Indoor Pedestrian Localization for Mobile Devices

The Model

Jonáš Ševčík

Faculty of Informatics, Masaryk University, Botanická 68a, Brno, Czech Republic

Keywords: Indoor Localization, Android, 802.11 Fingerprinting, Mobile Phone Tracking.

Abstract: Indoor localization using mobile devices is one of the emerging areas of today’s interest. This paper

presents a model for indoor localization based on 802.11 Wi-Fi fingerprinting in combination of inertial

navigation running in parallel. We introduce a novel model scheme, where we use a state-of-the-art

Compass system. The system is enhanced by clustering and the position calculation is influenced by the

distance travelled between each fingerprinting, allowing us to eliminate improbable location estimations.

Proposed system is supposed to be resilient to received signal strength blocking caused by a human body,

and also to be more accurate than other up-to-date solutions.

1 INTRODUCTION

Navigation has become inherent part of human lives.

The ability to successfully navigate oneself in an

unknown environment has lead into constructing

various means of navigation. These techniques and

technologies were gradually enhanced resulting into

more precise and reliable navigating systems. In

1994 United States Department of Defense finished

fully operational Global Positioning System (GPS)

providing the Standard Positioning Service for

everyone. With the decrease of prices of consumer

electronics in which was GPS frequently integrated,

it became widely spread technology used in

navigation systems. Location has proven to be an

important source of contextual information. If a

device can determine its own location then it can

infer its surroundings and adapt accordingly

(Woodman, 2010). One of the features of navigation

systems is to track position of pedestrians. But

despite the usage of GPS in open spaces, it is almost

impossible to use it indoors. Tracking GPS signals

indoors typically requires a receiver capable of

tracking signals with power levels ranging from

160dBW to 200dBW, however a typical receiver has

a noise floor of around 131dBW (Dedes, 2005).

Furthermore, to be fully functional, GPS relies on

direct visibility of at least 4 satellites (Kumar, 2008),

which is hardly attainable in closed spaces.

Multipath effects (signal multiplication caused by

reflections) are likely to cause degradation in

accuracy even if a receiver is able to track signals

from a sufficient number of satellites. Unlike in

outdoor environments, reflected signals are often

stronger than those received via direct line-of-sight

when indoors. Hence it is difficult for a receiver to

identify and track the correct (i.e. direct) signals

(Woodman, 2010).

It came into realization that for indoor use, GPS

has to be substituted by a different system. Emerged

solutions were based on a range of technologies

including lasers (Gutmann, 1996), ultrasound,

wireless networks, even a magnetic field

(Woodman, 2010; Storms, 2009). Unfortunately,

those solutions are depending on preinstalled

infrastructure, thus making them unusable in

ordinary environments.

2 RELATED WORK

Radiolocation based on 802.11 localization has been

an active area of research for the past two decades.

Originally, the research was focused on robot

navigation. Over the years, area of interest became

focused on pedestrians. Wi-Fi APs are now densely

deployed in many urban environments. A device can

estimate its position by searching a database

containing the locations of such APs for ones that

are visible from its current location. The accuracy of

488

Šev

cík J..

Indoor Pedestrian Localization for Mobile Devices - The Model.

DOI: 10.5220/0005104604880493

In Proceedings of the 9th International Conference on Software Engineering and Applications (ICSOFT-EA-2014), pages 488-493

ISBN: 978-989-758-036-9

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

such approach is highly dependent on the density of

APs. However, positioning errors are at worst equal

to the range of an AP (typically 50 – 100 m)

(Woodman, 2010). Recent development has brought

focus on cheap Bluetooth LE sensors, which can

substitute more expensive Wi-Fi APs. Possible

disadvantages of radiolocation are that a database of

Wi-Fi APs/Bluetooth sensors must be constructed

beforehand and it must be frequently updated, since

there may be changes in the infrastructure.

2.1 Techniques Using AP Location

This approach is based on constructing a radio map,

in which Wi-Fi signals are recorded at locations of

interest along with a vector of received signal

strength (RSS). This technique is known as

fingerprinting. Location estimation than can be

reproduced by matching measured RSS readings

against previously established database. One of the

first techniques using deterministic fingerprinting

(computes a single best guess of a position of a

device) was RADAR (Gutmann, 1996; Bahl, 2000).

This technique shows error less than 9 m 95% of the

time. RADAR was enhanced over the years. One of

the successors is Horus (Youssef, 2005), which uses

a stochastic description of the RSS map with the

combination of maximum likelihood – probabilistic

based approach. The Horus system reports accuracy

of 1.4 m 95% of the time. Also, Horus was enhanced

in the technique Compass by using digital

compasses and measuring RSS in 8 directions in one

location. There is also other technique, such as

(Varshavsky, 2007), which presents possibilities of

using GSM signal, or (Azizyan, 2009), presenting

map building, based on notable environment features

such as sound, light, colors etc.

All these presented approaches are depending on

detailed environment measurements. Some sources

(Haeberlen, 2004) state that 28 man-hours were

required to construct a radio map covering a 12,000

building. Moreover, this process must be

repeated whenever an AP is moved/added/removed,

to ensure reliable results. To avoid such time

consuming operations, there have been made several

efforts. For example DAIR (Bahl, 2005) does not

involve mapping at all, but it assumes, that used

environment is densely equipped with APs. We have

to realize, that all presented accuracy results were

made in areas housing multiple APs (typically not

family houses or apartments). In order to achieve

usable results it is required to have installed at least

3 APs (Woodman, 2010).

2.2 Inertial Positioning and Dead

Reckoning

Inertial positioning uses MEMS, particularly

magnetometers, accelerometers and gyroscopes, to

calculate change in mobile device’s location in time.

These systems (Cho, 2006) are based on dead

reckoning. We can use course heading from

magnetometer and readings from accelerometer can

serve as a step detector. With known course and

traveled distance, it is possible to calculate position

of the measuring device. When tracking a

pedestrian, we can use information from sensors

along with an estimate of a stride length to track his

approximate position relative to his starting point.

Over time, the calculated position will become less

accurate. This is caused by adding fixed stride

lengths (which differ from the actual ones), so that

the overall error accumulates. The longer path the

pedestrian walks the bigger error it accumulates.

This accumulation of error is commonly referred to

as drift (Ojeda, 2007). Drift is acceptable if the

target is tracked only for a short period of time. It is

apparent that if we want to build a system, which is

capable of tracking for a long period of time, to

avoid drift, position must be regularly calibrated by

absolute positioning system.

Pedestrian dead reckoning can be divided into 2

groups. One group is based on step detection, the

other on inertial navigation. Step based pedestrian

dead reckoning is based on detecting steps and

estimating stride length. As mentioned above, when

the step is detected, heading and step length is used

to update position of the tracked pedestrian. Main

advantage of this approach is that the tracking

device can be mounted almost anywhere on the

pedestrian’s body. Usual locations are foot, waist or

head. Since, 1 axis accelerometers are used for this

method; step is detected by peaks in the received

signals (Steinhoff, 2010). This also have a

disadvantage, because having only 1 axis

accelerometer means that this system works with the

assumption, that pedestrian is moving only forwards.

Step length estimation is also problematical, since

step length varies from step to step. Several

algorithms have been developed for estimating step

length. As presented in (Alvarez, 2006) step length

can be estimated using user-specific constants or

neural networks. Problem is that neural networks

must be trained for each individual user, which can

be time consuming. The last drawback is that such

systems cannot differentiate vertical displacement,

thus it is impossible to recognize for example

movement up on the stairs.

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The second group of pedestrian dead reckoning

is based on inertial navigation. It is a navigation

technique in which measurements provided by

accelerometers and gyroscopes are used to track the

position and orientation of an object relative to a

known starting point, orientation and velocity. Thus

it must use full 3 dimensional inertial navigation unit

(Foxlin, 2005). Actions must be taken to prevent

accumulation of drift – namely by placing measuring

device exclusively on the foot of the pedestrian and

then correcting the inertial navigation system when

the foot is grounded. At the time, sensors report

near-zero velocity. As shown in (Godha, 2006)

Bayesian filters prove to offer an ideal framework to

track such correlations that exist between

accumulating errors in different components and to

apply zero velocity updates in a way that allows

correlated errors in all components of the state of the

inertial navigation system to be corrected.

Particularly, Kalman filters are widely used

(Woodman, 2010). The main advantage of this

group of pedestrian dead reckoning is that the

system does not assume only forward movement.

Hence, it can correctly handle moving backwards or

strafing.

2.3 Hybrid Models

Hybrid models are based on combining various

localization techniques to achieve better accuracy

results. It is important to note that combining more

localization techniques also results into higher inner

complexity, thus overall position error may be

influenced by more factors. For the purposes of our

research we are going to focus only for those based

on 802.11 and inertial localization.

The work (Evennou, 2006) introduces solution

based on Wi-Fi, inertial navigation system, and

particle filtering. This system is using 6-degrees-of-

freedom zero velocity updating. Main supporting

feature of this system is particle filtering. Possible

disadvantage is relatedness to known precise

building layout (needed for particle filtering) and

placement of the measuring unit to the foot of the

user.

Another most advanced hybrid system (Frank,

2009) presents Wi-Fi localization system supported

by inertial navigation. Emphasis of this system is

given to sparse Wi-Fi fingerprint sampling. Authors

used fingerprinting points with a span of 5 m. As

opposed to the previously mentioned system

(Evennou, 2006), inertial navigation implements

acquiring a heading data from the additional

magnetometer. The inertial system is processed by

its own Bayesian filter to estimate individual steps

of the user; these estimates are then combined with

the estimate of the location from fingerprinting. This

approach allows precise data processing at sensors’

local sampling rates, which reduces overall

complexity without suffering from significant loss of

final estimation accuracy (Frank, 2009). This system

shows arithmetic mean error of 3.1777 m for pure

Wi-Fi fingerprinting and 1.6468 m for the fusion of

fingerprinting and shoe data.

The goal of presented hybrid systems is to use as

few calibration locations as possible and to rely on

the short-term accuracy of foot mounted inertial

dead-reckoning (for instance zero velocity update

based techniques) among these points. The role of

the Wi-Fi positioning here is to provide long term

accuracy in the area of interest (Frank, 2009).

3 PROPOSED MODEL

We have identified, that current known solutions are

either not mobile-based (Frank, 2009; Ojeda, 2007

etc.), they are dependent on construction of support

infrastructure (Lukianto, 2011) or the operating

hardware is directly mounted to the foot or leg

(Serra, 2010). Even in the cases when a mobile

device is used, the solutions are entirely based on

Wi-Fi or Bluetooth localization (Martin, 2010), or a

combination of visual tracking and step detection

based systems (Kothari, 2012; Lukianto, 2011;

Serra, 2010 etc.). Author of (Woodman, 2010)

claims that step detection based inertial navigation is

suitable for usage with mobile devices, but we

believe otherwise. From studies (Serra, 2010; Shala,

2011) it is visible, that approach based on step

detection is not optimal. In combination with Wi-Fi

tracking, it is possible to achieve accuracy

improvement (comparing to pure Wi-Fi localization

system), but results in (Ševčík, 2013) show gradual

increase of drift over the travelled distance, which

had grown in the worst case by 7 m. This actually

made fused position estimate worse than using just

plain Wi-Fi positioning. The study (Serra, 2010)

reveals comparable results and claims that using a

particle filter might improve drift accumulation,

which was proved wrong in (Ševčík, 2013), where

particle filter was already used. The same results

were confirmed in (Shala, 2011). After taking into

consideration all above mentioned systems and

techniques, our objective is to create universally

applicable hybrid system for mobile indoor

localization. Proposed novel solution, as presented

by Figure 1, will be composed of 2 localization

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techniques combined together for achieving better

accuracy. We have decided to adopt parallel

approach in order to process data locally and to

reduce overall complexity. This separation also

allows estimation filters to run at their local

sampling rates, helping to process data without

significant loss of final destination accuracy.

Location estimation will be based on probabilistic

Wi-Fi localization, using techniques proposed in

Horus system (Youssef, 2005), but for dealing with

problem of blocking effects caused by a human

body, we will extend this system by introducing

Compass system – orientation based Wi-Fi

fingerprinting as proposed in (King, 2006).

Sampling of the signal strength for selected

orientations at each reference point during the

fingerprint gathering phase and combining a subset

of these values to histograms in the location

estimation phase, allows orientation specific signal

strength distribution to be computed and utilized to

increase the accuracy of position estimates. The

Compass system indicates average error distance of

less than 1.6 m. Therefore, it will be used for initial

position estimation and further position calibrations.

Furthermore, Compass is optimal for addressing the

influence of human body as an obstacle, which is

blocking Wi-Fi signal.

Figure 1: Localization system abstraction.

The second localization technique will feature

inertial navigation. Inertial navigation will be used

for improving accuracy of Wi-Fi localization. We

propose a system based on gyroscope,

accelerometer, and magnetometer fusion. The fusion

will help to reduce sensor flaws. Data from

accelerometer and gyroscope will be filtered,

normalized and processed by step detection

algorithm, to be consequently used for zero velocity

updating (calibration technique reducing drift

accumulation). Subsequently, normalized linear

acceleration vector and course will be processed by

extended Kalman filter (EKF). We have chosen EKF

to estimate the instantaneous state of a linear

dynamic system, with measurements linearly related

to the state but influenced by white noise. The

estimator uses incomplete and noise-corrupted

measurements to estimate the tracking object’s state,

and the resulting estimation is statistically optimal

with respect to any quadratic function of estimation

error. Thus parallel EKF will be used to estimate

position errors, velocity errors, and hand movement

errors in terms of a Gaussian probability density

function. Integration of inertial navigation system

and Wi-Fi localization system will be performed via

the main particle filter, which will keep track of

position and course (heading). With a particle filter,

more information than RSS location and inertial

navigation data can be fused. In particular, a

building map is another very useful information

source, since a lot of location related data can be

extracted from the building structure information.

For the tracking problem, this information helps to

reduce the uncertainty of the walking trajectory.

Using a particle filter, the estimation can be

improved by deleting impossible particles, i.e. the

particles which would have crossed a wall.

The Compass as it is introduced in (King, 2006)

does not integrate any clustering algorithm, therefore

when calculating user’s position, all database data

need to be evaluated. This is not needed for small

data sets, but for large data sets, it is necessary to

select only a subset of fingerprint data, for the

system to be capable of running in real time. We

propose usage of Incremental Triangulation

algorithm as it is presented in (Youssef, 2006). If

during the location determination phase APs are

used incrementally, one after the other, then starting

with the ﬁrst AP (with the highest value of RSS), the

algorithm restrict our search space to the locations

covered by this AP. The second AP chooses only the

locations in the range of the ﬁrst AP and covered by

the second AP and so on, leading to a multi-level

clustering process. Location clustering can be further

enhanced using mobile sensors. We suggest

additional AP filtering using heuristics. From the

data obtained from inertial localization we can

improve the AP selection. Knowing user’s course

and approximate travelled distance from the last

known location, we can constrain the final location

estimation only to those points, which are in the

evaluated travel distance.

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4 CONCLUSIONS AND FUTURE

WORK

This paper presented hybrid pedestrian localization

system for mobile devices. The key concept is

parallel combination of inertial navigation and Wi-Fi

localization, where both parts are mutually

beneficial. Inertial navigation can be calibrated by

data obtained by Wi-Fi localization. On the other

hand, Wi-Fi localization accuracy can be enhanced

by restraining a selection of improbable results,

when taking sensor data into account – mainly the

digital compass and the approximated travelled

distance.

In our previous research, we have focused on

developing inertial navigation based on step

counting. We have implemented techniques for step

detection, which proved to be nearly 100% positive

in detecting steps while walking continuously. Now,

we are implementing the Compass system enhanced

by clustering. Current prototype is capable of

localizing a pedestrian with an error close to 4

meters. We believe that combination with inertial

navigation will reduce this error and provide better

results. Scaling will also affect the overall speed of

the system.

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