS propagation channel:measurements have to be conducted

DLR conducted field measurementsin 2002 for urban, sub-urban, rural,and pedestrian scenarios

frequency: 1460− 1560 MHz(L-band)

bandwidth: 100 Mhz

power: 10 W (EIRP)



DLR Land Mobile Satellite Channel SoundingMeasurementsResults

Raw measurementsESPRIT super-resolutionresult

Delay [ns] Delay [ns]



DLR GNSS Urban Channel Model Structure



Time-Variant Channel Impulse Responses (CIR)

Sample output of DLR GNSS urbanchannel model

time variable t, delay variable τ , update rate fCIR

t

tA

1/fCIR



DLR GNSS Urban Channel Model Output

time-variant CIRs

Power Delay Profile:2D histogram p(P, τ)

0 100 200 300 400 500−30

−25

−20

−15

−10

−5

0

Power delay profile − probability density function

Delay [ns]

Pow

er [d

B]

10−7

10−6

10−5

10−4

10−3

10−2

10−1

Resulting power delay profile of asample urban simulation run

CIR rate: 300 Hz

simulated time: 5 s

max vehicle speed: 50 km/h

satellite elevation: 30◦

satellite azimut: −45◦



Radio Channel Characteristics of Rural Environments

Rural measurements cover

villages

vegetationtrees, alleys, forests

electricity poles

Modeling approaches

statistic of all measurements

analyze measurements andidentify contributors

Synthesis approach⇒ at first, single trees will beanalyzed



Radio Channel Characteristics of Rural Environments



Rural Measurements, Analysis of Single Trees

track in open fieldneeded, withoutbuildings

van trajectory channel impulse responses



Wave Propagation Effects Caused by Single TreesTree Parameterization

Goaldevelop wide-band channelmodel for trees

leaves cause mainlyattenuation (water content)branches reponsible forscattering (wavelength)

Model properties

constant specificattenuation for tree canopyand trunknumber of point scatterersinside canopy



Rural Measurements, Analysis of Single TreesDelay Spread Determination

minimum excess distance

maximum excess distance



Treetop Scattering

transmitterreceiver

d (t)LOS

d (t)tx-tree

d (t)tree-rx

a

point sources drawn whenincident angle changes

multiple scattering insidetreetop up to 3rd order

specific attenuation modeledfor treetop and trunk



Rural channel model output, signal model

Complex amplitude of ith point source

ai (t) =

√Pmax

N· 1

[de,i (t)]2· e

j ·2π„

de,i (t)

λ

«(1)

Resulting signal at the receiver (LOS + N point sources incanopy):

s(t, τ) = k · e j ·2π“

dLOS(t)

λ

”δ(τ) +

N∑i=1

ai (t)δ(τ − τi ) (2)



Time-variance of the Radio ChannelArtificial Scenery



Rural Channel Model OutputComparison: Raw Measurements vs. Channel Model Output

raw channel soundingmeasurements

model output



GNSS performance in diffcult, high-multipath environmentsHow to Analyse Multi-Path Disturbances?

GNSS channel model/measurements X

description of time-domain simulation



Time-Domain Simulation, Structure

Channel model output is used

Simulation chain from sender to receiver



Time-Variant Channel Impulse Responses (CIR)How to use continous CIRs in a discrete time-domain simulation?



Time-Variant Channel Impulse Responses (CIR)Using Channel Model Data: CIR → FIR Coefficients Interpolation

−1 0 1 2 3 4 5 6

x 10−8

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

delay τ [s]

mag

nitu

de

CIR impulsessinc for CIR impulse 1sinc for CIR impulse 2sum of sinc functionsFIR coefficients

Time-continuous CIR impulsesmust be interpolated totime-discrete FIR coefficients

Low-pass interpolation:

FIR(t) =mX

k=0

ACIR (k) ·sin[ωmax(t − TCIR (k))]

ωmax(t − TCIR (k))

ωmax = 2πfsmpl

2

Example: fsmpl = 100 MHz



Time-Variant Channel Impulse Responses (CIR)CIR Usage in SNACS

−1 0 1 2 3 4 5 6

x 10−8

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

delay τ [s]

mag

nitu

de

CIR impulsessinc for CIR impulse 1sinc for CIR impulse 2sum of sinc functionsFIR coefficients



SNACS GNSS Simulation ChainImplementation

modular object-oriented approach, written in C++parallel processing

every processing block is implemented as its own threadcomplex convolution expands to multiple threads

blocks are connected with circular buffers (asynchronousaccess)



GNSS Simulation ChainSNACS Demonstration

simulation parameters Configuration File

sampling frequency 40 MHz

signal GPS C/A

two-sided bandwidth 10.23 MHz

ADC resolution 3 bit

early-late spacing 0.1 chips

DLL discriminator early-late

correlation time 0.001 s

Simulation: {SamplingFrequency = 40e6; // HzSignalLength = 10.0; // sIntermediateFrequency = 9e6; // HzSNBlocks = (

{ Type = ”snSignalGenerateGPS”;SignalType = ”C/A”; }

{ Type = ”snProcessorLPF”;CutOffFrequency = 10.23e6; }

{ Type = ”snProcessorChannel”;Filename = ”/CIRs/DLR-Urban-Elevation-25.h5”;

},

{ Type = ”snProcessorADC”; },

{ Type = ”snSDRGPS”;SignalType = ”C/A”;DiscriminatorType = ”EML”;...

}

);

};



SNACS Demonstration 1



GNSS Simulation ChainSNACS Demonstration

simulation parameters Configuration File

sampling frequency 40 MHz

signal GPS C/A

two-sided bandwidth 10.23 MHz

ADC resolution 3 bit

early-late spacing 0.1 chips

DLL discriminator early-late

correlation time 0.001 s

Simulation: {SamplingFrequency = 40e6; // HzSignalLength = 10.0; // sIntermediateFrequency = 9e6; // HzSNBlocks = (

{ Type = ”snSignalGenerateGPS”;SignalType = ”C/A”; }

{ Type = ”snProcessorLPF”;CutOffFrequency = 10.23e6; }

{ Type = ”snProcessorChannel”;Filename = ”/CIRs/DLR-Urban-Elevation-25.h5”;

},

{ Type = ”snProcessorADC”; },

{ Type = ”snSDRGPS”;SignalType = ”C/A”;DiscriminatorType = ”EML”;...

}

);

};



SNACS Demonstration 2



SNACS simulation result

GPS C/A signal

standard DLL



Simulation with Raw Measurement DataDrive through an Alley



Simulation with Raw Measurement DataDrive through an Alley



Bouncing off Walls and Trees: Multipath ChannelModeling for Satellite Navigation from the Samples’ Pointof View

Conclusion

flexible wide-band rural channel model is being developed

GNSS sample-level software simulator, C++, multi-threading

usage of channel model data and raw channel measurements

Future

Rural Channel Model

process all available measurement data for single treescatteringinclude electricity poles, forrests, and buildings

SNACS time-domain GNSS simulation

Galileo signals implementationmulti-link simulation



Bouncing off Walls and Trees: Multipath ChannelModeling for Satellite Navigation from the Samples’ Pointof View

SNACS is an open-source project hosted onhttp://snacs.sourceforge.net

Thank you very much for your attention!

[email protected]


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