Journal of Global Positioning Systems (2004)
Vol. 3, No. 1-2: 208-217
NAVIO – A Navigation and Guidance Service for Pedestrians
Günther Retscher and Michael Thienelt
Institute of Geodesy and Geophysics, Vienna University of Technology, Gusshausstrasse 27-29, A-1040 Wien, Austria
e-mail: [e-mail] Tel: [phone]; Fax: [phone]
Received: 15 Nov 2004 / Accepted: 3 Feb 2005
Abstract. In the research project NAVIO (Pedestrian
Navigation Systems in Combined Indoor/Outdoor
Environements) at our University we are working on the
improvement of navigation services for pedestrians.
Thereby we are mainly focusing on the information
aspect of location-based services, i.e., on the user’s task
at hand and the support of the user’s decisions by
information provided by such a service. Specifications
will allow us to select appropriate sensor data and to
integrate data when and where needed, to propose
context-dependent routes fitting to partly conflicting
interests and goals as well as to select appropriate
communication methods in terms of supporting the user
guidance by various multimedia cartography forms.
These taks are addressed in the project in three different
work packages, i.e., the first on “Integrated positioning”,
the second on “Pedestrian route modeling” and the third
on “Multimedia route communication”. In this paper we
will concentrate on the research work and findings in the
first work package. For continuous positioning of a
pedestrian suitable location technologies include GNSS
and indoor location techniques, cellular phone
positioning, dead reckoning sensors (e.g. magnetic
compass, gyro and accelerometers) for measurement of
heading and travelled distance as well as barometric
pressure sensors for height determination. The integration
of these sensors in a modern multi-sensor system can be
performed using an adapted Kalman filter. To test and to
demonstrate our approach, we take a use case scenario
into account, i.e., the guidance of visitors to departments
of the Vienna University of Techology. The results of
simulation studies and practical tests could confirm that
such a service can achieve a high level of performance
for the guidance of a pedestrian in urban areas and mixed
indoor and outdoor environments.
Key words: Pedestrian navigation, Location-based
services, Multi-sensor systems, Integrated positioning,
Indoor location
1 Introduction
Pedestrian navigation services require continuous
positioning and tracking of a mobile user with a certain
positioning accuracy and reliability. Especially
navigating in urban environments and mixed indoor and
outdoor areas is a very challenging task as pedestrians
move in spaces where no one of the known location
methods works continuously in standalone mode. A
solution of the problem can only be found if different
location technologies are combined in the sense of a
modern multi-sensor system. In this paper suitable
location technologies for pedestrian navigation are
identified and investigated. These technologies include
GNSS and indoor location services as well as cellular
phone positioning for absolute position determination;
dead reckoning sensors (e.g. magnetic compass, gyros
and accelerometers) for measurement of orientation and
travelled distance from a known start position as well as
barometric pressure sensors for height determination. For
location determination of a pedestrian in a multi-storey
building the use of WLAN (Wireless Local Area
Networks) is investigated. To achieve an integrated
positioning determination with other sensors and a
seamless transition between indoor and outdoor areas, a
multi-sensor fusion model based on an extended Kalman
filter approach is employed. Finally, in a practical use
case scenario the guidance of a pedestrian from public
transport stops to our Department of the Vienna
University of Technology is investigated. The results of
this study showed that such a pedestrian navigation
service can achieve a high level of performance.
2 Integrated positioning in navigation services
A reliable pedestrian navigation services requires the
determination of the current user’s position using
different sensors that are integrated into the system
design. In the work package “Integrated positioning” of

Retscher and Thienelt: NAVIO – A Navigation and Guidance Service for Pedestrians 209
the research project NAVIO (Pedestrian Navigation
Systems in Combined Indoor/Outdoor Environements)
the following challenging tasks are addressed:
• The capability to track the movements of a pedestrian
in real-time using different suitable location sensors
and to obtain an optimal estimate of the current user’s
position.
• The possibility to locate the user in 3 dimensions with
high precision (that includes to be able to determine the
correct floor of the user in a multi-storey building).
• The capability to achieve a seamless transition for
continuous positioning determination between indoor
and outdoor areas.
Thereby a navigation support must be able to provide
location, orientation and movement of the user as well as
related geographic information matching well with the
real world situation experienced by pedestrians. The
integration of the sensors in a modern multi-sensor
system can be performed using a Kalman filter as this
algorithm is particularly suited for real-time evaluation.
In the following the state-of-the-art in mobile positioning
is discussed and suitable sensors for integrated
positioning in a pedestrian navigation service are
identified.
2.1 State-of-the-art in mobile positioning
Satellite-positioning technologies (GNSS) are employed
most commonly for outdoor navigation. Then the
achievable positioning accuracies of the navigation
system are on the few meters to 10 m level in standalone
mode or sub-meter to a few meter level in differential
mode (e.g. DGPS). If an insufficient number of satellites
is available for a short period of time due to obstructions,
then in a conventional approach observations of
additional sensors are employed to bridge the loss of lock
of satellite signals. For pedestrian navigation, sensors
such as a low-cost attitude sensor (digital compass)
giving the orientation and heading of the person being
navigated and a digital step counter or accelerometers for
travel distance measurements can be employed. Using
these sensors, however, only relative position
determination from a known start position (also referred
to as Dead Reckoning DR) is possible and the achievable
accuracy depends on the type of movement tracking
sensors used and the position prediction algorithm
adopted.
For indoor positioning different techniques have been
developed. They offer either absolute or relative
positioning capabilities. Some of them are based on
short-range or mid-range technologies using sensors such
as transponders or beacons installed in the building (see
e.g. Klinec and Volz, 2000; Pahlavan et al., 2002). An
example are the so-called Local Positioning Systems
(LPS) that have an operation principle similar to GNSS.
The LPS systems claim to achieve a distance
measurement accuracy of about 0.3 to 1 m (see e.g. Werb
and Lanz, 2000; Sypniewski, 2000), but no details are
given on the test results and the achievable accuracy on
position fixing. Other indoor positioning systems include
so-called Active Badge or Active Bats Systems
(Hightower and Boriello, 2001). These systems are
mainly employed for the location of people and finding
things in buildings. Also WLAN (Wireless Local Area
Networks) can be employed for location determination.
In this case, the signal strength of the radio signals from
at least one WLAN access point installed in the building
is measured. The location fix is then obtained with
triangulation using measurements to several access points
or through comparison with in database stored signal
strength values from calibrated points (this method is also
referred to as fingerprinting). Further information can be
found in e.g. (Bastisch et al., 2003; Beal, 2003; Imst,
2004; Retscher 2004b). As the indoor radio channel
suffers from severe multipath propagation and heavy
shadow fading, the fingerprint method provides higher
accuracies than triangulation. It is reported that
positioning accuracies of about 1 to 3 m could be
obtained in a test office building using the fingerprint
WLAN positioning method (Imst, 2004). Another
alternative in indoor geolocation applications is the use of
ultra wideband (UWB) systems, which exploit
bandwidths in excess of 1 GHz, to measure accurate time
of arrival (ToA) of the received signals for estimation of
distance (Pahlavan et al., 2002). With results of
propagation measurement in a typical modern office
building, it has been shown that the UWB signal does not
suffer multipath fading (Win and Scholtz, 1998), which is
desirable for accurate ToA estimation in indoor areas.
The main disadvantage, however, is the possible
interference of UWB devices with the GPS system. Also
Bluetooth, which has been originally developed for short
range wireless communication, can be employed for
locating mobile devices in a certain cell area that is
represented by the range of the device which is typically
less than 10 m. It can be employed for location
determination using active landmarks. Locating the user
on the correct floor of a multistory building is another
challenging task. For more accurate determination of the
user’s position in vertical dimension an improvement can
be achieved employing a barometric pressure sensor or
digital altimeter additionally (see Retscher and Skolaut,
2003).
As an alternative for location determination in indoor and
outdoor environments, mobile positioning services using
cellular phones can be employed. Apart from describing
the location of the user using the cell of the wireless
network, more advanced positioning methods have been
developed. Most of them are based on classical terrestrial

210 Journal of Global Positioning Systems
navigation methods where at least two observations are
required to obtain a 2-D position fix (see e.g. Balbach,
2000; CPS, 2001; Drane et al., 1998; Hein et al., 2000;
Retscher, 2002). The achievable positioning accuracy
thereby depends mainly on the location method and type
of wireless network (GSM, W-CDMA, UMTS). As
advanced and more accurate methods, such as the E-OTD
(Enhanced Observed Time Difference) method, require
modification of the network as well as installation of
additional hardware in the network and reference stations
which are called LMU’s (Location Measurement Units),
they have not been widely deployed yet. Recent
developments have therefore been concentrated on the
reduction of network modification. The so-called Matrix
method (see Duffett-Smith and Craig, 2004) does not
need any additional hardware in the network apart from a
SMLC (Serving Mobile Location Centre) where the
location determination of the mobile handset is
performed. Using this method positioning accuracies of
50 to 100 m at the 67 % reliability level can be achieved
in the GSM network.
2.2 Suitable sensors for pedestrian navigation services
Suitable sensors and location techniques for pedestrian
navigation have been identified at the start of the project
NAVIO. Table 1 gives an overview about the positioning
methods and the sensors that will be employed in our
project. For absolute position determination primarily
GNSS is employed. In the case of no GNSS availability it
can be replaced by location techniques using cellular
phones or indoor positioning systems (e.g. WLAN
positioning). Apart from this sensors, relative DR (Dead
Reckoning) sensors are employed for the observation of
the travelled distance (from velocity and acceleration
measurements), direction of motion or heading and height
difference. The observables as well as their accuracies are
summarized in Table 1.
2.3 Integrated positioning using a multi-sensor fusion
model
An integrated position determination, using observations
of all available sensors, however, is not performed in
most common navigation systems. In vehicle navigation
systems for instance the resulting trajectory is determined
mainly based on the dead reckoning observations; GNSS
is used for updating and resolving the systematic error
growth of the DR observations. For guidance of a
pedestrian in 3-D space and updating of his route,
continuous position determination is required with
Method Sensor Observations Accuracy
GNSS e.g. Garmin GPS 35
DGPS y, x, z 6-10 m 1-4 m
Indoor Positioning
e.g. WLAN Positioning IMST ipos y, x, z 1-3 m
Cellular Phone
Positioning
GSM (e.g. Matrix method) y, x 50-100 m
Dead Reckoning
e.g. PointResearch DRM-III Dead Reckoning
Module
y, x
z
φ
20-50 m per 1 km
3 m
1°
Direction of Motion
(Heading)
e.g. Honeywell Digital Compass Module HMR
3000
φ 0.5°
Acceleration
e.g. Crossbow Accelerometer CXTD02 atan, arad, az > 0.03 ms-2
Velocity from GNSS
e.g. Garmin GPS 35 vy, vx
vz
~ 0,05 m-1
~ 0,2 m-1
Barometer
e.g. Vaisala Pressure sensor PTB220A z 1-3 m
Tab. 1 Sensors for pedestrian navigation services with their observables and accuracies
(Garmin, 2004; Imst, 2004; Duffett-Smith and Craig, 2004; PointResearch, 2004; Honeywell, 2004; Crossbow, 2004; Vaisala, 2004)
where y, x, z are the 3-D coordinates of the current position, vy, vx, vz are the 3-D velocities, φ is the direction of motion (heading) in the ground plane
xy, atan is the tangential acceleration and arad is the radial acceleration in the ground plane xy, az is the acceleration in height (z coordinate)

Retscher and Thienelt: NAVIO – A Navigation and Guidance Service for Pedestrians 211
positioning accuracies on the few meter level or even
higher, especially for navigation in multi-storey buildings
in vertical dimension (height) as the user must be located
on the correct floor. The specialized research hypothesis
of this work package in the project NAVIO is that a
mathematical model for integrated positioning can be
developed that provides the user with a continuous
navigation support. Therefore appropriate location
sensors have to be combined and integrated using a new
multi-sensor fusion model. A Kalman filter approach is
particular suited for the integration and sensor fusion in
real-time. Extending basic filter concepts, a Kalman filter
approach which integrates all observations from the
different sensors will be developed. The model must be
able to make full use of all available single observations
of the sensors at a certain time to obtain an optimal
estimate of the current user state (i.e., position,
orientation and motion). For further information on the
multi-sensor fusion model the reader is referred to
Retscher and Mok (2004) and Retscher (2004b).
3. Practical sensor tests
Practical tests in our research project are carried out for
the guidance of visitors of the Vienna University of
Technology to certain offices in different buildings or to
certain persons. Thereby we assume that the visitor
employs a pedestrian navigation system using different
sensors that perform an integrated positioning. Start
points are nearby public transport stops, e.g. underground
stop Karlsplatz in the center of Vienna or railway station
Südbahnhof near our university. In the following, results
of satellite positioning and first test measurements with
the Dead Reckoning Module DRM III from Point
Research are presented.
3.1 Satellite positioning test results
Figure 1 shows the GPS measurements for the path from
the underground stop Karlsplatz near our University to
our Institute building of the Vienna University of
Technology in the Gusshausstrasse located in the fourth
district of the city of Vienna using two different GPS
receivers, i.e., the Trimble GPS Pathfinder Pocket and the
Garmin eTrex. The length of the path is approximately
500 m and it starts at the exit of underground station
Karlsplatz where open skys provide free satellite
visibility. Then the pedestrian walks through a park (i.e.,
the Resselpark) with trees where satellite signals are
frequently blocked over short periods. Both GPS
receivers, however, are able to determine the track of the
pedestrian with a reasonable positioning accuracy. It can
be seen, that the track of the Garmin eTrex receiver is
much smoother as he performs some filtering or
smoothing to estimate the receiver track compared to the
Trimble GPS Pathfinder Pocket which provides the
original GPS single point positions. After leaving the
park, the path continues in a narrow street (i.e.,
Karlsgasse) onwards to our Institute building where 5-
storey buildings with heights of typically 20 m cause
obstructions of the satellite signals. The measurement
result from the Garmin eTrex shows an increasing
deviation from the true pedestrian path where the
maximum deviation in the range of 13 m is reached at the
intersection of Karlsgasse with Frankenberggasse. Then
the position changes quite significantly as more GPS
satellites become available. In the following, the positions
show again a drift from the true path. As the Trimble
GPS Pathfinder Pocket receiver does not apply any
filtering or smoothing, the positions in the Karlsgasse are
much more scattered than with the Garmin eTrex.
Maximum deviations from the true path of the pedestrian
of up to 25 m are reached and in some parts no position
determination is possible. This gaps have to be bridged
using dead reckoning observations. At the intersection of
Karlsgasse with Gusshausstrasse enough satellites are
visible for positioning and the path ends in front of the
building where our Institute is located.
3.2 Dead reckoning test results
For the measurements the Dead Reckoning Module DRM
III from Point Research (PointResearch, 2004) was
employed. The system is a self contained navigation unit
where GPS is not required for operation. It provides
independent position information based on the user’s
stride and pace count, magnetic north and barometric
altitude. The module is designed to self-calibrate when
used in conjunction with an appropriate GPS receiver,
and can produce reliable position data during GPS
outages. The system consists of an integrated 12 channel
GPS receiver, antenna, digital compass, pedometer and
altimeter. The module is clipped onto the user’s belt in
the middle of the back and the GPS antenna may be
attached to a hat. Firmware converts the sensor signals to
appropriate discrete parameters, calculates compass
azimuth, detects footsteps, calculates altitude and
performs dead reckoning position calculation. A Kalman
filter algorithm is used to combine dead reckoning
position with GPS position to obtain an optimum estimate
for the current user’s position and track. With the dead
reckoning module and GPS integrated together, a clear
view of the sky is only required for obtaining the initial
position fix. The fix must produce an estimated position
error of 100 m or less to begin initialization. Subsequent
fixes use both dead reckoning and GPS data, so
obstructed satellites are not as critical as in a GPS only
configuration. The Kalman filter continuously updates
calbration factors for stride length and compass mounting

212 Journal of Global Positioning Systems
Fig. 1 GPS measurements for the pedestrian path from the underground station Karlsplatz to our office building
of the Vienna University of Technology

Retscher and Thienelt: NAVIO – A Navigation and Guidance Service for Pedestrians 213
offset. The GPS position error must be less than 30 m
before GPS data will be used by the Kalman filter, and
the first such fix will also initialize the module’s latitude
and longitude. Subsequently, the filter will use any GPS
position fix with an estimated position error of 100 m or
less, adjusting stride, body offset, northing, easting,
latitude and longitude continually.
First of all the Dead Reckoning Module DRM III was
tested in open area with GPS satellite visibility. As test
site pedestrian paths in the park of Schönbrunn Palace in
Vienna have been chosen. Figure 2 shows the trajectory
of the pedestrian as well as GPS and two different dead
reckoning measurements. For the dead reckoning
measurements the GPS positioning and the calbration of
the stride length using the Kalman filter algorithm was
deactivated. Without using the filter, GPS measurements
are not employed to calibrate the stride length and the
dead reckoning module uses the preset value of 800 mm
for the stride length. The heading of the user is
determined from measurements of a digital compass and
a gyro. For the first dead reckoning measurement (No. 1)
shown in Figure 2 both sensors are employed to obtain
the heading, for the second measurement (No. 2) only the
observations of the compass are employed. This results in
larger deviations from the trajectory for the second dead
reckoning measurements; they range from 17 m over a
distance of 150 m and 29 m over 200 m. For the first
dead reckoning measurement the deviations from the
trajectory are in the range of 7 m over a distance of 150 m
and 20 m over 200 m. It can therefore be recommended
that a combination of compass and gyro measurements
are employed for heading observation. An improvement
of the dead reckoning measurements can only be
achieved if the calibration of the stride length is
employed to obtain a better estimate for the travelled
distance. For comparison, GPS measurements from the
internal receiver of the DRM III module are shown in
Figure 2 which reach a maximum deviation of 7 m from
the trajectory.
-1620 -1600-1580 -1560 -1540 -1520 -1500 -1480 -1460-1 440
5.3383
5.3383
5.3383
5.3384
5.3384
5.3384
5.3384
5.3384
x 106
Eas t [m ]
North [m]
DRM III Measurement(Park Schönbrunn)
Trajectory
Dead Reckoning Measurement 1
Dead Reckoning Measurement 2
GPS Meas ur ement
Fig. 2 Test measurements with the Dead Reckoning Module DRM III in the park of Schönbrunn Palace in Vienna

214 Journal of Global Positioning Systems
Fig. 3 Test measurements with the Dead Reckoning Module DRM III along a closed loop on narrow streets in the city Vienna
Figure 3 shows the measurement results of the DRM III
dead reckoning module and GPS single point positions on
a closed loop in the city of Vienna starting from the
Resselpark about 160 m along Argentinerstrasse, then
200 m along Gusshaustrasse and 290 m along Karlsgasse
back to the start point. The total length of the path is
around 550 m. The streets are quite narrow with 5-storey
buildings with an average heigth over 20 m causing
frequent obstructions of the satellite signals and high
GDOP values. As can be seen in Figure 3, the GPS only
measurements are quite far away from the true path of the
pedestrian along most parts of the track and a reliable
match to the correct street would not always be possible.
Using the position estimates of the dead reckoning
module the resulting trajectory follows the pedestrian
track along the most part of path and the deviations are
only in the range of a few meters. Due to the large errors
of the GPS postions, however, the calbration algorithm of
the DRM III fails at the end and the resulting trajectory
cannot follow the track of the pedestrian any more.
Figure 4 shows the calbration of the stride length in the
Kalman filter and it can be seen that the stride length gets
smaller and smaller until it reaches nearly 500 mm which
is not a matter of fact. In this case, it seems that the
weighting of GPS positioning is too high in the Kalman
filter calibration process for the stride length.
In our project, an improvement of the current position
estimate of a pedestrian using observations of different
dead reckoning sensors in combination with GPS and
other absolute postioning techniques (see Table 1) should

Retscher and Thienelt: NAVIO – A Navigation and Guidance Service for Pedestrians 215
be achieved using a new multi-sensor fusion model based
on an extended Kalman filter approach. Further
information about this approach can be found in Retscher
and Mok (2004) and Retscher (2004b).
0100 200300 400 500 600700800 900
500
550
600
650
700
750
800
Measu r ement s
Stride [mm]
DRM III: Stri deDetermi nati on
Measu r ement
Fig. 4 Calibration of the stride length using GPS observations in the Kalman filter of the DRM III Dead Reckoning Module
4. Conclusions
In the NAVIO project major aspects being important
when concepting a pedestrian navigation service are
investigated, i.e., integrated positioning, multi-criteria
route planning, and multimedia route communication (see
Gartner et al., 2004a and b). As a result, a specific
pedestrian navigation service as use case will derive the
requirements on positioning, route planning, and
communication. A prototype of the service will guide
visitors of the Vienna University of Technology to
departments and persons. Practical tests will allow us to
evaluate and demonstrate the usability of the service, and
thus, prove the projects attempts.
With the work package “Integrated Positioning” of the
project we will contribute to the integration of location
sensors in the sense of a multi-sensor system to achieve a
continuous positioning of the user of the service and a
seamless transition between indoor and outdoor areas.
Suitable sensors and location methods have been
identified and the basic concept of a multi-sensor fusion
model for integrated positioning has been developed
(Retscher and Mok, 2004; Retscher 2004b). Special
emphasis has been given on the location determination
and navigation of a pedestrain in a multi-storey building.
Currently we are investigating the use of WLAN
positioning for location determination in indoor areas.
The second work package of the project NAVIO on
“Pedestrian route modeling” is dealing with the
ontological modelling of navigation tasks, deriving well
founded criteria and optimization strategies in route
selection; and the third work package on “Multimedia
route communication” is working on models for context-
dependent communication modes of route information. In
general, it can be said that the results of the project
NAVIO will contribute to the improvement of modern
(pedestrian) navigation services.
Acknowledgements
The research work presented in this paper is supported by
the FWF Project NAVIO of the Austrian Science Fund

216 Journal of Global Positioning Systems
(Fonds zur Förderung wissenschaftlicher Forschung)
(Project No. P16277-N04).
References
Balbach O. (2000) UMTS – Competing Navigation System and
Supplemental Communication System to GNSS, in:
Papers presented at ION GPS Meeting 2000, Salt Lake
City, Utah, U.S.A., September 19-22, 2000.
Bastisch H., S. Häfker, J. Krause, S. Wilking, B. Haje (2003)
Projekt NOM@D - Location Based Services, Presentation,
Communication Networks, University Bremen, Germany,
http://www.comnets.uni-bremen.de/itg/itgfg521/aktuelles/
fg-treffen-230103/20030120-LBS-Allgemein-v2.pdf, Last
access 07.2004 (German).
Beal J. R. (2003) Contextual Geolocation - A Specialized
Application for Improving Indoor Location Awareness in
Wireless Local Area Networks, in: Papers presented at the
Midwest Instruction and Computing Symposium
MICS2003, The College of St. Scholastica, Duluth,
Minnesota, USA, http://www.css.edu/de pts/cis/
mics_2003/MICS2003_Papers/Beal.PDF, Last access
07.2004.
CGALIES (2002) Co-ordination Group on Access to Location
Information by Emergency Services, Work Package 1
Report, 2002, http://www.telematica.de/cgalies/, Last
access 07.2004.
Crossbow (2004) CXTD Digital Tilt and Acceleration Sensor,
Product Information, Crossbow, USA,
http://www.xbow.com/Products/Product_pdf_files/Tilt_pdf
/6020-0012-01_B_CXTD.pdf, Last access 07.2004.
CPS (Cambridge Positioning Services Ltd.) (2001) Cursor™
Mobile Location System, Product Information at
http://www.cursor-system.com/, Last access 07.2004.
Drane Ch., M. Macnaughtan, C. Scott (1998) Positioning GSM
Telephones, IEEE Communications Magazine, April 1998.
Duffett-Smith P. J., J. Craig (2004) Matrix, and Enhanced
Satellite Positioning, Invited paper presented at the 5th
IEE International Conference on 3G Mobile
Communication Technologies, 18-20 October 2004, Savoy
Place, London, UK, 4 pgs.
Garmin (2004) Garmin GPS Products, Garmin Ltd., USA,
http://www.garmin.com/products/, Last access 07.2004.
Gartner G., A. Frank, G. Retscher (2004a) Pedestrian
Navigation System for Mixed Indoor/Outdoor
Environments, in: Gartner G. (Ed.): Geowissenschaftliche
Mitteilungen, Schriftenreihe der Studienrichtung
Vermessungswesen und Geoinformation, TU Wien, Heft
66, Papers presented at the 2nd Symposium on Location
Based Services and Telecartography, January 28-29, 2004,
Vienna, Austria, pp. 161-167.
Gartner G., A. Frank, G. Retscher (2004b) Pedestrian
Navigation System in Mixed Indoor/Outdoor
Environment – The NAVIO Project, in: Schrenk M. (Ed.):
CORP 2004 and Geomultimedia04. Proceedings of the
CORP 2004 and Geomultimedia04 Symposium, February
24-27, 2004, Vienna, Austria, pp. 165-171,
http://corp.mmp.kosnet.com/CORP_CD_2004/archiv/pape
rs/CORP2004_GARTNER_ FRANK_RETSCHER.PDF,
Last access 07.2004.
Hein G., B. Eissfeller, V. Oehler, J. O. Winkel (2000) Synergies
Between Satellite Navigation and Location Services of
Terrestrial Mobile Communication, in: Papers presented
at ION GPS Meeting 2000, Salt Lake City, Utah, U.S.A.,
September 19-22, 2000.
Hightower J., G. Borriello (2001) Location Systems for
Ubiquitous Computing, Computer, Vol. 34, No. 8, IEEE
Computer Society Press, August 2001, pp. 57-66.
Honeywell (2004) HMR 3000 Digital Compass Module, User’s
Guide, Honeywell International Inc., USA,
http://www.ssec.honeywell. com/magnetic/datasheet s/hmr3
000_manual.pdf, Last access 07.2004.
Imst (2004) Indoor Locating – Imst ipos, Project c21,
Presentation, IMST GmbH, Carl-Friedrich-Gauß-Str. 2, D-
47475 Kamp-Lintfort, Germany.
Klinec D., S. Volz (2000) NEXUS – Positioning and
Communication Environment for Spatially Aware
Applications, in: Papers presented at ISPRS 2000,
Amsterdam, The Netherlands, Part B2, pp. 324-330, see
also: http://www.nexus.uni-stuttgart.de/, Last access
07.2004.
Pahlavan K., X. Li, J.-P. Mäkelä (2002) Indoor Geolocation
Science and Technolgy, IEEE Communications Magazine,
February 2002, pp. 112-118.
PointResearch (2004) DRM-III Dead Reckoning Module -
Engineering Development Tools, PointResearch
Corporataion, USA,
http://www.pointresearch.com/drm_eval.htm, Last access
07.2004.
Retscher G. (2002) Diskussion der Leistungsmerkmale von
Systemen zur Positionsbestimmung mit Mobiltelefonen
als Basis für Location Based Services, in: Papers
presented at the Symposium on Telekartographie, January
28-29, 2002, TU Wien, Austria, published as
Geowissenschaftliche Mitteilungen, No. 58, Institute of
Cartography, Vienna University of Technology, Austria,
pp. 41-58 (German).
Retscher G., G. Skolaut (2003) Untersuchung von
Messsensoren zum Einsatz in Navigationssystemen für
Fußgänger, Zeitschrift für Geodäsie, Geoinformation und
Landmanagement ZfV, No. 2, pp. 118-129 (German).
Retscher G. (2004a) Multi-sensor Systems for Pedestrian
Navigation and Guidance Services, in: Papers presented at
the 4th Symposium on Mobile Mapping Technology,
March 29-31, 2004, Kunming, China, CD-Rom
Proceedings, 7 pgs.
Retscher G. (2004b) Multi-sensor Systems for Pedestrian
Navigation, in: Papers presented at the ION GNSS 2004
Conference, September 21-24, 2004, Long Beach,
California, USA, CD-Rom Proceedings, 12 pgs.
Retscher G. (2004c) Pedestrian Navigation Systems and
Location-based Services, Invited paper presented at the 5th

Retscher and Thienelt: NAVIO – A Navigation and Guidance Service for Pedestrians 217
IEE International Conference on 3G Mobile
Communication Technologies, 18-20 October 2004, Savoy
Place, London, UK, 5 pgs.
Retscher G., E. Mok (2004) Sensor Fusion and Integration
using an Adapted Kalman Filter Approach for Modern
Navigation Systems, Survey Review, Vol. 37, No. 292
April 2004.
Sypniewski J. (2000) The DSP Algorithm for Locally
Deployable RF Tracking System, in: Papers presented at
International Conference on Signal Processing with
Applications, Orlando, October 2000,
http://www.syptech.com/publications/publications.html,
Last access 07.2004.
Vaisala (2004) PTB220 Digital Barometer, Vaisala, Finland,
http://www.vaisala.com/
DynaGen_Attachments/Att2468/PTB220%20Brochure.pdf
, Last access 07.2004.
Werb J., C. Lanz (2000) Designing a positioning system for
finding things and people indoors,
http://www.rftechnologies.com/pinpoint/index.htm, Last
access 07.2004.
Win M., R. Scholtz (1998) On the Performance of Ultra-Wide
Bandwith Signals in Dense Multipath Environment,
IEEE Commun. Letters, Vol. 2, No. 2, Feb. 1998, pp. 51-
53.