Evaluation of the Positioning System Utilizing
Smartphone’s Air Pressure Sensor andAccelerometer in Suzuka Circuit
Keiichiro Iwanami

Ismail Arai

Seiichi Tanaka

Advanced Course of Mechanical andDepartment of Electrical
Department of Mechanical Engineering,Electronic System Engineering,and Computer Engineering,
Akashi National College of Technology
Akashi National College of Technology Akashi National College of Technology

Abstract—GPS positioning is commonly used by smartphoneusers. But, it is difficult to continuously use all day due to thehigh power consumption of a GPS receiver. So, we investigate
another positioning system which infers a user’s position bycomparing altitude differences calculated from an air pressure
sensor with the digital elevation model. And, we assume that in
an undulating field, the proposed system will work well. Also,an angle of gradient and a speed are useful for positioning. In
this paper, we evaluate our method in Eco Mileage Challenge inSuzuka circuit where is famous for its undulating course.

I.

I NTRODUCTION

GPS positioning is commonly used by smartphone users.
Smartphones have built-in various sensors such as an acceleromater, a pressure sensor, and so on. And, they have builtin various receivers such as a GPS receiver and a wireress
LAN interface. They are used for various demand, especiallyfor positioning. A positioning system allows users to have apedestrians navigation, life log service, and so on.
Recently, the most popular positioning system is GPS.
Besides, GPS can’t avert increasing power due to continuously
stochastic inference. Power consumption of GPS receiver isthe largest in the Smartphone’s sensors. So, GPS is notsuitable for long time positioning. On the other hand, there
are some solutions for this problem. For example, a wireless
LAN positionnig system[4] and a dead reckoning system[5]are actively investigated. However, a positioning accuracy ofwireless LAN positioning systems are depends on the numberof access points around a user. And, dead reckoning systems
still have an accumulative error. Thus, another low power
positioning system has to be realized to meet purposes of all
location aware services.
To solve this problem, we have already investigated a low
power trajectory inferring system by comparing smartphone’spressure sensor log with a digital elevation model[1]. Against
this background, we have a chance to drive in Suzuka circuit.
So we evaluate the possibility to apply our system to work as
a positioning system in the circuit.
In this paper, we describe that our positioning method
which uses a smartphone’s pressure sensor and a digital
elevation map in II, abd an overview of the Eco Mileage
Challenge in III. In IV, we describe our logging application.
The result of ruuning in Suzuka circuit is described in V.
Finally, we conclude this paper in VI.

Time	：Pressure	  Sensor	  log	：GPS	  log	

Use	  for	  loca4on	  system	

Fig. 1.

II.

Overview of a data logging

A POSITIONING METHOD UTILIZING A SMARTPHONE ’ S
PRESSURE SENSOR AND A DIGITAL ELEVATION MAP

Fig. 1 shows an overview of the data logging part of a proposed positioning system which is comparing a smartphone’spressure sensor data and a digital elevation. On the path wherea user has walked, the user’s smartphone always logs pressure
sensor data, and logs GPS data at established intervals. Ateach interval, a GPS device is turned off. The proposed system
infers positions between the GPS data. The former position iscalled a start-point and the latter position is called an end-point.
After deciding both points, the system set the time of startpoint at zero and set the altitude of time n at Hn . The altitude
when the time is zero is calculated by comparing the GPSposition with the digital elevation map. To infer Hn+1 which
is an altitude of the next point of Hn , the system calculates a
relative altitude ∆z according to Eq. 1. Then, Hn+1 is obtained
from sum of Hn and ∆z as shown in Eq. 2. Note that these
are referred from the conventional research [3].

Tf∆z =γ((1−Pn+1Pn)) Rγg

Hn+1 = Hn + ∆z(1)(2)

The detail of each value of Eq. 1 is described as follows.
Tf [K] is a temperature while logging. A temperature lapse
rate is γ[K/m]. Gravitational acceleration is a g[m/s2 ], A gas
constant of air is R[J/kgK]. A pressure value is Pn+1 [hPa]when the time is n+1, A pressure value when the time is n isPn [hPa]. The altitude transition is calculated by incrementing
each relative altitude. In addition, an altitude transitions ofcandidate roads are different from each other. So, the proposedsystem can be inferring the trajectory well by using a pressure

Fig. 2.

Our original car for Eco Mileage Challenge

sensor when the road is undulating. After all, the proposed
method can be reducing power consumption of GPS because
it doesn’t need continuous GPS logging.
T HE CHARACTERISTIC OF E CO M ILEAGE
C HALLENGE

Fig. 3.

Eco Mileage Challenge is a motor race which is sponsoredby Honda. Racers compete not the running time but the gas
mileage. In the original cars class which we apply allows entry
teams to use their original car (Fig. 2 shows our original car)under the regulation of using a 50cc engine made by Honda.
We are given to gasoline of the quota and run the 2.2km lengthcourse at eight laps. Fig. 3 shows Suzuka circuit east course.
There are some important rules. First, if a car stopped andcouldn’t recover, the run is end. Second, a total runnig timemust be lower than 43 minutes. Third, an average speed mustbe faster than 25 km/h. After a running, the staff calculates agas mileage(km/l) of each car by using Eq. 3.

Mileage =Total distanceBurn-off fuel weight/Fuel density

50	  	  40	  	  30	  	  20	  	  Al+tude	  changes	  in	  Suzuka	  circuit	  east	  course	  10	  	  0	  	  Fig. 4.

500	  	  1000	  	  1500	  	  Distance[m]	

2000	  	  2500	  	  The altitude changes in Suzuka circuit east course(3)

In this race, a tilt angle of the course is important factor
because of the characteristic of the race, so it is also importantthat a driver opens the accelerator when he drives in an uphillslope and closes the accelerator when he drives in a downhill
slope. If a driver did so, the gas mileage rises. Additionally,Suzuka circuit course has a 35m vertical interval. Fig. 4 shows
altitude changes in Suzuka circuit east course. So, it is easy
to actually logging pressure variations. For the above reasons,
it is ideal environment for our positioning system.
IV.

The Suzuka circuit east course

60	  	  Al+tude[m]	III.

T HE LOGGING APPLICATION FOR E CO M ILEAGE
C HALLENGE

We developed an application which logs the sensor data of
Eco Mileage Challenge and displays some supportive information (e.g. such as current altitude and total runnning time) fora driver. Here are the requirements.
A. Requirements
1. Logging System
In this research, logging data from the air pressure sensor
and the GPS while driving is indispensable. So, this application
has to have a logging function. In theory, it is necesary to turnon/off the GPS at each interval. But, in this experiment, welogged not only air pressure sensor data, but also GPS data at
all times because the capacity of the smartphone’s battery isenough for such a short time race.
2.Displaying various data
It is desirable that the driver can view various supportive information such as uphill/downhill information, average driving
speed, driving time and each rap time from this application.
3.No operation while driving
It is dangerous that a driver operates the smartphone when
he/she is driving. So, it is necessary for the application to
update and display data automatically.
B. Desigin
To meet the requirements as mentioned as above, we design
an application as follows.
1. The data logging partThe data logging part logs air pressure, GPS data, acceleration, geomagnetism, tilt angle of the smartphone and timestamp. It logs them at 0.2 second intervals. The most of data
are stored in csv. GPS-NMEA data are stored in txt.

70	  60	  Speed	  60	  Al0tude	  50	  40	  40	  30	  30	  20	  20	  Al0tude[m]	

Speed[km/h]	

50	  10	  10	  0	  0	  0	  200	  400	  Time[sec]	

Fig. 7.

Fig. 5.

55	  Angle	  of	  2lt	Average	  Speed	Start	  	  Bu7on	

A screenshot of Glorious Run50	  45	  Al3tude[m]	

Total	  	  running	  distance	

Speed and altitude changes

40	  35	  30	  25	  "Course	  Al3tude"	  20	  "Proposal	  method"	  15	  0	  Fig. 8.

Fig. 6.

Driver’s view

2. The information display partA driver have to know a current position is wheter uphill or
downhill. So, the application has to display all altitude changes
in Suzuka circuit and a current position. Thus, the application
display a line graph which shows all altitude change in Suzuka
circuit east course. And, an arrow is overlaid on the current
position in the graph. So, a driver can know the current altitudewith tne surrounding altitudes. Also, the application displays
total driving distance, current angle of tilt and average driving
speed while the car is running.
3. The automatic stopwatch part
The application has a function to record each lap time andtotal running time and a function to display them. The driver
starts the application at a just moment before the race starts.
Then, the application records and displays each lap time. Anda total running time is always displayed.

We implemented the application “Glorious Run” based on
the discussion in IV-A and IV-B. Fig. 5 shows the screenshot
of Glorious Run. At the rihgt half of the application, a total

4000	  6000	  8000	  10000	  12000	  Travel	  distance[m]	

14000	  16000	  18000	  Travel distance and altitude changes

running time is printed in green, lap times are printed in white.
The graph which on the left half of application describes
altitude changes in Suzuka circuit. Its vertical axis is foraltitude[m], and the horizontal axis is for the distance ofSuzuka circuit east course. At the lower left of the application,
there are the various information display units and the start
button. The various information display units of a total driving
distance, a current tilt and an average driving speed. The total
driving distance is calculated by integrating the GPS datachanges. The angle of tilt displays a current tilt. It is calculated
by finding the remainder between the standard tilt value andthe current tilt value. The standard tilt value is set when thedriver push the zero set button in application menu. In thisexperience, the driver pushed the zero set button befor racestart in the starting point. This operation sets the stating point’s
tilt value to standart tilt value. And, an average driving speed
is obtained by dividing the total driving distance by the total
time.
Fig. 6 shows a viewpoint of driver. There are some controllers such as an accelerator and an brake, and various
measuring instruments, such as a fuelometer and a rev counter,and a different stopwatch. There is the smartphone which isinstalled Glorious Run is a located on the left of the handle.
Glorious Run is a Java application which running on Galaxy
S4. Galaxy S4 has an air pressure sensor which has a resolutionof 0.01[hPa].
V.

C. Implementation of Glorious Run

2000	  T HE EVALUATION RESULT OF RUNNING THE S UZUKACIRCUIT

This section describes the result of logging data whilerunning the Suzuka circuit. Fig. 8 shows relativity between thecar speed and the car altitude. The car altitude is calcureted

55	  

In consideration of above problem, the other correction
method weights the altitude changes only for uphill. As aresult, the heigher limit and the lower limit of the altitudechanges closed to the truth more. Incidentally, the timing ofchanging uphill or downhill can be detected by the angle oftilt.

50	  Al6tude[m]	

45	  40	  35	  30	  25	  Truth	  20	  Inferred	  15	  0	  Fig. 9.

2000	  4000	  6000	  8000	  10000	  12000	  Travel	  distance[m]	

14000	  16000	  18000	  Correction inferred values(weighted all altitude changes)

55	  50	  Al6tude[m]	

45	  40	  35	  30	  25	  Truth	  20	  Inferred	  15	  0	  Fig. 10.

2000	  4000	  6000	  8000	  10000	  12000	  Travel	  distance[m]	

14000	  16000	  18000	  Correction inferred values(weighted altitude changes on uphills)

from the air pressure data. When the car drives on downhill, thecar is accelerated(the altitude is decreased) by the gravitationalacceleration. On uphill, the car decelerates and the altitude
increases. These are evidence that are relativity between thecar speed and the car altitude.
Fig. 8 shows the altitude transition inferred by the proposed
method in the continuous line and the officially public altitudein the dashed line. Additionally, it shows that the altitudechanges as the car runs the course. And, the altitude changes
with the pressure sensor value. On the downwards, the inferred
altitude follows the truth. But on the upwards, the inferred
altitude has a delay. Then, the vertical variations were shorter
than the truth. In our method, it is desirable that an altitude
error is less than 0.8m at undulating field. In fact, the result of
the upward is not sufficient for our method. And, it is desirable
that an altitude error is less than 0.4m at a flat field. But, itis so difficult because an air pressure always wiggles. Even
if you moving on the perfect flat road, an air puressure value
wiggles too. So, it seems that the altitude changed by ourmethod wiggles. Also, Fig. 8 shows another fact. When thecar goes throug between the 41.8m altitude point to the 38.9m
altitude point(the true verical interval is approximately one
meter between them), the altitude transition has a fluctuationnoise. It means the pressure sensor on the smartphone doesn’t
have a sufficient accuracy for one meter vertical interval.
We tried to correct the result of Fig. 8. Fig. 9 and Fig.
10 show the resurts which are corrected by each method. InFig. 9, All the altitude changes are weighted(weight: 1.18). As
a result, the highest altitude value which is estimated by ourmethod get close to the truth. But, the distance between thelower values and their corresponding truth increased.

VI.

C ONCLUSION

In this paper, we apply the proposed low power consumption positioning system which compares a smartphone’s sensor
data with a digital elevation map to the Eco Mileage Challengein Suzuka circuit. As a result of the circuit running, we confirm
the inferred altitude follows the truth at the downwards. But inthe upwards, the inferred altitude has a delay and errors. And
we confirmed good effectiveness of correctness to the altitudechanges by angle of tilt. But, it is still obscure parts in that.
So, We have to find out a method for deciding a weight.
Here after, we have a plan to improve them by additionallyusing the angle of tilt and the speed data more correctly. The
future work is evaluate the effect of power consumption. The
related work[3] uses an air pressure sensor all the time and aGPS sensor at established at 120 minutes intervals In that case,they succeeded in extending the loging time to three times ofthe time. Thus, we’ll set the target of the interval same as theabove.
R EFERENCES[1] Keiichiro Iwanami, Ismail Arai, “A proposal and a evaluation of a
trajectory inferring method by comparing variations air pressure sensor
data with a digital elevation model”(in Japanese) , IPSJ-UBI-37(46), pp.
1-7, (2013).
[2] GPS.gov,“GPSOverview”,
2October
2013,<http://www.gps.gov/systems/gps/>.
[3] Katsuhiro Morishita, Nagahisa Mita, “The Development of a Method for
Migration Path Estimation Using a GPS receiver and an AtmosphericPressure Sensor”(in Japanese), GPS/GNSS symposium 2011,(2011).
[4] Jun Rekimoto ， Atsushi Shinozaki ， Takahiko Sueyoshi ， Takashi
Miyake，“PlaceEngine: a WiFi location platform based on realworld
folksonomy”(in Japanese)，IC2006，pp. 95-104，(2006)[5] Daisuke Kamisaka ，Shigeki Muramatsu , Takeshi Iwamoto , Hiroyuki
Yokoyama “Dead Reckoning Method by Hand for Pedestrian Navigation
System”(in Japanise)，Trans. IPS Japan Vol.52，No.2, pp. 558-570，(2011)