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Tracking Moving Devices with the Cricket Location System Adam Smith, Hari Balakrishnan, Michel Goraczko, and Nissanka Priyantha Mobisys, 2004 Ku Dara Contents Introduction Tracking Algorithm Hybrid Architecture Evaluation Conclusion Human navigation Multi-player games

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Tracking moving devices with the cricket location system l.jpg

Tracking Moving Devices with the Cricket Location System

Adam Smith, Hari Balakrishnan, Michel Goraczko, and Nissanka Priyantha

Mobisys, 2004

Ku Dara


Contents l.jpg
Contents

  • Introduction

  • Tracking Algorithm

  • Hybrid Architecture

  • Evaluation

  • Conclusion

Tracking Moving Devices with the Cricket Location System


Introduction 1 4 l.jpg

Human navigation

Multi-player games

Robotic navigation

Introduction(1/4 )

  • Location-aware application

Direct users to their desired destinations on active map

Lacation sensors provide position information to a moving robot

Players can move in the real world game like Doom or Quake

Tracking Moving Devices with the Cricket Location System


Introduction 2 4 l.jpg
Introduction(2/4)

  • Location-awareness system

Indoor environment

Outdoor environment

Active Badge(infrared)

GPS

(Global Positioning System)

Active Bat(ultrasonic)

Hiball tracking system(infrared LED)

Whisper system(audio)

Cricket(RF,ultrasonic)

Tracking Moving Devices with the Cricket Location System


Introduction 3 4 l.jpg
Introduction(3/4)

  • Indoor location architecture

Infrastructure receivers

Infrastructure trasmitters

DB

receiver

trasmitter

Cricket system

Active Badge, Active Bat

Tracking Moving Devices with the Cricket Location System


Introduction 4 4 l.jpg
Introduction(4/4)

  • Comparison

    • Active mobile architecture

      • Cost high, scalability low, performance high

      • Require a network infrastructure to connect the deployed receiver to central database

      • raising privacy concern

    • Passive mobile architecture

      • Scalability high, cost low, performance low

      • Independent privacy concern

Tracking Moving Devices with the Cricket Location System


Tracking algorithm 1 5 l.jpg
Tracking algorithm(1/5)

  • Three Componets of tracking algorithm

    • Lesat-squares minimization(LSQ)

      • Use LSQ to reset the bad EKF state

    • Extended Kalman filter(EKF)

      • Predicte next device’s state from samples

        • Correct the prediction each time new distance sample is obtained

    • Outlier rejection

      • Bad distance sample eliminate

[t,p,d]

t:current time

p:known position of the beacon or receiver

d:distance btwn mobile device and known beacon or receiver

Φ:a good positionestimate

Tracking Moving Devices with the Cricket Location System


Tracking algorithm 2 5 l.jpg

di : B

(||φ- pi|| - di)

A-B error

Current location

estimateφ

Reciever location pi

||φ- pi|| : A

Tracking algorithm(2/5)

  • LSQ(Least Squares Minimization)

    • If mobile devices were static, a standard way to solve the problem of estimating is by minimizing the sum of the squares of the error terms corresponding to each distance sample

    • LSQ is complex

    • LSQ does not always produce a good estimate

      • Use to initializing and reseting the Kalman filter (bad state)

A

B

Tracking Moving Devices with the Cricket Location System


Tracking algorithm 3 5 l.jpg

INPUT

EKF

OUTPUT

[

]

samples

estimate Φ

T

x

,

y

,

z

,

v

,

v

,

v

x

y

z

Internal state

Tracking algorithm(3/5)

  • EKF(Extended Kalman Filter)

    • Using a state vector with six components

      • Three position components(x,y,z), three velocity components( )

    • Use the most recent distance sample and internal state to project ahead and produce an estimate of Φ of where the device might be in the next time-step

      • P model: velocity and higher-order derivatives are zero

      • PV model: acceleration and higher order derivatives are zero

      • Multi-modal filter: combining the output states of PV and P models

Tracking Moving Devices with the Cricket Location System


Tracking algorithm 4 5 l.jpg
Tracking algorithm(4/5)

  • Outlier Rejection

    • Wherein egregiously bad distance samples are eliminated

Outlier Rejection

bad samples

outlier elliminated samples

IF( ?)

True→eliminate

False→accept

r : residual(guess-actual measurement)

γ:empirically-selected parameter

Tracking Moving Devices with the Cricket Location System


Tracking algorithm 5 5 l.jpg
Tracking algorithm(5/5)

  • Tracking algorithm

Measurements

t:current time

p:known position of the beacon

or receiver

d:distance btwn mobile device

and known beacon or receiver

transmitter

EKF next position extimate

Current estimate distance correct

Tracking Moving Devices with the Cricket Location System


Hybrid architecture 1 3 l.jpg
Hybrid Architecture(1/3)

  • Problem

    • Bad EKFstate

      • Extremely different estimate

      • Passive mobile system has a higher probability of reaching a bad state

      • Rarely happen in active mobile

1

2

3

1

2

3

[1][2][3]

[1][2][3]

[1][2][3]

[1]

[1][2]

[1][2][3]

Recent value

Old value

active mobile:multiple samples, accurate

Passive mobile:one sample, inaccurate

Tracking Moving Devices with the Cricket Location System


Hybrid architecture 2 3 l.jpg
Hybrid Architecture(2/3)

  • Solution

    • Normal state: passive mode use for Scalability, user-privacy

    • Bad Kalman filter state: active mode use

Tracking Moving Devices with the Cricket Location System


Hybrid architecture 3 3 l.jpg
Hybrid Architecture(3/3)

  • Solution

Compute distance samples

RF message

Beacon use a simple CSMA scheme with randomized back off to avoid RF collisions

ActiveChirp

receiver

Reset EKF

Internal state

Tracking Moving Devices with the Cricket Location System


Evaluation 1 4 l.jpg
Evaluation(1/4)

  • Experimental setup

    • Cricket’s h/w and s/w

    • Computer-controlled Lego train with Cricket attached to the moving train

      • Cricket listener to the train, beacons to the ceiling

    • Six different speed:model a range of realistic pedestrian speeds

Tracking Moving Devices with the Cricket Location System


Evaluation 2 4 l.jpg

Ac

10cm

P-LSQ

50cm

Hv

20cm

P-MM

30cm

Evaluation(2/4)

  • Error CDF(speed:0.78m/s)

  • Passive

    • Multi-modal(MM)

      • 90%, error 30cm

    • LSQ

      • 90%,error 50cm (poor)

    • Precision MM>LSQ

  • Active

    • 90%, error 10 cm

    • High precision

Occurrence

error(cm)

Tracking Moving Devices with the Cricket Location System


Evaluation 3 4 l.jpg
Evaluation(3/4)

  • Error CDF(speed:1.43m/s)

  • Hybrid

    • 0.43 m/s: 90%, 20 cm

  • Speed increase, LSQ low precision

High precision

Active-MultiModal

Hybrid-EKF-PV

Passive-MultiModal

Passive-LSQ

Low precision

Act

10cm

P-LSQ

85cm

Hv

45cm

P-MM

65cm

Tracking Moving Devices with the Cricket Location System


Evaluation 4 4 l.jpg
Evaluation(4/4)

  • Median error

    • Hybrid close to active mode

Passive

25cm below

Hybrid

15cm below

Active

5cm below

Tracking Moving Devices with the Cricket Location System


Conclusion 1 1 l.jpg
Conclusion(1/1)

  • Hybrid architecture

    • Preserve the scalability and privacy advantages of the passive mobile

    • Improving tracking precision

Tracking Moving Devices with the Cricket Location System


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