Radiolocation in Cellular Networks - Virginia Tech of A-GPS Cellular ... (for super-resolution) ......

Radiolocation in

Cellular Networks Document ID: PG-TR-090603-GDD

Date: 3 June 2009

Prof. Gregory D. Durgin

777 Atlantic Ave. Atlanta GA 30332-0250 Voice: (404)894-8169 Fax: (404)894-5935

http://www.propagation.gatech.edu

Abstract – This tutorial presents an overview of how cellular phones can be found within a cellular network for both E911 dispatching and location-based services. We show how real radio location systems work with examples of measurement campaigns in live systems. After reviewing all location techniques, significant attention is paid to emerging received signal strength “finger-printing” techniques that have been deployed across the US and beyond. The tutorial concludes with several case studies in applications and radio forensics – using cellular location to fight crime.

No portion of this document may be copied or reproduced without written (e-mail)

consent of the Georgia Institute of Technology.

1

Copyright 2006-20092

AcknowledgementsGeorgia Tech FoundationPolaris Wireless Inc.Cingular (now AT&T)Comarco WirelessNSF CAREER Grant ECS-0546955Aerospace Corp.


Credits

Albert Lu:measurementsLab Engineer2006-2008

Greg Durgin:Professortenure 2009

Josh Griffin:measurementsPhD 2009

Anil Rohatgi:measurements & web demoMS 2006

Ryan Pirkl:measurementsPhD 2009

Jian “Jet” Zhu:models, analysis, measurementsPhD 2006

Chris Durkin:models, measurementsMS 2005

Special Thanks to the Polaris Wireless Team…Robert MartinMarty FeuersteinSteve SpainTarun BhattacharyaSoma PitchaiahManlio Allegra

Prof. Paul Steffes:cellular consultantcolleague

1

Radiolocation in Cellular Networks

by Prof. Gregory D. Durgin

Section I: Basics of Cellular Location; Assisted GPS




Credits

Albert Lu:measurementsLab Engineer2006-2008

Greg Durgin:Professortenure 2009

Josh Griffin:measurementsPhD 2009

Anil Rohatgi:measurements & web demoMS 2006

Ryan Pirkl:measurementsPhD 2009

Jian “Jet” Zhu:models, analysis, measurementsPhD 2006

Chris Durkin:models, measurementsMS 2005

Special Thanks to the Polaris Wireless Team…Robert MartinMarty FeuersteinSteve SpainTarun BhattacharyaSoma PitchaiahManlio Allegra

Prof. Paul Steffes:cellular consultantcolleague

2


This Tutorial is Geared Towards…Engineers interested in how location works on a cellular networkResearchers who are looking for interested research topics in new location areasTechnologists interested in the possibilities of real-time location systems and their applicationsPeople who want to have a good time


Outline of Section IRecommended ReadingHistory of Cellular LocationTaxonomy of Location TechniquesTrade-offs for Different TechniquesReview of GPS PrinciplesAssisted Global Positioning System


E911 Position Location Mandate1996 FCC mandate for all US carriers

Original Target: <50m 67%, <100m 95% (3D)Handset Target: <100m 67%, <300m 95% (2D)Network Target: <50m 67%, <150m 95% (2D)

Numerous delays, failuresToday’s systems use a conglomerate of

Time-difference-of-arrivalAssisted GPSReceived Signal Strength Matching

3


Cell Global Identity (CGI)CGI uses serving cell identificationIn most areas, CGI error is > 1 kmEasy and cheap to implement


Cellular Global Identity (CGI)

Serving cell provides location estimate for the cell phone


MS

NeighborCell

ServingCell

Antenna Array

Location Calculation & Control

Antenna Array

AOA: Angle of Arrival– Location solution based on apparent signal arrival angle

ProsOnly 2 base stations neededLegacy handsets covered

ConsLOS condition essentialLow location accuracy Additional equipment costLong deployment period

FactsModerate open area accuracyAtlanta area deployment cost : Several million dollars

Angle-of-Arrival

4


Time-Difference Of Arrival (TDOA)

Time-of-Arrival triangulationRequires at least 3 audible base stations for 2D location


MSNeighborCell

ServingCell

Location Calculation & Control

TOA: Time of Arrival– Location solution based on apparent signal arrival time

NeighborCell

ProsLegacy handsets covered

ConsPrecisely synchronized clocksLOS condition essentialPoor in rural areaAdditional equipment for GSM,TDMA

FactsModerate open area accuracyAtlanta area deployment cost : Several million dollars

Brother technologiesTDOAE-OTD

Time-of-Arrival


Global Positioning System (GPS)Requires separate RF chain in every handsetRequires LOS to at least 3 satellitesMust be adapted to the cellular network

5


MSServing

Cell

GPS Assist Location Calc

& Control

A-GPS: Assisted Global Positioning System– Location solution based on apparent TOA from GPS space vehicles

GPS SV ProsHigh outdoor accuracy

ConsLOS condition essentialLegacy handset not covered

FactsAtlanta area deployment cost : Several million dollars to be absorbed by consumer

Assisted Global Positioning System


MSServing

Cell

Location Calc & Control

RSS: Received Signal Strength Fingerprint– Minimum of 1 site measurements attributes of received signal

ProsIndoor accuracyLow deployment costFast deployment speedLegacy handsets covered

ConsPoor coverage in rural area (Better than TOA and AOA solution though)

FactsModerate indoor and outdoor accuracyAtlanta area deployment cost : several thousand dollars

Received Signal Strength


RSS Position Location for E911

Received Signal Strength (RSS)Measurements by a cellular handset…

…are matched against a Database of RF Maps

6


PoorGreatPoorPoorRural Accuracy

LowLowHighHighOperation Cost

CoveredNot CoveredCoveredCoveredLegacy Handset

FastSlowModerateModerateDeployment Speed

RSSA-GPSAOATOA

LowModerateVery HighVery HighDeployment Cost

ModerateHighModerateModerateOutdoor AccuracyModeratePoorPoorPoorIndoor Accuracy

Comparison of Techniques


Basics of A-GPSCellular implementation of a GPS receiverOften implemented as a single chip solution in a handsetOperates similarly to a portable GPS unit, with some important simplifications


GPS Satellites24 satellites

6 constellations with 4 satellites each“spares” in orbit, too

Each orbit inclined 55 degrees, at 60 degree longitudinal orientationAll-earth coverage

Minimum 4 satellitesMaximum 9+ satellites

7


Pseudo-Noise Generator

R. Pirkl, “A Sliding Correlator Channel Sounder for Ultra-Wideband Measurements”. Georgia Tech MS Thesis. PG-TR-070510-RJP.August 2007, http://www.propagation.gatech.edu/Archive/PG_TR_070510_RJP/PG_TR_070510_RJP.pdf


Pseudo-Noise Sequence

R. Pirkl, “A Sliding Correlator Channel Sounder for Ultra-Wideband Measurements”. Georgia Tech MS Thesis. PG-TR-070510-RJP.August 2007, http://www.propagation.gatech.edu/Archive/PG_TR_070510_RJP/PG_TR_070510_RJP.pdf


GPS Satellites TransmissionUses a Gold Sequence

Two 10-bit shift registers XORed togetherEasy to generate and correlate in early hardwareDoes not require an extensive table in memory

Different delays between two sequences result in different codes

Uniquely identifies each satelliteAbout 64 “useful” delay offsets (other delay offsets result in partial correlation in some Gold codes)Correlation with a PN provides relative time-of-arrival estimate

8


GPS TransmissionThe GPS satellites also spread a low-rate (50 bps) data waveform that helps…

Correct for satellite orbit variations (ephemeris)Synchronize the clockProvide satellite “health and history” (almanac)

Spread spectrum allows reception in the presence of some interference


Example Calculation


Example Calculation

9


How Easy is it to Jam GPS?Sample Problem:

A typical, open-sky GPS receiver picks up 8 approximately equal-powered satellite signals when operated near Hartsfeld-Jackson International Airport. These signals (the GPS C/A codes) are 50 bit/sec identifcation and timing data that are spread at 1.023 Mcps to an RF bandwidth of 2 MHz. You are hired by the department of homeland security to evaluate the ability to jam GPS receivers near the airport. If a malevolent agent has a 7 dBi-gain Yagi antenna connected to a jammer/transmitter and aims this device at a plane beginning a landing approach from 5 kilometers away, how much jamming power is required to make the C/A code have a C/I < 6 dB after despreading?


Jamming GPS


Accuracy in GPS SystemsNsat – number of satellites used in estimateTb – bit duration (20 ms)Tc – chip duration (1 μs)Tint – integration period (for super-resolution)(C/N)despread – carrier-to-interference ratio after despreading (linear)

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Dilution of Precision (DOP)Previous formula assumes near-ideal arrangement of GPS satellites receivedIf decoded satellites are clustered in one area of the sky, significant DOP resultsDifferent types of DOP

GDOP – geometrical dilution of precisionPDOP – position dilution of precisionHDOP – horizontal dilution of precisionVDOP – vertical dilution of precision


Sources of Error in GPS 2.4m – Receiver noise4.3m – Ephemeris error (orbit discrepancies)3.5m – Clock errors 6.4m – Ionospheric delay2.0m – Tropospheric delay3.0m – Multipath (optimistic)32.0m – Selective Availability (discontinued)Total error of 9.5m without selective availability

T. Pratt, C. Bostian, J. Allnutt, Satellite Communications, 2th edition, Pratt, Bostian, and Allnutt. Wiley, 2003.


Augmented GPS Concepts Which errors are avoidable?

2.4m – Receiver noise4.3m – Ephemeris error (orbit discrepancies)3.5m – Satellite clock errors6.2m – Ionospheric delay2.0m –Tropospheric delay3.0m – Multipath

Make a fixed location measurement in a local area to check for these errorsBroadcast this corrective information

11


The “A” in Assisted GPS for CellularHandset receives synchronization cuesBase station has information about local atmospheric delayHandset only calculates pseudo-ranges, sends to base station for the final calculationsPrinciple issue is yield, not accuracy


Applications for Cellular NetworksE911 Emergency LocationFind a friendSite-specific advertising and vendor locationReal-time traffic modelingSite-specific driving directionsSmart network optimization (by carrier)


For Further Reading…S. Ahonen and H. Laitinen, “Database correlation method for umts location,” Jeju, South Korea, 2003, vol. vol.4 of 57th IEEE Semiannual Vehicular Technology Conference. VTC 2003 (Cat. No.03CH37431), p. 2696, IEEE.

M. Aso, M. Kawabata, and T. Hattori, “A New Location Estimation Method Based on Maximum Likelihood Function in Cellular Systems,” in Vehicular Technology Conference 2001 Fall. IEEE VTS 54th, 2001, vol. 1, pp. 106 – 110. Y. Chen and H. Kobayashi, “Signal Strength Based Indoor Geolocation,” in Communications, 2002. ICC 2002. IEEE International Conference on, May 2002, vol. 1, pp. 436 – 439.

G.D. Durgin, “Location Estimation of Wireless Terminals Using Indoor Radio Frequency Models.,”Patent filed on, December 2003.Federal Communication Commission, “Enhanced 911 - Wireless Services,” Online at http://www.fcc.gov/911/enhanced/, 1999.I. Y. Kelly, Deng Hai, and Ling Hao, “On the feasibility of the multipath fingerprint method for location finding in urban environments,” Applied Computational Electromagnetics Society Journal, vol. 15, no. 3, pp. 232, 2000.H. Koshima and J. Hoshen, “Personal Locator Services Emerge,” Spectrum, IEEE, vol. 37, pp. 41 –48, Feb 2000.Pratt, Bostian, and Allnutt, Satellite Communications, 2nd edition, Wiley, 2003. J. Warrior, E. McHenry, and K. McGee, “They Know Where You Are [location detection],” IEEE Spectrum, vol. 40, no. 7, pp. 20, 2003.

1



Section I: RSS System Trials


Outline of Section IITrials on Georgia Tech Campus

Indoor/outdoor data collectionUrban campus performance

Trials in Greenville, SCWide area urban/suburban performanceMulti-story buildingsEnhanced Algorithms

Trials in Manhattan, NYUrban environmentIndoor penetration modeling


Why RSS Signature Location?Moderate indoor and outdoor accuracyLow deployment costFast deployment speedLegacy handsets coveredCovers multiple cellular technologiesAdditional capability: indoor/outdoor discriminationFits in different sizes of networkExpandable to other communication technologies such as WLAN2

2


How an RSS Signature Engine Works


How an RSS Signature Engine Works

-74

-98

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-98

-67

-112

Corresponding toCell ID

10013

10012

10011

34101

34103

43952

RSS

ChannelNumber

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797

NMR: Network Measurement Report

MSServing

CellLocation Calc

& Control


Cramer-Rao Lower Bound

The CRLB provides a lower bound on the covariance matrix of the unbiased estimator

( ) )(CovˆCov cr zz ≥

⎥⎦

⎤⎢⎣

⎡=

yx

z where

Path loss exponent

Geometry of base station

Measurement correlation

Number of NMRs used

Number of audible base station

Measurement Error

3


CRLB- Simulation Environment

−3 −2 −1 0 1 2 3 4

−3

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Unit distance in X direction

Uni

t dis

tanc

e in

Y d

irect

ion

Baseline:Path loss exponent: 3.3

Average base station separating distance: 500(m)

Measurement correlation from same base station: 0.5

Number of NMRs: 30

Standard deviation of measurement error: 3.5

Number of audible base station: 4

Output: 82.0 m


Numerical Result: Path Loss-Related

2 2.5 3 3.5 4 4.5 520

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larger path loss exponent because higher path loss increases the uniqueness of the RSS signature.


200 400 600 800 1000 1200 1400 16000

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Performance: Base Station Separation Distance

the location error increaseslinearly with the base station separation distance.

4


Numerical Result: Measurement Error-related

2 3 4 5 6 7 80

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Sta

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of th

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3 audible base stations4 audible base stations6 audible base stations A higher standard deviation

of measurement error leads to a more inaccurate location estimation.

The standard deviationof the measurement error has to be lower than 6.5 dB so that the standard deviation of the locationerror is lower than 100 m when six base station signals are reported in an NMR.


Numerical Result: NMRs Used

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Using more NMRs increase location accuracy.

The location accuracy improves dramatically when the number ofNMRs used increases from 1 to 10.


Phase I: Georgia Tech Campus Study

5


Three Keys to Accurate RSS LocationAccuracy of Predicted Signal Database

Most difficult aspect of the problemRequires propagation modeling

Repeatability of Measurement at Handset Location Algorithm

Many different variations possibleAttempt to achieve CRLB limit


Information used in preparing RF maps:

• Base station longitude

• Base station latitude

• Sector antenna orientation

• Sector antenna height

• Frequency channel

• Transmit power

Preparing a Predicted Signal Database


Level 0 Predicted Signal Database

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Measurement Route Record at Architecture Building

Building Measurement Sample

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Measurement Photos


Indoor Location

Stat: 67% of all European cell-phone calls are indoors.

RSSI-based system perhaps the only way to discriminate indoor/outdoor users.


Predicted Signal Database Modeling

Octant model of orientation loss

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0 50 100 150 200 250

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Level 2: Calibration with outdoor measurements and indoor modeling



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Comparison of Different PSDs

Level 3Level 2Level 1Level 0

HighModerateModerateLowGenerating Cost

Very SlowModerateModerateFastGenerating Speed

BestHighModerateLowAccuracy

Level 0: Pure Prediction

Level 1: Calibration with outdoor measurement

Level 2: Calibration with outdoor measurement and indoor modeling

Level 3: Calibration with exhaustive outdoor and indoor measurements

9


Collecting Handset Test DataManually log indoor data

Connect cellular scanners to palmtop computerRecord data on indoor mapsActive call data

Separate acquisitionsScanner data for predicted signal databaseActive call data to build a test database







Repeatability MeasurementsHead-handset shadowing

Measure tracks of data in the same area, but with different orientationsAverage variation has σ = 2 dB

Small-scale fading within a “bin”Measure tracks of data through a binNote: pure Rayleigh fading predicts σ = 5 dBAverage variation of σ = 2 dBHandsets perform some temporal averaging in their measurements

10







Assumption in Absolute RSS Location: – Assume prefect knowledge of the antenna/RF chain bias between the user

handset and the scanner used to calibrated the PSD

Algorithm: Absolute RSS Location

LocationError

Statistics

67%45%20%<100m

86%78%32%Indoor/OutdoorDiscrimination Rate

95%90%60%<300m

Level 3Indoor/Outdoor Meas.

Level 2Indoor Model

Level 1Outdoor Meas. PSD level


Relative RSS Location: – Mean is removed from Both NMR and each roaster point in PSD

Algorithm: Relative RSS Location

LocationError

Statistics

60%54%54%<100m


95%94%94%<300m

Level 3Indoor/Outdoor

Meas.

Level 2Indoor Model


11


Assumption for Hybrid RSS Location: – All commercial hand sets have roughly similar attenuation in

RF chain.

Fact: – Indoor/Outdoor discrimination information is embedded in

absolute RSS– Fingerprint method is more accurate by using relative RSS

information

RSSA: Received Signal Strength Aggregate, The average of the strongest several channels, could be used to discriminate indoor/outdoor caller.

Algorithm: Hybrid RSS Location


RSS Indoor/Outdoor Discrimination

-97.8dB -85.5dB


Algorithm: Hybrid RSS Location

LocationError

Statistics

65%56%56%<100m


96%96%96%<300m


Meas.

Level 2Indoor Model


12


10 NMRs were linearly averaged to form an averaged NMR to increase the Repeatability of Measurement at Handset

Algorithm: Location With Averaging

LocationError

Statistics

78%64%61%<100m


98%98%97%<300m


Meas.

Level 2Indoor Model



Comparison of Different Algorithms

SatisfiedCloseNot goodNot goodE911 Mandate

Hybrid with Averaging

HybridRelativeAbs

SlowFastFastFastLocation Fix Generation Time

BestLowModerateHighLocation Error Statistics

BestHighLowLowDiscrimination Rate


RSS location techniques meet the FCC's requirements for E911 accuracy.

The techniques remain accurate for indoor handsets.

RSS location engine has ability to discriminate between indoor and outdoor handsets

Research provide performance up-limit for Indoor modeling

Phase I: Conclusions

13


Different commercial network trials in varied environmentsUrban, suburban, rural environments in Triton’s GSM network at

Greenville, SCLarger testing area allow the existing of egregious location errorThe effect of high-rise building

Accurate propagation modelingBased on more knowledge: building structure, building materials,

surrounding environment, multi-path effects, base station location and elevation. Reduce the time and cost of extensive drive-testing

More complicated RSS fingerprint location algorithmDSP filtering technology: matching vs. trackingIterative calculation

Phase II: Large Area GSM Experiments


The 7000 m by 9000 m test area in Greenville, SC

Extended Experiment in Greenville, SC


Longitude/Latitude map of base stations (* and O) in Greenville, SCusing DCCH 786 on Dec 14, 2004. The thick path s a single drive-test route throughthe test area.

Base stations in Greenville

14






GPS effectiveness

15


RSS in a High-rise Building


RSS Location Performance in Greenville

Location error statistics for the relative RSS-method with limited search area and distance matrix aggregate. (10 NMRs, 6 sectors)


Phase III: Manhattan, NYThe “ultimate urban environment”Indoor modeling is criticalA-GPS struggles in this kind of environment

16


Indoor Propagation Model


Example Indoor Prediction Mask

Indoor Pattering Block


Location Results in Manhattan (Fall 2005)

100%99.1%92.5%83.5%67.4%Level 1 PSD

Outdoor Test PointsIndoor Test Points

100%100%95.4%77.0%36.8%Level 2 PSD

100%100%<500m100%98.9%<300m95.3%92.0%<150m85.1%75.9%<100m68.0%25.3%<50mError

Statistics

Level 2 PSDLevel 1 PSDPSD Level

Level 1 PSD: RF database calibrated with outdoor measurement

Level 2 PSD: Calibration with outdoor measurement and indoor modeling

1



Section III: Propagation/CSI




Outline of Section IIIPath Loss ModelsData Mining Propagation Elements

Antenna PatternsRoadsTerrain DiffractionClutter Effects

Crime-Fighting with CellularCase Study: 2000 Dekalb Co. Murder Investigation

2


Path Loss Exponent Model

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Exhaustive Propagation Prediction

Virginia Tech Campus


Example RF Measurement (5.8 GHz)


Basics of a Seidel-Rappaport Model

4


Tuning a Seidel-Rappaport Model


Tuning a Seidel-Rappaport Model

This linear system is over-constrainedMay be solved using MMSE Normal Equations:


Generalized Seidel-Rappaport ModelModel distance-related loss as free spaceAdd attenuation factors for each object blocking the line-of-sightBenefits

IntuitiveFast computationLinear optimization

Pit fallsNot deterministicRequires some measurements for tuning

5


For Cellular Location Systems, We HaveDistance between base station and handsetBearing angle with respect to BS antennaBase station antenna height & downtiltGeographical Information Services

Terrain mapsClutter mapsRoad vectorsBuilding footprints (urban areas)


Distance-Related LossesUse a path-loss exponent model for lossKey differences

Other losses presentTypically lower than exponent-only models

( )d10log17.2

3.0010

⎪⎭

⎪⎬

⎫

⎪⎩

⎪⎨

⎧− 2.60

Example from 900 MHzMacrocells


Distance and Antennas Only2 x 2.5 km Received Signal Strength Map

6


Antenna PatternsMMSE fit to a 4th-order harmonic expansionMost important is the first harmonic cos θθ is angle between center sector and handset bearing angleThis model consistently outperforms the range-measure base station antenna patterns

Table 1: Basic analysis of all data, sorted by frequency and base station type.

Cell Type # Sites n Offset (dB) cos θ cos 2θ cos 3θ cos 4θ

Macro 900 558 2.56 24.3 -6.5 -0.5 0.0 -0.4 Macro 1900 2275 2.79 9.3 -9.7 0.0 0.4 -0.3 Micro 900 222 2.94 20.8 -4.7 -0.7 -0.8 -0.4 Micro 1900 71 1.73 55.6 -4.3 -1.6 -0.2 0.0


Spatial Measurement Distribution


Road Orientation LossesRoads cut “channels”through environmentPeak gain whenever BS bearing angle aligns with roadRoad orientation data is ubiquitously availableOften requires conversion to raster format

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Geometry of the Diffracting Half-Plane

y

zx

PEC ScreenShadow Boundary

Reflect

ion Bou

ndary

Point of Observation( , )

i

IncidentPlane Wave

i

i

π−

π−


Exact Solution for Diffracting PEC Edge


Geometrical Optics – Conducting Edge

Phase of the Geometrical Optics Field,

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Total ⊥ -Field, Phase of the Total ⊥ -Field, ( = 60o)

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250

300


−4λ 4λ −4λ 4λ−4λ

4λ

−4λ

4λ


Comparison of Diffraction (Normal Inc.)

0 50 100 150 200 250 300 350

-30

-20

-10

0

10

20

30

Diffracted Power for ⊥- and ||-Polarizations

Azimuth Angle

P||

/P⊥

( dB

)

90o 270o180o


Knife Edge Diffraction Formula

Total Field, Phase of the Total Field, ( = 60o)

0

50

100

150

200

250

300

360

−4λ 4λ−4λ

4λ

−4λ

4λ

−4λ 4λ

0 dB

<-40

-30

-20

-10

12


Knife Edge Formula Minus GO


0

50

100

150

200

250

300


−4λ 4λ −4λ 4λ−4λ

4λ

−4λ

4λ

0 dB

<-40

-30

-20

-10


Terrain Diffraction Example

r2=500mφ2=225o

φ3=45or

3=500mr 1=500m

TX RXφ1=27.5o

φ4=242.5o

150m 150m

r2=500mφ2=270o

φ3=90o r3=500mr 1=500m

TX RXφ1=72.5

o

φ4=287.5o

150m 150m

Wedge Geometry

Equivalent Screen Geometry


Example: FM Propagation Over TerrainWREK transmits a 91.1 MHz FM

radio signal with effective isotropic radiated power of EIRP = 54.0 dBW (about 250 kW). To reach one coverage area, the station must rely on double diffraction over the top of two terrain peaks of equal altitude that can be modeled as 90-degree wedges. How much received power would each GTD coefficient predict?

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