Indoor Localization and TrackingFazail Aslam, Jules Fakhoury, and Tho Le-Ngoc

McGill University, Department of Electrical and Computer Engineering,

Broadband Communications Research Lab

IntroductionThe industry has been recently very interested in

applying wireless real-time localization in mobile

indoor environments for commercial applications

such as: personnel and equipment tracking.

In this research, we investigate the environmental

effects on the performance of indoor localization

and tracking systems and then find methods

suitable to improve their reliability and accuracy.

We also aim to deploy, improve, and use an ultra-

wideband (UWB) based location system to carry

out mainly workflow related studies in a hospital

environment. Experiments were performed on the 3

operational UWB indoor localization systems, one

installed in a hospital and the other two in our

research labs.

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Zone AZone B

Zone C

Zone D

48%

27%

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Zone A

Zone B

Zone C

Zone D

Zone A Zone B Zone C Zone D

Zone A 0 6 0 0

Zone B 6 0 4 0

Zone C 0 4 0 0

Zone D 0 0 0 0

Time spent by the doctor in each zone during the

monitored days

Average time spent by the

doctor

Number of transits between

zones for the doctor

Tags in the Emergency Department of the Royal

Victoria Hospital

Zone A

Zone B

Zone C

Zone D

Conclusions•Our UWB-based location system performed with a reported

accuracy in 3D of 15 cm.

•The fingerprinting method yielded results with accuracy in

the range of 1-3 m depending on the case

•The trilateration is not particularly accurate, only being

good for a rough estimation

UWB

Localization•UWB systems are characterized by a very large

bandwidth and short-time pulses.

•A large bandwidth improve the reliability as the

signal contain different frequency components,

which increases the probability that at least some

of them can go through obstacles.

•Spreading the signal energy over a large

bandwidth decreases the power spectral density,

thus reducing interference to other systems in the

hospital.

Combined AoA, TDoA technology:

•The system performs measurement of

both pseudorange (based on TDoA) and

AoA

•More measurements per sensor make the

system more robust

Green lines

represent the AoA

seen by each sensor,

while blue curves

represents the

possible tag

positions that would

have generated the

TDoA

RTLS

Architecture

Tags

•Transmits UWB radio pulses

•Flexible update rate

•Comes into 2 forms: slim and

compact

Filter-based location algorithm:

•Sensors include information filters

algorithms that use models of object

dynamics to enhance location accuracy of

dynamic and static objects in a variety of

environments, eliminating reflections and

ambiguous data

•Algorithm works in an iterative manner

by combining the previous estimate of the

position with the current measurements

Sensors

•Detects UWB pulses from tags

•Contain an array of antennas which

can measure the Angle-of-Arrival

(AoA) and Time-Difference-of-Arrival

(TDoA) of tag signals.

UWBRTLS

Medical

staff

Device

EquipmentTag

UbisenseSensor

GUIs

API

Database

(I,x,y,z)

(I,x,y,z)

RTLSData Proc.

Statistical data

• Movement pattern

• Site visit statistics

• Total walking distance

Movement heat map

online

offline

Map

Server

Visual statistical results

Workflow Analysis and Organizational Setup

Improvement

RTLS DataCapture

2.4GHz6.0GHz

Internet+ VPN

Emergency Area

PersonalTag

Results

ApplicationsManagement of the Medical Staff

Healthcare Asset Tracking and

Management

Patient Monitoring

•The UWB

system

estimates the

position of

many doctors

during their

shift in real

time.

Measurements with 2 sensors and a static

tag

Wi-Fi Localization

•This method can be implemented using the existing Wi-Fi setup

or usb Wi-Fi detectors

•First Wi-Fi access points/ detectors to be used are selected

•A region is then selected and calibrated using these access point

•Once a region has been mapped, a tag can be detected using the

calibration maps and the access points

The location coverage shown above measure he signal

strength provided by various AP’s

The survey calibration accuracy is shown above

•This method can be implemented using usb Wi-Fi detectors

•Position of at least 3 detectors known

•Using the received signal strength and the path loss model, we

can calculate the distance between the fixed detectors and the

mobile node

•Using simple geometry, we can then calculate the exact

position.

Fig (a) is the ideal case and (b) is with range

estimation error

Fingerprinting Method:

Trilateration Method:

•The information about the position of the doctors can be

used to extract useful information such as the time spent

in each zone and the number of transits between zones.

•Using fingerprinting a location is mapped first and then the

nearest neighbor method is used to estimate the location.

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Position of Reference Points (blue) and Access Points (red)

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Distance Estimate from AP1

Distance Estimate from AP2

Distance Estimate from AP3

Distance Estimate from AP4

True position

Estimated Position 1

Estimated Position 2

Estimated Position 3

Estimated Position 4

Estimated Position 5

•Trilateration proves to be

the simplest, yet the most

inaccurate of the three

approaches to localization.

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x (meters)

y (

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Distance Estimate from AP1

Distance Estimate from AP2

Distance Estimate from AP3

True position

Estimated Posiion

A calibrated map of the MC-

846 lab

Original and estimated position

of tags using fingerprinting.

Original and

estimated position of

tags using

trilateration

The location system can be used for offline

studies of clinical workflow patterns, helping

improve efficiency.

Tracking of specialized medical equipment

would reduce the time wasted by clinicians

looking for them throughout the facility.

A precise location system in the ED would

enable staff to track patients in a timely

manner, ensuring accurate and consistent

delivery of care.