January 23, 2006 ©KP

RFID Academic ConvocationAUTO-ID Laboratory at MIT

CWINS

Trends in RF Location Sensing Prepared by: Kaveh Pahlavan,

Prof. of ECE and CS and Director CWINS, WPI Presented by: Allan H. Levesque

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Outline

Evolution of RF Location Sensing Applications – in Commerce – in Military and Public Safety – in other disciplines

Localization Research at CWINS/WPI – RSS Localization– Accurate Localization Using TOA

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Evolution of Localization IndustryProblem of locating soldiers carrying mobile radios was first addressed in World War IIDuring the Vietnam conflict US department of defense lunched a series of GPS satellites to support military operationIn 1990 signals from GPS satellites became accessible to publicIn 1996 FCC required locating mobile phones with in 100 meters in 67% of locationsIn 1997 urban and indoor geolocation started– Military: SUO/SAS project– Commercial: PinPoint, WearNet

In 2000 indoor WiFi localization (Newbury Networks and Ekahau)In 2003 UWB localization for WPANsIn 2004 localization for sensor networksIn 2005 outdoor WiFi localization as a GPS alternatives– Commercial: WiFi positioning in outdoor areas (Skyhook)– Military: Signals of opportunity for disaster recovery (DARPA)

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Localization in Commerce

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Typical Commercial ApplicationsAsset tracking in warehouses and hospitalsDispatching and monitoring support staffLocating elderly personsLocating children in public areasEmergency management in hospitalsInvisible fencing of petsTracking golf ballsLocating surgical equipment in operation roomsGuiding visitors in museums Navigating sight impaired personsGPS complement for indoor applications

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Today Everything is a Terminal!

Printer

Scanner

iBook

VCRVCR

Entertainment /Home Networking

Location Sensors

Computer/Communication

Appliances/SecurityControl/Metering

PROGRAM

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Standards and Terminals

1

100,000

10

100

1,000

10,000

MPE

G2

Vide

o

3D G

ames

Ban

dwid

th (K

bps)

(100 Mbps)

(10 Mbps)

(1 Mbps)

Prin

ting

Inte

rnet

Acc

ess

Secu

rity

Syst

ems

Trad

ition

al G

ames

Phon

e A

pplia

nces

Rea

l Aud

io

MP3

Aud

io

Smar

t App

lianc

es

Util

ity M

eter

ing

Cam

cord

er D

V

USB

Con

nect

ors

1000,000 (1 Gbps)

Mul

tiple

HD

TV

802.15.4 WPAN

802.11 WLAN

802.15.3a WPAN

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Localization Accuracy

Mbps1 10 1000.1

Out

door

Fixed

Walk

Vehicle

Indo

or

Fixed/Desktop

Walk

Mobility

WLAN

User Bitrate, Datacom services

WPAN

WiFi Positioning (a few meters indoors)

UWB (a few centimeters indoor)

E-911 (100 meters 67%)

1000

3G Cellular

2G C

ellu

lar

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Accurate Localization in Military and Public Safety

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Typical Applications

Navigation for fireman Navigation for policemenProviding situation awareness for soldiersTracking inmates in prisonsTracking in disaster recovery zones

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SUO-SAS and Urban Fighting

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Localization Everywhere

Robot in rubble Forces inside buildingsInside caves

Urban canyons Unattended ground sensors

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Using Signals of Opportunity

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Localization in other Research Areas

Location-based handoffLocation-based routing in ad hoc networksLocation-based authentication and security

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Localization Research at CWINS/WPI

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Two TechnologiesRSS

– Simple hardware • Accuracy is not sensitive to multipath and bandwidth• Does not need synchronization• Can work with any existing infrastructure and card which can measure power

– For accurate localization • Needs high density of references or long training procedure• Needs more complex and computationally intensive algorithms

– Used for WiFi Positioning by Ekahau, Newbury Networks, PanGo, Skyhook. TOA

– Suitable for accurate indoor positioning• Only needs a few references• Does not need training

– More complex hardware • Accuracy is sensitive to multipath and bandwidth• Needs synchronization of the reference time

– Used in GPS, PinPoint, WearNet, 802.15.3 and 4.

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Two Classes of Algorithms

Tx1Tx2

Tx3

d1

d2

d3x

x

x

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x x

x x

x x

x x

x x

x x

x x

x x

x x

x

AP1 AP2

AP3

AP4 AP5

MH

100 m

ReferencePoint

100 m

GridResolution

Y

X

Distance Based Localization

Pattern Recognition

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RSS Localization at CWINSMicrosoft: Nearest Neighbor algorithm based on signature data collection in a training period.

– P. Bahl and V. Padmanabhan, “RADAR: an in-building RF-based user location and tracking system,” IEEE INFOCOM, Israel, March 2000.

Ekahau: Uses a statistical algorithms for localization with improved accuracy and reduced training data.

– T. Roos, P. Myllymaki, H. Tirri, P. Miskangas, and J. Sievanen, “A Probabilistic Approach to WLAN User Location Estimation,” International Journal of Wireless Information Networks, Vol. 9, No. 3, July 2002.

CWINS: Develops a real time laboratory testbed and a framework for comparactive performance evaluation of localization algorithms.

– M. Heidari and K. Pahlavan, Performance Evaluation of Indoor Geolocation Systems Using PROPSim Hardware and Ray Tracing Software, IWWAN’04, Oulu, Finland, May 2004.

CWINS: Uses channel modeling techniques (Ray Tracing and WiFi channel models) to eliminate the need for training data and improve the performance.

– A. Hatami, and Kaveh Pahlavan, " Comparative statistical analysis of indoor positioning using empirical data and indoor radio channel models," Consumer communications and networking conference, 2006.

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Challenges for TOA Estimation Coverage– Existing knowledge only supports coverage of the total power while

in TOA estimation we are keen on the coverage of the DP. We need more measurement and modeling effort to solve this problem.

Bandwidth– Common belief: increase in bandwidth increases the resolution

therefore UWB is the final solution– Problem: Increase in bandwidth reduces the coverage of the first

path, which means UWB is not the ultimate solution for Indoor Geolocation

UDP– Errors caused by UDP conditions are very large – Detection of UDP is difficult, so we can not avoid UDPs– If we detect the UDP and avoid, coverage become very small

Therefore: we need research in algorithms which operate in UDP conditions and provide good coverage with reasonable bandwidth

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TOA Research at CWINS

Modeling of the behavior of TOA sensors (for NSF)UWB– For high-resolution short distance (for DARPA)

Super-resolution algorithms– For signals of opportunity (with NSF and RCI)

Diversity Techniques– AOA (with Draper Laboratory)– Space and cooperative location estimation (with IWT)

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ConclusionLocalization is one of the fastest growing industries attracting strong research investments from military and commerce Variety of applications are emerging around RSS and TOA based technologies CWINS is the oldest laboratory working in both RSS and TOA technologies with close ties to commerce and militaryLocalization for RFID applications is one the areas of interest at CWINS for future research