Device signal strength self-calibration using histograms - - PDF document

▶
device signal strength self calibration using histograms
SMART_READER_LITE
LIVE PREVIEW

Device signal strength self-calibration using histograms - - PDF document

Feb 09, 2023 207 likes •540 views

See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/256484371 Device signal strength self-calibration using histograms (Presentation slides) Data September 2013 CITATIONS READS 0 69

slide-1
SLIDE 1

See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/256484371

Device signal strength self-calibration using histograms (Presentation slides)

Data · September 2013

CITATIONS READS

69

1 author: Some of the authors of this publication are also working on these related projects: Motive View project Christos Laoudias University of Cyprus

89 PUBLICATIONS 1,405 CITATIONS

SEE PROFILE

All content following this page was uploaded by Christos Laoudias on 20 May 2014.

The user has requested enhancement of the downloaded file.

slide-2
SLIDE 2

Introduction

  • Motivation

Device Calibration

  • Device Diversity
  • Manual Calibration

Self-Calibration

  • RSS Histograms
  • Self-Calibration

Method

Performance Evaluation

  • Measurement Setup
  • Experimental

Results

Conclusions

  • Concluding Remarks

Device Signal Strength Self-Calibration using Histograms

Christos Laoudias∗, Robert Pich´ e† and Christos Panayiotou∗

∗KIOS Research Center for Intelligent Systems and Networks, University of Cyprus †Tampere University of Technology, Tampere, Finland

International Conference on Indoor Positioning and Indoor Navigation, Sydney, Australia 15 November 2012

slide-3
SLIDE 3

Introduction

  • Motivation

Device Calibration

  • Device Diversity
  • Manual Calibration

Self-Calibration

  • RSS Histograms
  • Self-Calibration

Method

Performance Evaluation

  • Measurement Setup
  • Experimental

Results

Conclusions

  • Concluding Remarks

Outline

Introduction Device Calibration Self-Calibration Performance Evaluation Conclusions

International Conference on Indoor Positioning and Indoor Navigation, Sydney, Australia 15 November 2012

slide-4
SLIDE 4

Introduction

  • Motivation

Device Calibration

  • Device Diversity
  • Manual Calibration

Self-Calibration

  • RSS Histograms
  • Self-Calibration

Method

Performance Evaluation

  • Measurement Setup
  • Experimental

Results

Conclusions

  • Concluding Remarks

Motivation of our work

◮ RSS is intended for determining the signal quality and not for

positioning purposes

International Conference on Indoor Positioning and Indoor Navigation, Sydney, Australia 15 November 2012

slide-5
SLIDE 5

Introduction

  • Motivation

Device Calibration

  • Device Diversity
  • Manual Calibration

Self-Calibration

  • RSS Histograms
  • Self-Calibration

Method

Performance Evaluation

  • Measurement Setup
  • Experimental

Results

Conclusions

  • Concluding Remarks

Motivation of our work

◮ RSS is intended for determining the signal quality and not for

positioning purposes

◮ Different devices do not report RSS values in the same way

◮ The WiFi standard (IEEE 802.11) defines the RSS

Indicator (1 byte integer) for measuring RSS in [0 255]

◮ Each vendor’s implementation is limited up to RSSImax ◮ RSSI is mapped to power values in dBm internally by

the device driver (proprietary information)

◮ Even worse: same chipsets may not report the same

RSS values due to different antennas or packaging

International Conference on Indoor Positioning and Indoor Navigation, Sydney, Australia 15 November 2012

slide-6
SLIDE 6

Introduction

  • Motivation

Device Calibration

  • Device Diversity
  • Manual Calibration

Self-Calibration

  • RSS Histograms
  • Self-Calibration

Method

Performance Evaluation

  • Measurement Setup
  • Experimental

Results

Conclusions

  • Concluding Remarks

Motivation of our work

◮ RSS is intended for determining the signal quality and not for

positioning purposes

◮ Different devices do not report RSS values in the same way

◮ The WiFi standard (IEEE 802.11) defines the RSS

Indicator (1 byte integer) for measuring RSS in [0 255]

◮ Each vendor’s implementation is limited up to RSSImax ◮ RSSI is mapped to power values in dBm internally by

the device driver (proprietary information)

◮ Even worse: same chipsets may not report the same

RSS values due to different antennas or packaging

◮ Using a new device for positioning is feasible, but the RSS

values are not compatible with the radiomap

International Conference on Indoor Positioning and Indoor Navigation, Sydney, Australia 15 November 2012

slide-7
SLIDE 7

Introduction

  • Motivation

Device Calibration

  • Device Diversity
  • Manual Calibration

Self-Calibration

  • RSS Histograms
  • Self-Calibration

Method

Performance Evaluation

  • Measurement Setup
  • Experimental

Results

Conclusions

  • Concluding Remarks

Motivation of our work

◮ RSS is intended for determining the signal quality and not for

positioning purposes

◮ Different devices do not report RSS values in the same way

◮ The WiFi standard (IEEE 802.11) defines the RSS

Indicator (1 byte integer) for measuring RSS in [0 255]

◮ Each vendor’s implementation is limited up to RSSImax ◮ RSSI is mapped to power values in dBm internally by

the device driver (proprietary information)

◮ Even worse: same chipsets may not report the same

RSS values due to different antennas or packaging

◮ Using a new device for positioning is feasible, but the RSS

values are not compatible with the radiomap

◮ Best accuracy is guaranteed only if the user carries the same

device during positioning, otherwise calibration is required

International Conference on Indoor Positioning and Indoor Navigation, Sydney, Australia 15 November 2012

slide-8
SLIDE 8

Introduction

  • Motivation

Device Calibration

  • Device Diversity
  • Manual Calibration

Self-Calibration

  • RSS Histograms
  • Self-Calibration

Method

Performance Evaluation

  • Measurement Setup
  • Experimental

Results

Conclusions

  • Concluding Remarks

Motivation of our work

◮ RSS is intended for determining the signal quality and not for

positioning purposes

◮ Different devices do not report RSS values in the same way

◮ The WiFi standard (IEEE 802.11) defines the RSS

Indicator (1 byte integer) for measuring RSS in [0 255]

◮ Each vendor’s implementation is limited up to RSSImax ◮ RSSI is mapped to power values in dBm internally by

the device driver (proprietary information)

◮ Even worse: same chipsets may not report the same

RSS values due to different antennas or packaging

◮ Using a new device for positioning is feasible, but the RSS

values are not compatible with the radiomap

◮ Best accuracy is guaranteed only if the user carries the same

device during positioning, otherwise calibration is required

◮ Existing calibration methods do not fit well in real-time

positioning scenarios

International Conference on Indoor Positioning and Indoor Navigation, Sydney, Australia 15 November 2012

slide-9
SLIDE 9

Introduction

  • Motivation

Device Calibration

  • Device Diversity
  • Manual Calibration

Self-Calibration

  • RSS Histograms
  • Self-Calibration

Method

Performance Evaluation

  • Measurement Setup
  • Experimental

Results

Conclusions

  • Concluding Remarks

Device Diversity

Source: K. Kaemarungsi (2006)

International Conference on Indoor Positioning and Indoor Navigation, Sydney, Australia 15 November 2012

slide-10
SLIDE 10

Introduction

  • Motivation

Device Calibration

  • Device Diversity
  • Manual Calibration

Self-Calibration

  • RSS Histograms
  • Self-Calibration

Method

Performance Evaluation

  • Measurement Setup
  • Experimental

Results

Conclusions

  • Concluding Remarks

Good News: Linearity between RSS values

−100 −90 −80 −70 −60 −50 −40 −30 −20 −100 −90 −80 −70 −60 −50 −40 −30 −20 Mean RSS from HTC Flyer [dBm] Mean RSS from HP iPAQ [dBm] RSS pairs Cloud center Least−squares Fit −100 −90 −80 −70 −60 −50 −40 −30 −20 −100 −90 −80 −70 −60 −50 −40 −30 −20 Mean RSS from Samsung Nexus S [dBm] Mean RSS from Asus eeePC [dBm] RSS pairs Cloud center Least−squares Fit

◮ Manual Calibration: Collect several colocated RSS pairs at

known locations and estimate the linear coefficients through least squares ¯ r (2)

ij

= α12¯ r (1)

ij

+ β12

International Conference on Indoor Positioning and Indoor Navigation, Sydney, Australia 15 November 2012

slide-11
SLIDE 11

Introduction

  • Motivation

Device Calibration

  • Device Diversity
  • Manual Calibration

Self-Calibration

  • RSS Histograms
  • Self-Calibration

Method

Performance Evaluation

  • Measurement Setup
  • Experimental

Results

Conclusions

  • Concluding Remarks

Good News: Linearity between RSS values

−100 −90 −80 −70 −60 −50 −40 −30 −20 −100 −90 −80 −70 −60 −50 −40 −30 −20 Mean RSS from HTC Flyer [dBm] Mean RSS from HP iPAQ [dBm] RSS pairs Cloud center Least−squares Fit −100 −90 −80 −70 −60 −50 −40 −30 −20 −100 −90 −80 −70 −60 −50 −40 −30 −20 Mean RSS from Samsung Nexus S [dBm] Mean RSS from Asus eeePC [dBm] RSS pairs Cloud center Least−squares Fit

◮ Manual Calibration: Collect several colocated RSS pairs at

known locations and estimate the linear coefficients through least squares ¯ r (2)

ij

= α12¯ r (1)

ij

+ β12

◮ Limited Applicability: (i) User needs to be familiar with the

indoor area and (ii) a considerable data collection effort is required

International Conference on Indoor Positioning and Indoor Navigation, Sydney, Australia 15 November 2012

slide-12
SLIDE 12

Introduction

  • Motivation

Device Calibration

  • Device Diversity
  • Manual Calibration

Self-Calibration

  • RSS Histograms
  • Self-Calibration

Method

Performance Evaluation

  • Measurement Setup
  • Experimental

Results

Conclusions

  • Concluding Remarks

Can we do it more efficiently?

Objectives

◮ Fully automatic approach with short calibration time ◮ Runs concurrently with positioning while the user walks

around

◮ No user intervention or tedious data collection

Idea

◮ Perform device self-calibration on-the-fly using histograms of

RSS values observed simultaneously with positioning

International Conference on Indoor Positioning and Indoor Navigation, Sydney, Australia 15 November 2012

slide-13
SLIDE 13

Introduction

  • Motivation

Device Calibration

  • Device Diversity
  • Manual Calibration

Self-Calibration

  • RSS Histograms
  • Self-Calibration

Method

Performance Evaluation

  • Measurement Setup
  • Experimental

Results

Conclusions

  • Concluding Remarks

RSS Histograms

−100 −90 −80 −70 −60 −50 −40 −30 −20 −10 0.005 0.01 0.015 0.02 0.025 0.03 0.035 0.04 0.045 0.05 Mean RSS Value Probability −100 −90 −80 −70 −60 −50 −40 −30 −20 −10 0.005 0.01 0.015 0.02 0.025 0.03 0.035 0.04 0.045 0.05 Mean RSS Value Probability

Figure: HP iPAQ (left) and Asus eeePC (right)

−100 −90 −80 −70 −60 −50 −40 −30 −20 −10 0.005 0.01 0.015 0.02 0.025 0.03 0.035 0.04 0.045 0.05 Mean RSS Value Probability −100 −90 −80 −70 −60 −50 −40 −30 −20 −10 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Mean RSS Value Probability HP iPAQ Asus eeePC HTC Flyer

Figure: HTC Flyer (left) and Empirical cdfs (right)

International Conference on Indoor Positioning and Indoor Navigation, Sydney, Australia 15 November 2012

slide-14
SLIDE 14

Introduction

  • Motivation

Device Calibration

  • Device Diversity
  • Manual Calibration

Self-Calibration

  • RSS Histograms
  • Self-Calibration

Method

Performance Evaluation

  • Measurement Setup
  • Experimental

Results

Conclusions

  • Concluding Remarks

Self-Calibration Method

Reference Device ecdf Radiomap User Device ecdf Device Calibration Positioning Algorithm Transformation

i

r ˆ( ) k l ( ) s k

( , ) α β

( ) s k %

International Conference on Indoor Positioning and Indoor Navigation, Sydney, Australia 15 November 2012

slide-15
SLIDE 15

Introduction

  • Motivation

Device Calibration

  • Device Diversity
  • Manual Calibration

Self-Calibration

  • RSS Histograms
  • Self-Calibration

Method

Performance Evaluation

  • Measurement Setup
  • Experimental

Results

Conclusions

  • Concluding Remarks

Self-Calibration Method

Reference Device ecdf Radiomap User Device ecdf Device Calibration Positioning Algorithm Transformation

i

r ˆ( ) k l ( ) s k

( , ) α β

( ) s k %

  • 1. Create the ecdf of the reference device from the radiomap

International Conference on Indoor Positioning and Indoor Navigation, Sydney, Australia 15 November 2012

slide-16
SLIDE 16

Introduction

  • Motivation

Device Calibration

  • Device Diversity
  • Manual Calibration

Self-Calibration

  • RSS Histograms
  • Self-Calibration

Method

Performance Evaluation

  • Measurement Setup
  • Experimental

Results

Conclusions

  • Concluding Remarks

Self-Calibration Method

Reference Device ecdf Radiomap User Device ecdf Device Calibration Positioning Algorithm Transformation

i

r ˆ( ) k l ( ) s k

( , ) α β

( ) s k %

  • 1. Create the ecdf of the reference device from the radiomap
  • 2. Create and update the ecdf of the new device by using s(k)

International Conference on Indoor Positioning and Indoor Navigation, Sydney, Australia 15 November 2012

slide-17
SLIDE 17

Introduction

  • Motivation

Device Calibration

  • Device Diversity
  • Manual Calibration

Self-Calibration

  • RSS Histograms
  • Self-Calibration

Method

Performance Evaluation

  • Measurement Setup
  • Experimental

Results

Conclusions

  • Concluding Remarks

Self-Calibration Method

Reference Device ecdf Radiomap User Device ecdf Device Calibration Positioning Algorithm Transformation

i

r ˆ( ) k l ( ) s k

( , ) α β

( ) s k %

  • 1. Create the ecdf of the reference device from the radiomap
  • 2. Create and update the ecdf of the new device by using s(k)
  • 3. Fit a linear mapping between the reference and new device to
  • btain (α, β) by using “representative” ecdf values

International Conference on Indoor Positioning and Indoor Navigation, Sydney, Australia 15 November 2012

slide-18
SLIDE 18

Introduction

  • Motivation

Device Calibration

  • Device Diversity
  • Manual Calibration

Self-Calibration

  • RSS Histograms
  • Self-Calibration

Method

Performance Evaluation

  • Measurement Setup
  • Experimental

Results

Conclusions

  • Concluding Remarks

Self-Calibration Method

Reference Device ecdf Radiomap User Device ecdf Device Calibration Positioning Algorithm Transformation

i

r ˆ( ) k l ( ) s k

( , ) α β

( ) s k %

  • 1. Create the ecdf of the reference device from the radiomap
  • 2. Create and update the ecdf of the new device by using s(k)
  • 3. Fit a linear mapping between the reference and new device to
  • btain (α, β) by using “representative” ecdf values
  • 4. Transform the observed RSS values with ˜

sj(k) = αsj(k) + β

International Conference on Indoor Positioning and Indoor Navigation, Sydney, Australia 15 November 2012

slide-19
SLIDE 19

Introduction

  • Motivation

Device Calibration

  • Device Diversity
  • Manual Calibration

Self-Calibration

  • RSS Histograms
  • Self-Calibration

Method

Performance Evaluation

  • Measurement Setup
  • Experimental

Results

Conclusions

  • Concluding Remarks

Self-Calibration Method

Reference Device ecdf Radiomap User Device ecdf Device Calibration Positioning Algorithm Transformation

i

r ˆ( ) k l ( ) s k

( , ) α β

( ) s k %

  • 1. Create the ecdf of the reference device from the radiomap
  • 2. Create and update the ecdf of the new device by using s(k)
  • 3. Fit a linear mapping between the reference and new device to
  • btain (α, β) by using “representative” ecdf values
  • 4. Transform the observed RSS values with ˜

sj(k) = αsj(k) + β

  • 5. Estimate location ˆ

ℓ(k) with any fingerprint-based algorithm

International Conference on Indoor Positioning and Indoor Navigation, Sydney, Australia 15 November 2012

slide-20
SLIDE 20

Introduction

  • Motivation

Device Calibration

  • Device Diversity
  • Manual Calibration

Self-Calibration

  • RSS Histograms
  • Self-Calibration

Method

Performance Evaluation

  • Measurement Setup
  • Experimental

Results

Conclusions

  • Concluding Remarks

Inverse ecdf Linear fitting

  • 100
  • 90
  • 80
  • 70
  • 60
  • 50
  • 40
  • 30
  • 20
  • 10

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Mean RSS Value Probability HP iPAQ HTC Flyer

◮ F(x) gives the probability that the RSS value is less than x,

F −1(y) returns the RSS value that corresponds to the y-th cdf percentile

◮ Fr(x) and Fu(x) are the ecdfs of the reference and user device ◮ F −1

r

(y) = αF −1

u (y) + β, y ∈ {0.1, 0.2, . . . , 0.9}

◮ (α, β) are initialized to (1, 0) and updated periodically (e.g.

every 10 sec) thereafter, while the user is walking

International Conference on Indoor Positioning and Indoor Navigation, Sydney, Australia 15 November 2012

slide-21
SLIDE 21

Introduction

  • Motivation

Device Calibration

  • Device Diversity
  • Manual Calibration

Self-Calibration

  • RSS Histograms
  • Self-Calibration

Method

Performance Evaluation

  • Measurement Setup
  • Experimental

Results

Conclusions

  • Concluding Remarks

Inverse ecdf Linear fitting

  • 100
  • 90
  • 80
  • 70
  • 60
  • 50
  • 40
  • 30
  • 20
  • 10

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Mean RSS Value Probability HP iPAQ HTC Flyer

◮ F(x) gives the probability that the RSS value is less than x,

F −1(y) returns the RSS value that corresponds to the y-th cdf percentile

◮ Fr(x) and Fu(x) are the ecdfs of the reference and user device ◮ F −1

r

(y) = αF −1

u (y) + β, y ∈ {0.1, 0.2, . . . , 0.9}

◮ (α, β) are initialized to (1, 0) and updated periodically (e.g.

every 10 sec) thereafter, while the user is walking

International Conference on Indoor Positioning and Indoor Navigation, Sydney, Australia 15 November 2012

slide-22
SLIDE 22

Introduction

  • Motivation

Device Calibration

  • Device Diversity
  • Manual Calibration

Self-Calibration

  • RSS Histograms
  • Self-Calibration

Method

Performance Evaluation

  • Measurement Setup
  • Experimental

Results

Conclusions

  • Concluding Remarks

Inverse ecdf Linear fitting

  • 100
  • 90
  • 80
  • 70
  • 60
  • 50
  • 40
  • 30
  • 20
  • 10

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Mean RSS Value Probability HP iPAQ HTC Flyer

  • 66 dBm
  • 87 dBm

◮ F(x) gives the probability that the RSS value is less than x,

F −1(y) returns the RSS value that corresponds to the y-th cdf percentile

◮ Fr(x) and Fu(x) are the ecdfs of the reference and user device ◮ F −1

r

(y) = αF −1

u (y) + β, y ∈ {0.1, 0.2, . . . , 0.9}

◮ (α, β) are initialized to (1, 0) and updated periodically (e.g.

every 10 sec) thereafter, while the user is walking

International Conference on Indoor Positioning and Indoor Navigation, Sydney, Australia 15 November 2012

slide-23
SLIDE 23

Introduction

  • Motivation

Device Calibration

  • Device Diversity
  • Manual Calibration

Self-Calibration

  • RSS Histograms
  • Self-Calibration

Method

Performance Evaluation

  • Measurement Setup
  • Experimental

Results

Conclusions

  • Concluding Remarks

Experimental Setup

START END

◮ 560 m2 office, 9 WiFi APs, 5 devices (1 HP iPAQ PDA, 1

Asus eeePC laptop, 1 HTC Flyer Android tablet, 2 Android smartphones)

◮ Training Data: 105 reference locations, 20 fingerprints per

location (2100 in total) with each device for comparison

◮ Testing Data: Route with 2 segments, 96 test locations, 1

fingerprint per location, route sampled 10 times

International Conference on Indoor Positioning and Indoor Navigation, Sydney, Australia 15 November 2012

slide-24
SLIDE 24

Introduction

  • Motivation

Device Calibration

  • Device Diversity
  • Manual Calibration

Self-Calibration

  • RSS Histograms
  • Self-Calibration

Method

Performance Evaluation

  • Measurement Setup
  • Experimental

Results

Conclusions

  • Concluding Remarks

Experimental Results – 10 Routes

1 2 3 4 5 6 7 8 9 No Calibration Self−Calibration Manual Calibration Device−Specific Mean Positioning Error Per Route [m]

  • 100
  • 90
  • 80
  • 70
  • 60
  • 50
  • 40
  • 30
  • 20
  • 10

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Mean RSS Value Probability HP iPAQ HTC Flyer

Figure: HTC Flyer user with HP iPAQ radiomap

International Conference on Indoor Positioning and Indoor Navigation, Sydney, Australia 15 November 2012

slide-25
SLIDE 25

Introduction

  • Motivation

Device Calibration

  • Device Diversity
  • Manual Calibration

Self-Calibration

  • RSS Histograms
  • Self-Calibration

Method

Performance Evaluation

  • Measurement Setup
  • Experimental

Results

Conclusions

  • Concluding Remarks

Experimental Results – 10 Routes

0.5 1 1.5 2 2.5 3 3.5 4 No Calibration Self−Calibration Manual Calibration Device−Specific Mean Positioning Error Per Route [m]

  • 100
  • 90
  • 80
  • 70
  • 60
  • 50
  • 40
  • 30
  • 20

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Mean RSS Value Probability Asus eeePC HTC Flyer

Figure: HTC Flyer user with Asus eeePC radiomap

International Conference on Indoor Positioning and Indoor Navigation, Sydney, Australia 15 November 2012

slide-26
SLIDE 26

Introduction

  • Motivation

Device Calibration

  • Device Diversity
  • Manual Calibration

Self-Calibration

  • RSS Histograms
  • Self-Calibration

Method

Performance Evaluation

  • Measurement Setup
  • Experimental

Results

Conclusions

  • Concluding Remarks

Experimental Results – Single Route

10 20 30 40 50 60 70 80 90 100 2 4 6 8 10 12 14 16 18 Samples Positioning Error [m] No Calibration Self−Calibration Device−Specific

◮ iPAQ radiomap with Flyer user-carried device ◮ For the first 10 sec the device is uncalibrated and accuracy is

not adequate

◮ Beyond that point, the device is automatically calibrated and

accuracy is greatly improved

International Conference on Indoor Positioning and Indoor Navigation, Sydney, Australia 15 November 2012

slide-27
SLIDE 27

Introduction

  • Motivation

Device Calibration

  • Device Diversity
  • Manual Calibration

Self-Calibration

  • RSS Histograms
  • Self-Calibration

Method

Performance Evaluation

  • Measurement Setup
  • Experimental

Results

Conclusions

  • Concluding Remarks

Results with pairwise device combinations

Table: Median of the mean error ¯ ǫ [m], with and without calibration. iPAQ eeePC Flyer Desire Nexus S iPAQ 2.7 2.8 (6.6) 3.0 (7.5) 2.9 (8.4) 2.6 (7.7) eeePC 2.8 (4.4) 2.3 2.3 (2.8) 2.6 (3.5) 2.5 (2.9) Flyer 3.2 (5.9) 2.6 (3.0) 1.9 2.1 (2.3) 2.6 (2.7) Desire 3.4 (6.1) 2.8 (3.2) 2.5 (2.5) 2.4 2.5 (2.6) Nexus S 3.0 (6.2) 2.6 (2.8) 2.7 (2.7) 2.4 (2.5) 2.3

◮ All 5 devices used as a reference (row) and test device (column) ◮ Mean positioning error using device self-calibration (results

without calibration shown in parentheses)

◮ The diagonal cells report the accuracy when the reference and

test devices are the same (i.e. device-specific radiomap is used)

◮ Self-calibration improves the accuracy for all device pairs

International Conference on Indoor Positioning and Indoor Navigation, Sydney, Australia 15 November 2012

slide-28
SLIDE 28

Introduction

  • Motivation

Device Calibration

  • Device Diversity
  • Manual Calibration

Self-Calibration

  • RSS Histograms
  • Self-Calibration

Method

Performance Evaluation

  • Measurement Setup
  • Experimental

Results

Conclusions

  • Concluding

Remarks

Concluding Remarks

Device diversity is one of the reasons that hinders the proliferation of RSS-based positioning systems.

Our Contributions

◮ Low-complexity, yet effective method that allows any mobile

device to be self-calibrated

◮ Automatic calibration is attained shortly after the user has

started positioning Future Work

◮ Application in larger scale setups featuring non uniform WiFi AP

layouts (possible skewness of the RSS histograms)

◮ Integrate with our Airplace indoor positioning platform

developed for Android smartphones http://www2.ucy.ac.cy/~laoudias/pages/platform.html

International Conference on Indoor Positioning and Indoor Navigation, Sydney, Australia 15 November 2012

slide-29
SLIDE 29

Introduction

  • Motivation

Device Calibration

  • Device Diversity
  • Manual Calibration

Self-Calibration

  • RSS Histograms
  • Self-Calibration

Method

Performance Evaluation

  • Measurement Setup
  • Experimental

Results

Conclusions

  • Concluding

Remarks

Thank you for your attention

Contact Christos Laoudias KIOS Research Center for Intelligent Systems and Networks Department of Electrical & Computer Engineering University of Cyprus Email: laoudias@ucy.ac.cy

International Conference on Indoor Positioning and Indoor Navigation, Sydney, Australia 15 November 2012

slide-30
SLIDE 30

Introduction

  • Motivation

Device Calibration

  • Device Diversity
  • Manual Calibration

Self-Calibration

  • RSS Histograms
  • Self-Calibration

Method

Performance Evaluation

  • Measurement Setup
  • Experimental

Results

Conclusions

  • Concluding

Remarks

Extra Slides

International Conference on Indoor Positioning and Indoor Navigation, Sydney, Australia 15 November 2012

slide-31
SLIDE 31

Introduction

  • Motivation

Device Calibration

  • Device Diversity
  • Manual Calibration

Self-Calibration

  • RSS Histograms
  • Self-Calibration

Method

Performance Evaluation

  • Measurement Setup
  • Experimental

Results

Conclusions

  • Concluding

Remarks

RSS Difference Approach

Assume that a mobile device resides at a location ℓ, which is covered by 2 WiFi APs, namely AP1 and AP2. The RSS values recorded by the device are given by RSS1 = A − 10γ log10 d1 + X1 RSS2 = A − 10γ log10 d2 + X2 where di, i = 1, 2 is the distance from the i-th AP, while X1, X2 ∼ N(0, σ2

n) are independent Gaussian noise components

disturbing the RSS values. Taking the difference of these RSS values, denoted as RSSD12, gives RSSD12 = RSS1 − RSS2 = 10γ log10 d2 d1 + X ′ where X ′ ∼ N(0, 2σ2

n) is the linear combination of X1, X2.

International Conference on Indoor Positioning and Indoor Navigation, Sydney, Australia 15 November 2012

slide-32
SLIDE 32

Introduction

  • Motivation

Device Calibration

  • Device Diversity
  • Manual Calibration

Self-Calibration

  • RSS Histograms
  • Self-Calibration

Method

Performance Evaluation

  • Measurement Setup
  • Experimental

Results

Conclusions

  • Concluding

Remarks

Inverse ecdf Least Squares Fitting

If u is a continuous random variable and y = f (u) with monotonically increasing f then f = F −1

y

  • Fu. In particular, the

inverse cdf ordered pairs {(ui, yi) = (F −1

u (qi), F −1 y

(qi)) : qi ∈ {0.1, . . . , 0.9}} lie on the curve y = f (u). Proof: We have Fu(u) = P(u ≤ u) = P(f (u) ≤ f (u)) = = P(y ≤ f (u)) = Fy(f (u)). Applying F −1

y

to both sides gives the identity f = F −1

y

  • Fu. Also,

the components of the inverse cdf ordered pairs satisfy yi = F −1

y

(qi) = F −1

y

(Fu(ui)) = f (ui).

International Conference on Indoor Positioning and Indoor Navigation, Sydney, Australia 15 November 2012

View publication stats View publication stats