FILA:

Fine-grained Indoor Localization

Kaishun Wu, Jiang Xiao, Youwen Yi, Min Gao, Lionel M. Ni

IEEE 2012 INFOCOM

March 29th, 2012

Hong Kong University of Science and Technology

Outline

Introduction

Motivation

System Design

Performance Evaluation

Conclusions

2

3

Assert Tracking

Emergence Response Healthcare

Security

Social Network

Goals:

To fast locate objects

To obtain high accuracy

To minimize deployment costs

Indoor Location-based Services

3

Techniques System Accuracy Cost

GPS SnapTrack >5m Medium

GSM GSM Fingerprinting >2.5m Medium

Ultrasonic Criktet 4*4 Sq Ft (100%) High

Infrared Active Badge 5-10m Medium

UWB Ubisense 15cm High

RFID LANDMARC 2m Low

Sensors RIPS 3cm High

WLAN RADAR, Horus 3-5m; 2m Low

RF-based

Indoor Localization Techniques

Radio Frequency (RF) is the frequency that the radio signals

are carried and transmitted from the antenna.

Existing RF-based Indoor Localization Techniques

4

Highest!

Lowest!

WLAN Cost

Accuracy

WLAN Advantages

Dense-deployed APs

Prevalent WiFi-enabled devices

Low cost and easy for implementation

5

WLAN RSSI-based

Indoor Localization Techniques

Received Signal Strength Indicator (RSSI)

a measurement of the power present in a received radio signal

Radio propagation model: distance=f(RSSI)

Free space path loss model

6

Distance

RSS

I

Free Space Path loss

RSSI AP

WiFi-enabled device

Related Work

RADAR [INFOCOM’00] , Horus [Mobisys’05]

Fingerprint: signal strength radio map

Accuracy: 3m for 50%, 0.7m for 50%

Wideband Powerline Positioning [UbiComp’08]

Apply wideband frequency to mitigate the time variance.

Accuracy: reduce accuracy degradation over time

Indoor localization without pain [MobiCom’10]

Radio propagation model based

Accuracy: 2m for 50%

7

All based on

RSSI

Is

RSSI

a reliable indicator?

8

Observation

RSSI value is a packet-level estimator

Average the signal power over a packet.

RSSI is easily varied by multipath.

9

Constructive Destructive

Multipath Path loss

RSSI is not reliable !

Could we find

a reliable metric to improve indoor localization?

10

Key Insight

Orthogonal Frequency Division Multiplexing

IEEE 802. 11 a/g/n leverage OFDM to provide high throughput

In OFDM, a channel is orthogonally divided into multiple sub

channels, namely subcarriers

Data is transmitted in parallel on multiple subcarriers that

overlap in frequency

11

1st Subcarrier 2nd Subcarrier 3rd Subcarrier

IFFT Data in

Modulation D/A

Baseband O

FDM signal Transmitter

FFT Data out

Modulation A/D

Baseband O

FDM signal Receiver

Key Insight

Channel State Information

In OFDM system, the received signal over multiple subcarriers is

Y = H X + N (X– transmit signal, N– noise)

H=Y/X -- Channel State Information (CSI)

H=hejw(h: amplitude, w: phrase)

CSI is the channel response at the receiver in frequency domain

12

Data out

Channel

Data in Encoder

Transmitter Receiver

Decoder X Y

+ x

N H

Key Insight

.

13

RSSI

RSSI estimates the channel

in packet level.

Only a single amplitude

CSI

CSI estimates the channel in

subcarrier level. [1]

Vector with amplitude and

phase

RSSI

CSIs

Packet 2.4GHz

antenna

Receiver S/P FFT

Baseband

[1]D Halperin and et al., “Predicable 802.11 Packet Delivery from Wireless Channel Measurements”, in SIGCOMM, 2010.

So compared to RSSI,

CSI Is

Fine Grained metric full of

frequency domain information!

We expect to exploit such

information to obtain a reliable

indicator for location.

14

Scope

15

Motivation • RSSI is inaccurate and not reliable

• CSI is fine grain information

Approach • Replace RSSI with CSI

• Design a FILA system

Goal • Improve the indoor

localization performance

Outline

Introduction

Motivation

System Design

Performance Evaluation

Conclusions

16

Cross Layer Architecture

17

Tx AP Location

Information

(2) Process CSI CSIeff (2)’ Distance

Calculator

OFDM

Demodulator

OFDM

Decoder Rx

Normal

Data

+ (3) Locate Rx

Network

layer

Pysical layer

Cross layer

(1) Collect CSI

Channel

Estimation

AP1

d2 AP2

AP3

d1

Design Approach

CSI Collection

Process CSI and Distance Estimation

Location Determination

18

Approach (1st Step)

The first step is to collect the subcarriers CSI which divided into 30

groups on the received baseband in WLAN.

19

CSI Collection Process CSI Location

Hardware Wireless card

Operating SystemDevice Driver

Location Determination System

Wireless API

Approach (2nd Step)

Two processing mechanisms:

#1 Time-domain Multipath Mitigation

#2 Frequency-domain Fading Compensation

Distance Estimation

20

CSI Collection Process CSI Location

Approach (2nd Step)

Time-domain Multipath Mitigation

The received signal is the combination of

multiple reflections with LOS signal

If bandwidth is wider than coherence

bandwidth, the reflections will be

resolvable.

The bandwidth of 802.11n is 20MHz, that

provides the capability of the receiver to

resolve the different reflections in the

channel.

h=IFFT(CSI) 21

CSI Collection Process CSI Location

0 20 40 600

5

10

15

20

25

Time delay

Ch

ann

el R

esp

on

se A

mp

litu

de

Approach (2nd Step)

Frequency-domain Fading Compensation

When the space between two subscarriers is larger than coherence

bandwidth, they are fading independently

22

CSI Collection Process CSI Location

Exploit the frequency diversity of CSI to

eliminate small-scale fading

We define effective CSI as the weighting

average among all subcarriers

-30 -20 -10 0 10 20 30-28

-27

-26

-25

-24

-23

-22

-21

-20

-19

-18

Subcarrier Index

Re

ce

ive

Po

we

r(d

Bm

)

CSI𝑒𝑓𝑓 =1

𝐾

𝑓𝑘

𝑓0

𝐾𝑘=1 × 𝐶𝑆𝐼𝑘 , k ∈ [−15,15]

Approach (2nd Step)

Distance Determination

Refined model: distance= f(CSIeff )

d =1

4𝜋

𝑐

𝑓0× 𝐶𝑆𝐼𝑒𝑓𝑓

2

× 𝛿

1

𝑛

δ: environment factor

𝓃: path loss fading exponent

KNN algorithm

23

CSI Collection Process CSI Location

Initialized δ and 𝓃

Choose a CSIeff dataset corresponding to a distance,

and then train δ and 𝓃

Use δ，𝓃 to verify the CSIeff dataset of other distances

Other distances are in conformity with the training δ

and 𝓃

Approach (3rd Step)

Obtain the coordinates of the APs.

Calculate the distance between object and the APs.

Apply the trilateration method to locate object.

𝑑1 = 𝑥1 − 𝑥02 + 𝑦1 − 𝑦0

2

𝑑2 = 𝑥2 − 𝑥02 + 𝑦2 − 𝑦0

2

𝑑3 = 𝑥3 − 𝑥02 + 𝑦3 − 𝑦0

2

24

CSI Collection Process CSI Location

AP1

AP2 AP3

So, we can determine the location of the !

Outline

Introduction

Motivation

System Design

Performance Evaluation

Conclusions

25

Experimental Setup

Router iwl5300

26

Hardware

Intel WiFi Link 5300, 802.11n router

Software

Linux 2.6.38 kernel, Matlab, Python

Implementation (4 Scenarios)

Chamber

5m Χ 8m

Lab Lecture Hall

20m Χ 25m 3m Χ 4m

27

Corridor

Evaluation Metric

Temporal stability

Accuracy

28

Relation between CSI and Distance

29

2.5 3 3.5 4 4.5 5 5.5 610

15

20

25

30

35

40

45

50

Distance (meters)

CS

I eff a

mp

litu

de

CSIeff amplitude

Exponential Fitting

29

Temporal Stability

30

30

Accuracy of Distance Estimation

31

31

Location Accuracy in Lab

32

For over 90% of data points, the localization error < 1m

For over 50% of data points, the localization error < 0.5m

32

Location Accuracy in Lecture Hall

For over 90% of data points, the localization error < 1.8m

For over 50% of data points, the localization error < 1.2m

33

Location Accuracy in Corridor

34

For over 90% of data points, the localization error < 2m

For over 50% of data points, the localization error < 1.2m

34

Outline

Introduction

Motivation

System Design

Performance Evaluation

Conclusions

35

Conclusions

36 We use fine gained PHY information (CSI) in OFDM-based

WLANs to improve indoor localization performance.

We design FILA, a fine grained cross layer localization system

leveraging CSI based on existing WLAN standards.

Experiments with commercial NICs in different scenarios

show that FILA can achieve significantly accuracy gain

comparing with corresponding RSSI methods.

36

Thanks.

Questions?

37

jxiao@cse.ust.hk

PhD Candidate @ Hong Kong University of Sci.& Tech.