UBICOMP '14 ADJUNCT, SEPTEMBER 13 - 17, 2014, SEATTLE, WA, USA

Cross-Assistive Approach for PDR
and Wi-Fi Positioning
Kazuya Miyazaki
Ritsumeikan University
1-1-1 Noji-Higashi
Kusatsu, Shiga 5258577
JAPAN
mii@ubi.cs.ritsumei.ac.jp

Nobuhiko Nishio
Ritsumeikan University
1-1-1 Noji-Higashi
Kusatsu, Shiga 5258577
JAPANnishio@cs.ritsumei.ac.jp

Masahiro Mochizuki
Ritsumeikan University
1-1-1 Noji-Higashi
Kusatsu, Shiga 5258577
JAPAN
moma@ubi.cs.ritsumei.ac.jp
Kazuya Murao
Ritsumeikan University
1-1-1 Noji-Higashi
Kusatsu, Shiga 5258577
JAPANmurao@cs.ritsumei.ac.jp

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http://dx.doi.org/10.1145/2638728.2641303

777

Abstract
In indoor positioning using Wi-Fi, there is a problem thatthe accuracy is not stable by the occurrence of large
errors. Large errors tend to occur when density of wireless
LAN access points is low or the radio wave condition isunstable. Furthermore, as for positioning utilisingsmartphone, it takes a while to scan Wi-Fi beacons.
Thereby, errors tend to occur while user is moving.
Because it is diﬃcult to observe exactly Wi-Fi beacons.
Accordingly, the authors proposed Cross-Assistive
Approach for PDR and Wi-Fi Positioning. First of all,fingerprinting that is often used Wi-Fi positioning is
improved by confining fingerprints to location where isestimated by PDR. As a result, this approach improved
the accuracy about 2 meters. Furthermore, in order tocorrect accumulated errors in PDR, the authors proposeda method that corrects PDR with accurate Wi-Fi
positioning results. Additionally, the authors proposed amethod that estimates the accuracy of Wi-Fi positioning
results. The mean error of accurate Wi-Fi results
estimated by the accuracy estimating method was 0.98
meters. Thus, the accuracy estimating method detected
accurate Wi-Fi positioning results eﬀectively. In thecomprehensive evaluation, our approach improved an
existing Wi-Fi method about 3.4 meters by assisted PDR
with Wi-Fi positioning and assisted Wi-Fi positioning with
PDR cooperatively. Moreover, this approach enabledUBICOMP '14 ADJUNCT, SEPTEMBER 13 - 17, 2014, SEATTLE, WA, USA

accumulated errors in PDR to be corrected.

Moreover, we conducted an experiment to evaluate ourapproach.

Author Keywords
Indoor Positioning, Pedestrian Dead Reckoning, Wi-Fi

Basic Indoor Positioning

ACM Classification Keywords

There are various kinds of indoor positioning methods.
This section introduces basic indoor positioning methods
that are often used.

K.6.4 [MANAGEMENT OF COMPUTING AND
INFORMATION SYSTEMS]: System ManagementQuality assurance

Introduction
Recently, location-aware services that provide information
suitable for the user’s situation, such as Yelp[1] andFoursquare[2], have become very popular. We usually get
the location information with GPS. Nevertheless, it isdiﬃcult to estimate indoor location utilising GPS because
GPS satellite waves cannot reach. Therefore, indoor
positioning has been studied so that we can get the indoor
location information with IMES and Wi-Fi and such
[3, 4, 5]. In particular, positioning with Wi-Fi has
attracted attention because it is low cost by already set
wireless LAN access points. On the other hand,
positioning with Wi-Fi has the problem that the accuracy
is not stable by the occurrence of large errors. large errors
tend to occur when density of wireless LAN access points
is low or the radio wave condition is unstable.
Furthermore, as for positioning utilising smartphone, ittakes a while to scan Wi-Fi beacons. Thereby, errors
tends to occur while user is moving. Because it is diﬃcult
to observe exactly Wi-Fi beacons.
Accordingly, we proposed the Cross-Assistive Approach for
Pedestrian Dead Reckoning (PDR) and Wi-Fi Positioning.
In our approach, each positioning method improves
accuracy cooperatively, such as PDR improves Wi-Fi
positioning and improved Wi-Fi positioning boosts PDR.

Positioning with Wi-Fi
In general, Wi-Fi positioning methods estimate thepresent location by beacons of the wireless LAN accesspoint. For example, there is a method using thetrilateration based on the relationship between
attenuation rate of radio waves and distance between thesmartphone and the wireless LAN access point, comparing
RSSIs1 learned in advance with RSSIs observed at present,
estimating the present location stochastically by RSSIs
observed at present, and so on.
1. Fingerptinting
Bahl proposed RADAR [6] that estimates thepresent location by comparing RSSIs learned inadvance with RSSIs observed at present. Thissystem needs to learn the measurement coordinates
and beacons of the wireless access point finely. In
addition, the system clusters Wi-Fi feature values
based on the measurement coordinate (fingerprint).
Next, the system calculates euclidean distances
between fingerprints and RSSIs observed at present.
Euclidean distance is calculated as follows:
v
um
u∑
2
′
En = t(1)(rssii − rssi i )i=1

1 Received Signal Strength Indication

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Where E is euclidean distance, n is the number offingerprints, m is the number of common wireless
LAN access points between fingerprint and Wi-Fi
information observed at present,
rssi is RSSI′
observed at present, rssi is RSSI in the fingerprint.
Present location is estimated by selecting thecoordinate of fingerprint that has the shortest
euclidean distance or calculating the coordinate byweighted average with fingerprints that have theshorter euclidean distance.
2. Particle Filter
Particle filter positioning method [7] usuallyestimates the present location stochastically by
maximum likelihood estimation method. Maximum
likelihood estimation method compares radio wave
environment models with RSSIs observed at present.
The radio wave environment models is modeled bytraining Wi-Fi information in advance. Firstly, thesystem moves particles that are an hypothesis of theuser present location. The system calculates thelikelihood between the models and RSSIs observed
at present in the directions of particles. Secondly,
the system estimates the present location with eachcoordinate of particles and likelihood. One of theestimating location methods uses weighted averagecoordinate of the particles with weight. The
likelihood is used as weight.

Problems
This section explains problems of basic positioning
methods.
Problem of positioning with Wi-Fi
Wi-Fi positioning methods have a problem that theaccuracy depends of the radio wave environment of thepositioning area. Figure 1 shows result of preliminary
experiment using the fingerprinting. In the experiment, we
walked counterclockwise from the southeast corner. The
mean error was 6.7 meters because of large errors occur inthe place that has open ceiling (center of Figure 1).

Figure 1: Trajectory of the Fingerprinting

Pedestrian Dead Reckoning
Pedestrian Dead Reckoning (PDR) [8] is a technologythat estimates a relative present location by the numberof steps and the direction. PDR counts the number ofsteps by accelerometer sensor and estimates the direction
by gyroscope sensor. Wi-Fi positioning can introduce
PDR at a low cost by built-in sensors on smartphones.

779

The large errors occurrence can be caused by :1. Density of wireless LAN access points is low
The positioning area of south has manylaboratories. Thus, the density of wireless accesspoints was high. By contrast, the density of wirelessUBICOMP '14 ADJUNCT, SEPTEMBER 13 - 17, 2014, SEATTLE, WA, USA

access points was low in connecting corridors ofwest and east. The accuracy was low in the low
density place because there was no features between
observed Wi-Fi information.
2. Radio wave condition is unstable
A lot of packet loss was occurred in that positioning
area. The packet loss occurrence was high, thepositioning accuracy was lower because observing
device cannot accurately observe Wi-Fi information.
It is inferred that a packet loss tends to occur inunstable radio wave environment, such as reflection
or interference is strong.

Cross-Assistive Approach for PDR and Wi-Fi
Positioning
In order to enable high accuracy Wi-Fi positioning insituations that have the accuracy limitation, we proposed
Cross-Assistive Approach for PDR and Wi-Fi Positioning(Figure 2). In our approach, each positioning method
1 PDR
improves accuracy cooperatively, such as ⃝2
improves Wi-Fi positioning and ⃝ improved Wi-Fi
positioning boosts PDR. Accordingly, we established above
two methods, and we created a cooperative mechanism.

PDR

3. Users walk around during observation
The Wi-Fi positioning with a smartphone takes
more than two seconds to scan Wi-Fi beacons. Evenif smartphone scan Wi-Fi beacons with two seconds
interval, the distance walked in that time is large.
Therefore, large errors occur because thesmartphone cannot scan Wi-Fi beacons.
Thus, positioning methods with Wi-Fi have limitations
depending on situations. Additionally, existing Wi-Fi
positioning methods cannot know accuracy, so
applications can only use results as it is. Consequently,
bad positioning results have a bad impact on applications.
Problem of PDR
PDR estimates a present location at a tidy trajectory. On
the other hand, PDR has a problem that accuracy decline
by accumulating sensing errors. Additionally, it is diﬃcult
for PDR to correct accumulated errors because PDR
estimates the present location relatively.

780②
Wi-Fi①
PDR

Wi-Fi

Figure 2: Cross-Assistive Approach for PDR and Wi-Fi
Positioning

Assisted Wi-Fi positioning with PDR
1 PDR improves Wi-Fi
Firstly, we considered the method ⃝positioning. We used fingerprinting that is commonly usedin the Wi-Fi positioning. The fingerprinting used theestimation method selecting coordinate of fingerprint thathas the shortest euclidean distance. Fingerprints aremeasured in each positioning area such as floors ofbuilding, underground mall and so on. Additionally, if
positioning area becomes bigger, the range of fingerprint
matching during positioning is widened. Thus, large errors
become easy to occur due to being selected fingerprint
apart from the present location when a calibration device

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cannot receive Wi-Fi beacons accurately. Therefore, we
proposed a method that sets a limit to matchingfingerprints during positioning. Firstly, PDR estimates thepresent location. Secondly, fingerprinting selects
fingerprints close to the present location estimated by
PDR. Thereby, the limitation of the fingerprints prevents
large errors occur even when Wi-Fi beacons cannot bereceived accurately.
Assisted PDR with Wi-Fi positioning
2 Wi-Fi positioning
Secondly, we considered the method ⃝boosts PDR. This method corrects the present locationand the direction of PDR by accurate Wi-Fi positioning
results. Therefore, it is necessary to determine if Wi-Fi
positioning result is accurate, and determine Wi-Fi
positioning accuracy with a property that PDR results
become a consecutive trajectory. We defined what an
accurate Wi-Fi positioning result is :distw ≤ distp
dirp − α ≤ dirw ≤ dirp + α

(2)
(3)

Where, distw is the distance of Wi-Fi positioning results,
distp is the distance of the PDR result, dirw is the
direction of Wi-Fi positioning results, dirp is the directionof the PDR result, α is any angle. Firstly, PDR estimates
the present location using the number of steps and thestep length that is set in advance. Secondly, theestimating accuracy method calculates distance between
latest Wi-Fi positioning result and latest accurate Wi-Fi
positioning result. If there is no accurate Wi-Fi
positioning result, the method calculates distance between
latest positioning result and one result before latest result.
In consequence, if distw is shorter than distp , it ispresumably that the Wi-Fi positioning result is accurate
because of fitting (Equation 2). On the other hand, dirw
is unstable, and it is not always true that dirw is similar781

to present user direction. Therefore, the method adopt
only a Wi-Fi positioning result that transited within αdegree from PDR direction (Equation 3). The method
estimates that Wi-Fi positioning result is accurate when
two conditions are met. Owing to it, the estimating
accuracy method can remove Wi-Fi positioning results inwhich transition distance is short and transition direction
diﬀerent from present user direction. Additionally, themethod corrects accumulating errors simply by assigning
the coordination of the Wi-Fi positioning result to thecoordination of a criterion in PDR and the direction of the
Wi-Fi positioning result to the direction of a criterion in
PDR.
Cross-Assistive Approach
Finally, above described approaches improve integrated
accuracy cooperatively by repeating the following process:1. Improve fingerprinting accuracy by PDR
2. Estimate whether a fingerprinting result is accurate
3. Correct accumulating errors of PDR by the accurate
fingerprinting result
Thus, our approach can remove large errors in Wi-Fi
positioning, and correct accumulating errors of PDR.

Evaluation
In this section, we evaluated proposed methods.
Results improving fingerprinting using PDR
Firstly, we measured Wi-Fi information and set
fingerprints showed as red pins in Figure 3. Additionally,
the fingerprinting uses estimation method selecting
coordinate of fingerprint that has the shortest euclidean
distance. In the experiment, we walked at a constantUBICOMP '14 ADJUNCT, SEPTEMBER 13 - 17, 2014, SEATTLE, WA, USA

speed counterclockwise from the southeast corner in thefloor. The floor size is about 40 m × 20 m. Moreover, we
calculated answer coordinates from walking time andwalking distance. We set default values of PDR such as
the coordinate and the direction in advance.

Wi-Fi with PDR: inner rectangular trajectory (purple)
PDR: outer rectangular trajectory (red)Figure 4: Trajectory of the Fingerprinting
Table 1: Mean Error of the Fingerprinting
Figure 3: Set Fingerprints

Wi-Fi

Wi-Fi with PDR

6.73

4.47

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Error$(m)

Figure 4 shows results of experiment using improved
fingerprinting. Actual positioning results have spread on
corridors, and there are overlaps in the trajectory.
Comparing Figure 1 and Figure 4, positioning results withproposed method similar to measurement route, andFigure 4 has no large errors. Additionally, Table 1 shows
mean errors of existing method and proposed method. As
a result, proposed method improved accuracy about 2
meters. Nevertheless, as shown in Figure 5, the accuracy
became lower over time in this approach. It is inferredthat the approach could not limit fingerprints properly
because the accuracy of PDR became lower by
accumulated errors.

Mean
error (m)Figure 5: Errors of the Fingerprinting

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Results considering method for estimate Wi-Fi positioning
accuracy
We verified the precision of accurate Wi-Fi positioning
results estimated by the Wi-Fi accuracy estimating
method with PDR. Table 2 shows a mean error of
accurate results that were estimated by the estimating
method. As a result, the precision of estimation was high,hence the proposed method estimate the accuracy of
Wi-Fi positioning eﬀectively.
Table 2: Mean Error of Accurate Results Estimated by
Proposed Method

Mean error (m)method for estimate the accuracy
0.982

Cross-assistive approach
Finally, we evaluated a integration positioning. We useddescribed above settings. Figure 6 shows trajectories of
PDR, Wi-Fi with PDR and cross-assistive approach. As aresult, cross-assistive approach could correct trajectory by
fixing incorrect PDR results such as passing a corner. On
the other hand, as shown in Table 3, cross-assistive
approach could improve the accuracy 3.4 meters morethan existing method.

Wi-Fi with PDR: Inner rectangular trajectory (purple)

783

Cross-Assistive Approach: Medium rectangular trajectory (green)
PDR: Outer rectangular trajectory (red)Figure 6: Trajectory of the Cross-Assistive Approach
Table 3: Average Error of each Positioning MethodMeanerror (m)Wi-Fi

Wi-Fi with PDR

Cross-Assistive
Approach

6.73

4.47

3.31

Related Work
This section introduces related works such as improving
fingerprinting, developing integration positioning using
dead reckoning and Wi-Fi.
Improved fingerprinting with BluetoothBaniukevic improves fingerprinting with Bluetooth [9].
This approach divides a positioning area optimally by
setting Bluetooth base stations that send Bluetoothbeacons. Firstly, they set Bluetooth base stations basedon positioning error measured in advance and the numberof fingerprints , and divide optimally positioning area that
fingerprints are set. Additionally, this approach do notdivide fingerprints at the similarity of Wi-Fi feature value
into same positioning area because of positioning error iseasy to occur. They define hotspot as places that
Bluetooth base station is set. This approach improves
positioning accuracy by hotspot to divide fingerprints
area. In the evaluation, they set two Bluetooth basestations in a floor that has an area of 50 m2 . As a result,
this approach improved a positioning mean error from9.75 meters to 7.57 meters. Although this approach
improves a positioning accuracy, this approach has
problems such as setting of Bluetooth base stations takes
a cost, it is necessary to calculate positions that

UBICOMP '14 ADJUNCT, SEPTEMBER 13 - 17, 2014, SEATTLE, WA, USA

Bluetooth base stations are set.
Advanced Integration of Wi-Fi and Inertial Navigation Systems
Evennou proposes the integration positioning with Wi-Fi
and dead reckoning framework [10]. The proposed
method fuses dead reckoning framework that uses inertial
navigation sensors, such as accelerometer, gyroscope andpressure sensor, into particle filter positioning. Firstly, thisapproach prevents particle filter positioning from
calculating results including irremovable coordinate by
map information. Actually, likelihoods of the particle thatmoved in the wall is set 0. Additionally, this approach
improves a positioning accuracy by spreading particles
following PDR around actual present location. Thisapproach has problems because it is necessary to prepare
map in advance , the calculation volume is large due tousing a lot of particles, and so no.
Although there are approaches that improve existing
Wi-Fi positioning methods, they cannot estimate a Wi-Fi
positioning accuracy in real time.

Conclusion
In order to enable high accuracy Wi-Fi positioning in thesituation that has accuracy limitations, we proposed
Cross-Assistive Approach for PDR and Wi-Fi Positioning.
First of all, we improved fingerprinting by confining
fingerprints to a location where is estimated by PDR. As aresult, we improved the accuracy about 2 meters.
Furthermore, in order to correct accumulated errors in
PDR, we proposed a method that corrects PDR withaccurate Wi-Fi positioning results. The mean error of
accurate Wi-Fi results estimated by the accuracy
estimating method was 0.98 meters. Thus, the accuracy
estimating method detected accurate Wi-Fi positioning

784

results eﬀectively. In the comprehensive evaluation, ourapproach improved an existing Wi-Fi method about 3.4
meters by assisted PDR with Wi-Fi positioning andassisted Wi-Fi positioning with PDR cooperatively.
Moreover our approach enabled accumulated errors in
PDR to be corrected. Additionally, we improve estimating
Wi-Fi accuracy method by considering situation of stayand not using PDR as a future work. Moreover, weconsider algorithms to correct complex trajectories such as
meandering.

Acknowledgements
Part of this research is supported by SCOPE(#132307011) (Strategic Information andCommunications R&D Promotion Program) from the
Ministry of Internal Aﬀairs and Communications(MIC),
Japan.

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