Journal of Global Positioning Systems (2007)
Vol.6, No.1: 80-88
PC104 Based Low-cost Inertial/GPS Integrated Navigation Platform:
Design and Experiments
Di Li, René Jr. Landry a n d Ph i l i p p e Lav o i e
LACIME, Department of Electrical Engineering
École de Technologie Supérieure (ÉTS), University of Quebec, Montreal, Quebec, Canada, H3C 1K3
Abstract. The integration of Global Positioning System
(GPS)/Inertial Navigation System (INS) has become very
important in various navigation applications. In the last
decade, with the rapid development of Micro Electro
Mechanical Sensors (MEMS), great interest has been
generated in low cost integrated GPS/INS applications.
This paper presents a PC104 based low cost GPS/INS
integrated navigation platform. The platform hardware
consists of low cost inertial sensors and an assembly of
various PC104 compatible peripherals, such as data
acquisition card, GPS receiver, Ethernet card, mother
board, graphic card, etc. The platform software including
inertial/GPS data acquisition, inertial navigation
calculation and integrated GPS/INS Kalman filter is
implemented with Simulink, which can be directly loaded
and processed in the PC104 mother board with the aid of
Matlab Real-Time Workshop (RTW) utility. This
platform is totally self-embedded and can be applied
independently or as part of a system. Simulation and real
data experiments have been performed to validate and
evaluate the proposed design. A very low cost MEMS
inertial sensor was utilized in the experiments. The
reference is the navigation solution derived from a tactic
grade Inertial Measurement Unit (IMU). Test results
show that PC104 navigation platform delivers the
integrated navigation solutions comparable to the
reference solutions, which were calculated with a
conventional laptop computer, however with less power
consumptions, less system volume/complexity and much
lower over-all costs. Moreover the platform hardware is
compatible to various inertial sensors of different grades
by configuring the related parameters in the system
software.
Keywords: PC104, GPS, MEMS, Kalman filter, Real-
time Workshop, xPC Target
1 Introduction
As an independent means of navigation, GPS is capable
of delivering position and velocity information with time-
independent precision, while the performance becomes
unreliable however when the system is exposed to high
dynamics, interference from communication equipments
and intentional/non-intentional jamming, etc. Compared
with GPS, INS providing position, velocity, and attitude
information via the measurements from inertial sensors
has various advantages, such as totally autonomous, high
dynamic response, good short-term accuracy and robust
performance when exposed to interference and or
jamming. However its usage as a stand-alone navigation
system is limited due to time-dependent growth of the
inertial sensor bias/noise. Because of the aforementioned
complementary characteristics, GPS and INS are
commonly coupled by Kalman filter to augment the over-
all performance by overcoming the shortcomings of each
individual system.
A high precision integrated GPS/INS system requires
expensive inertial sensors that have exceptional long term
bias stability. The sensor cost limits such kind of
integrated navigation systems to very expensive
applications (Hayward et al., 1997). Over the past decade
low cost MEMS are experiencing rapid improvements in
terms of precision, robustness, size, high dynamic
response and so on. With the quick growth in demand for
low cost navigation systems for general aviation,
unmanned automotive vehicles, locating personnel,
mobile mapping systems, athletic training and
monitoring, and computer games, etc, it has become
important to develop low cost integrated navigation
systems.
This paper introduces the development of a low cost
inertial/GPS integrated navigation platform. The platform
hardware is constructed on the basis of a PC104 computer
and an assembly of PC104 peripherals, such as data
acquisition card, graphic card, Ethernet card, power

Li et al.: PC104 Based Low-cost Inertial/GPS Integrated Navigation Platform: Design and Experiments 81
supply board and a PC104 compatible GPS receiver, etc.
Matlab Simulink’s modularity and graphical design make
it convenient for point-wise improvements and facilitate
the ramp-up knowledge of future contributors (Giroux,
2005). Also one of the Simulink’s powerful assets is the
possibility to do rapid real-time testing through the RTW
and xPC Target. Therefore the system software
comprising the data acquisition, strapdown inertial
mechanization and integrated Kalman filter is
implemented by the Matlab Simulink. This Simulink
based platform software can be directly compiled into
executable code for PC104 computer by the RTW. The
Simulink based design scenario has various advantages,
such as rapid real-time prototyping and fast re-
designing/debugging which is particularly helpful in the
early stage of developing a real-time navigation system.
The architecture of PC104 navigation platform is
depicted in Fig. 1.
Fig. 1 Architecture of PC104 navigation platform
This paper is organized as follows. Section 2 presents an
overview of the platform, the devices utilized, the
software used to communicate and the algorithm
structure. Section 3 describes the low cost MEMS real
time test results followed by a performance evaluation.
Finally, future work and potential enhancement of the
project are discussed in the conclusions.
2 Hardware platform and software design
2.1 Hardware platform
The hardware platform consists of a PC104 computer and
an assembly of PC104 peripherals, such as data
acquisition card, graphic card, Ethernet card, power
supply board and a PC104 compatible GPS receiver. All
the component parts are stacked up together through the
PC104 bus. This configuration makes the individual
peripheral independent from each others as well as
provides a good synchronization for the data
transmission. For example, the platform utilizing MEMS
inertial sensor is depicted in Fig. 2.
Fig. 2. PC104 navigation platform prototype
On the top layer there is a MEMS inertial sensor which
generally is not PC104 compatible but the information is
sent via an external bus to the data acquisition card. This
layer provides the specific force and angular rate
measurements given by the MEMS. On the next layer,
there is a PC104 compatible GPS receiver which obtains
and transmits the position and velocity observations to
the algorithm. Following the GPS receiver, there is a 16-
bit data acquisition card which collects and converts the
MEMS inertial analog signal to digital data. If the digital
inertial data are available in MEMS sensor, e.g. a USB
interface is provided by the newly acquired MEMS
sensor in the lab (nIMU MEMSenseTM) or a RS232
interface used in the tactic IMU, this card can be
excluded. The digital data should be directly connected to
the PC104 computer USB/RS232 interface, under which
a PC104 compatible power board provides the power
supply to each card. Then, a PC104 compatible network
card is added to build a TCP/IP connection to a host
computer. This connection is used for carrying out
several important tasks: first, to download the programs
to the mother board, second to setup the control
parameters and record the calculated navigation results,
then it enables the platform in the remote-control mode
through Internet. This platform is also equipped with a
graphic card to display all the data/parameters/results
residing in the platform in real time on an additional
screen. On the bottom layer, there is the PC104
motherboard where the algorithm is loaded and executed.

82 Journal of Global Positioning Systems
2.2 Platform software
The proposed system software package consists of
strapdown inertial mechanization, the integrated Kalman
filter and the software interface. The scheme of the
system software is shown in Fig. 3.
Fig. 3. Scheme of the system software
Strapdown inertial mechanization
There are many approaches to implementing the
strapdown mechanization, which are generally divided
into two categories, i.e. a multi-speed digital design
including accurate coning, sculling, scrolling
compensations for attitude/velocity calculation and a
simplified single speed continuous design without any
attitude, velocity or position compensation algorithm
(Savage, 2000). Both algorithmic designs have been
implemented and investigated by Li et al. (2007). By
utilizing Matlab Simulink’s capabilities to directly
evaluate the differential equations in the continuous
mode, a simpler single high speed inertial calculation
algorithm structure based on the INS analytically
continuous differential equations is implemented in this
study. The attitude, velocity and position solutions are
derived by evaluating the rate equations as follows:
NN
EN
E
N
E
N
IEIEP
NN
IE
N
EN
N
P
N
SF
N
N
EN
N
IE
L
N
L
IL
L
B
L
IL
B
IB
L
B
L
B
vhCC
Rggvgav
CCCC
⊥
=×=
××−=×+−+=
+=×−×=
& &
&
&
)(
)()2(
)()()(
ω
ωωωω
ωωωωω
(1)
where L
B
Cis the attitude matrix; ×
B
IB
ω is the skew-
symmetric matrix of the angular rate vector in the body
frame (B-frame), ×
L
IL
ω is the skew-symmetric matrix of
the angular rate vector caused by the translational motion
in the L frame, N
EN
ω is the angular rate of N Frame
relative to E Frame, N
vis the velocity vector, N
SF
ais the
specific force vector, P gis the plumb-bob gravity, g is
the standard gravity, R is the position location vector
from the earth centre. For example the Matlab
implementation of the attitude solution is depicted in Fig.
4.
Fig. 4. Attitude implementation in Simulink
Integrated Kalman filter
According to the aforementioned advantages, the Kalman
filtering is applied to combine the inertial/GPS data in
this study. The roles of the Kalman filter in our
application are to estimate/correct the errors in navigation
parameters, e.g. position, velocity and attitude, and also
to estimate the inertial sensor bias/drift which enables the
in-motion calibration of inertial sensor raw
measurements.
The measurements in the proposed Kalman filter are
formed from the comparison between the INS calculated
and GPS receiver derived position/velocity data. Such
measurements are derived using the estimated attitude,
velocity, position and MEMS sensor error states. The
design of the Kalman filter dynamic model is based on
the INS error model. In our system, the so-called psi-
angle error model is applied, which defines the errors in
attitude, velocity and position parameters ( RV
δ
δ
,,
Ψ
)
(Savage, 2000):
NN
EN
NN
NN
EN
N
IE
N
Mdl
NN
SF
B
SF
N
B
N
NN
IN
B
IB
N
B
N
RVR
VgaaCV
C
δωδδ
δωωδδδ
ωδω
×−=
×+−+Ψ×+=
Ψ×−−=Ψ
&
&
&
)2( (2)
where, RV
δ
δ
,,
Ψ
are the errors in attitude, velocity and
position parameters, N
B
C is the attitude matrix expressed
in the navigation frame (N-frame), B
IB
δω
is the angular-
rate error vector in the B-frame, B
SF
N
SF aa
δ , are the specific
force vector in the N-frame and the specific force error

Li et al.: PC104 Based Low-cost Inertial/GPS Integrated Navigation Platform: Design and Experiments 83
vector in the B-frame, N
Mdl
g δ
is the plump-bob gravity
error, N
EN
N
IE
ωω , are the earth rotation rate vector and the
transport rate vector in the N-frame, respectively; N
IN
ω
the N-frame rotation rate in the inertial frame (I-frame).
The error parameters in attitude, velocity and position
are represented as the error states which are propagated
in the Kalman filter through the dynamic model. As in
the application of low cost inertial sensors, the sensor
noises are the dominant terms causing attitude, velocity
and position errors, i.e. many error terms in the INS
error model are negligible when compared with raw
measurements errors B
SF
a δ and B
IB
δω
. Therefore those
terms can be removed from the error model which in
turn reduces the number of the Kalman filter error
states, remarkably decreasing the computing load (Li et
al., 2007). The simplified error equation is given as:
NN
NN
SF
B
SF
N
B
N
B
IB
N
B
N
VR
aaCV
C
δδ
δδ
δω
=
Ψ×+=
−=Ψ
&
&
&
(3)
This continuous Kalman dynamic model should be
discretized to build Kalman state transition matrix (Phi-
Matrix) and the integrated dynamic noise matrix (Q
matrix). The discretizing rate is chosen as the available
maximum sensor data rate, which is sensor dependent,
typically varying from 100Hz to 200Hz. The Matlab
implementation of the Kalman Dynamic model is
depicted in Fig. 5.
Fig. 5. Simulink implementation of Kalman state transition matrix
Software interface
For real time processing, the Simulink implemented
navigation algorithm can be re-written in C, complied as
executable codes and then downloaded to the hardware
platform by the RTW. Simulink’s xPC Target Library
provides the necessary drivers for the different
peripherals. The connection to the monitor, keyboard and
the host computer to display, read and command the
platform in the real time is created by the drivers from
xPC Target Library. For example, the data acquisition of
the raw inertial measurement implemented in Simulink
by xPC Target is depicted in Fig. 7 . In this study, the
Diamond MM 16 AT card is utilized by the platform. The
configuration of the Diamond MM16AT Acquisition
Driver Module is shown in Fig. 6.
The settings for this module are Number of Channels:
Number of different outputs; Range Vector: Maximum
Amplitude of the Output; Input Coupling: Number of
input for the ADC; Sample Time: Output Sampling Time;
Base Address: Output Address. In this platform, the data
are encoded with 16 bits precision for the 5 volts range.
Therefore the resolution is 7.63×10-5 V/bit which means a
resolution of 76.3 μg for the accelerometers as the
sensitivity is 1000 mV/g and a resolution of 6.1×10-3
º/sec for the gyroscopes as the sensitivity is 12.5
mV/º/sec.
Fig. 6. Configuration of data acquisition driver
3 Experiments
Simulation and real data experiments were performed to
validate and evaluate the proposed design. The simulation
test was to validate the designed navigation algorithm.
Following the simulation, the real data experiment tests
the platform hardware and software. A low cost MEMS
and a low cost tactic IMU were utilized in the real data
experiments.

84 Journal of Global Positioning Systems
3.1 Simulation test
Fig. 8 depicts the trajectory processed in the simulation.
The red curve is the reference trajectory generated by the
Flight Simulator (Microsoft FS2004) and the blue one
corresponds to the solution calculated by the algorithm.
Fig. 7. Simulink implementation of data acquisition software
Fig. 8. 3D trajectory simulation vs. reference
Position: The Kalman filter provides the updated
corrections of the position errors every 10ms. The
maximum error located on an axis is about 5 meters. This
period corresponds to the high dynamic period during the
flight. It can be seen from the Fig. 9 that the position
errors remain small and stable during the simulation.
P matr ix: i.e. the state covariance matrix gives the
information about Kalman filter estimation performance.
Its convergence means that the estimates are close to the
reality. The P matrix of this experiment shows that all the
parameter estimates converge quickly as shown in Fig.
10.
Velocity: Compared with the reference solutions, the
velocity errors depicted in Fig. 11 never exceeds 1% of
the velocity reference profile, indicating that the
estimated velocity values are accurate.
050 100 150 200 250
-2
0
2x 1 0
-5
Lat Err[deg]
Position Errors
050 100 150 200 250
-5
0
5x 1 0
-5
Long Err [deg]
050 100 150 200 250
-2
0
2
Alt Err[m]
Time[s]
Fig. 9. Position errors
Similar to the position and velocity results, the attitude
errors shown in Fig. 12 are small and stable. From the
above results it can be concluded that the simulation test
validates the proposed navigation algorithms.

Li et al.: PC104 Based Low-cost Inertial/GPS Integrated Navigation Platform: Design and Experiments 85
0200 400
0
5000
P POS xyz[m
2
]
0200 400
0
5000
0200 400
0
100
200
0200 400
0
2000
4000
P Vel enu[(m/s)
2
]
0200 400
0
2000
4000
0200 400
0
100
200
0200 400
0
20
40
P Att RPH [deg
2
]
Time [s]0200 400
0
20
40
Time [s]0200 400
0
20
40
Time [s]
Fig. 10. Convergence of Kalman estimation
050100150 200 250
-2
0
2
vel
n
Err[m/s]
Velocity Errors
050100150 200 250
-1
0
1
vel
e
Err[m/s]
050100150 200 250
-1
0
1
vel
u
Err[m/s]
Time [s]
Fig. 11. Velocity errors
050 100 150 200 250
-5
0
5Att it ude Errors
Roll Err[deg]
050 100 150 200 250
-5
0
5
Pitch Err[deg]
050 100 150 200 250
-10
0
10
Heading Err[deg]
Time [s]
Fig. 12. Attitude errors
3.2 Real dat a tes t
The real data experiment was performed to test the
proposed navigation platform using a very low cost
MEMS inertial sensor (MEMSenseTM’s AccelRate3D).
Only analog inertial measurements are available from
AccelRate3D MEMS sensor, which were collected and
converted to digital data in PC104 data acquisition card
(Diamond MM 16 AT). The compiled navigation
software was downloaded to the platform from host
computer through the PC104 Ethernet card. The control
commands and navigation software parameters were
setup by an independent keyboard. Moreover the control
commands and the real-time navigation solution may also
be displayed by the monitor with the aid of PC104
graphic card. The navigation solutions (NovAtel SPAN TM
Best PVA) derived from a tactic grade IMU (Honeywell
HG1700 IMUTM) were employed as the reference. The
inertial devices are shown in Fig. 13 and Fig. 14. IMU
specifications are provided in Table 1 and Table 2
respectively.
Fig. 13. AccelRate3D MEMS inertial sensor
Fig. 14. Reference SPAN IMU
Table 1 MEMS sensor specs (MEMSense AccelRate3D)
AccelRate3D Dynamic
Range Noise
Accelerometer ±2 (g) 35(µg/√Hz)
Angular Rate
Sensor ±300 (º/s) 0.1(º/h/√Hz)
Table 2 Reference IMU (NovAtel SPAN)
SPAN IMU Dynamic
Range
Noise(Random
Walk)
Accelerometer ±50(g) 34(µg/√Hz)
Angular Rate
Sensor ± 1000 (º/s) 0.125(º/√hr)

86 Journal of Global Positioning Systems
First of all, a stand alone test was made with this very
low-cost MEMS sensor. The purpose of this test was to
test its performance without any aiding from GPS. The
trajectory was made inside THE ETS building. Since the
positions are know precisely in the testing corridor, the
reference can be achieved accurately as depicted in Fig.
15.
Fig. 15. Corridor trajectory
The data were processed by the navigation platform, and
the results are shown in Fig. 16. The detailed results are
shown in Table 3.
Due to the MEMS sensor’s high noise characteristics, the
test of 20 seconds is fairly long for MEMS standalone
application. Hence there are significant time-dependent
drifts in the navigation solutions as shown in Fig. 16.
4545.0005 45.00145.0015 45.002 45.0025 45.003 45.0035 45. 00
4
- 73.004
- 73.00 35
- 73.003
- 73.00 25
- 73.002
- 73.00 15
- 73.001
- 73.00 05
-73
- 72.99 95
latitude(d eg)
l ongitude(deg)
MEMS Tra ject ory
Fig. 16. MEMS standalone solution
Table 3 Trajectory vs. MEMS results
Reference MEMS
Total Distance 15.97m 8.30m
Latitude Dist 8.23m 5.32m
Longitude Dist. 13.67m 6.36m
Test Duration 20.36s
0.7932 0.7932 0.7932 0.7932 0.7932 0.7932 0.7932
-1.2848
-1.2848
-1.2848
-1.2848
-1.2848
-1.2848 Trajectory (Latitude-Longitude)
Latitude (rad)
Longitude (rad)
MEMS
SPAN
Fig. 17. Trajectory MEMS vs. SPAN
Second, the integrated navigation test was performed on
the platform. The trajectory was made outside the
building with the good acquisition of GPS signals. The
GPS and MEMS inertial data were acquired and
processed by the PC104 platform and the results were
logged. The reference solutions were calculated in the
laptop computer. The trajectory, i.e. the integrated
MEMS/GPS solution vs. the reference, is depicted in Fig.
17. The duration of the test was 107s. It can be seen that
the MEMS/GPS integration solutions start to diverge
from the reference at the end of the test due to the MEMS
IMU error growing much faster than that of the high
quality tactic grade SPAN IMU sensor.
Compared with the reference solutions, the maximum
position solution errors from the platform shown in Fig.
18 are about 5×10-7 radian (1-2 meters in Cartesian
coordinates).
020 40 60 80100120
-2
0
2x 10
-7 Position Errors ( MEMS Vs SPAN)
Latitude(rad)
020 40 60 80100120
-5
0
5x 10
-7
Longitude(rad)
020 40 60 80100120
-2
0
2
t (s)
Altitude(m)
Fig. 18. Position solution errors
The velocity solution errors are depicted in Fig. 19. The
errors remained small during the test, while the vertical
velocity started to diverge at the end. One of the reasons
causing this divergence is the time dependent biases in
MEMS accelerometer raw measurements. It can be seen
that the raw specific force measurements of the MEMS
IMU are much noisier than those of the tactic grade IMU
as shown in Fig. 20.

Li et al.: PC104 Based Low-cost Inertial/GPS Integrated Navigation Platform: Design and Experiments 87
02040 6080100120
-0.5
0
0.5 Velocity Errors (MEMS Vs SPAN)
East Ve l ( m/s )
02040 6080100120
-0.5
0
0.5
North Vel(m/s)
02040 6080100120
-0.5
0
0.5
t (s)
Up Vel(m/s)
Fig. 19. Velocity solution errors
Compared with the reference solutions, the attitude errors
are 5.7º, 2.9º and 11 º respectively in roll, pitch and
heading angle, as shown in Fig. 21. These errors are
caused by the high noise contaminating the MEMS
angular rate measurements, and more importantly there is
no direct attitude observation available from GPS. The
heading error grows over the time, which is the essential
reason causing the slight divergences in velocity and
position solutions.
010 20 30 40 50 60
-2
0
2Raw Accelerometers data (m/s
2
)
X (m/ s
2
)
010 20 30 40 50 60
-5
0
5
Y (m/s
2
)
010 20 30 40 50 60
-15
-10
-5
time (s)
Z (m/s
2
)
SPAN
MEMS
Fig. 20. Raw specific force measurements MEMS vs. SPAN
020 40 60 80100 120
-0.1
0
0.1 Attitude Errors ( M EMS Vs SPAN)
Roll(rad)
020 40 60 80100 120
-0.05
0
0.05
Pitch(rad)
020 40 60 80100
-0.2
-0.1
0
t (s)
Heading(rad)
Fig. 21 Attitude solution errors
Similarly the raw angular rate measurements from the
MEMS IMU and the reference IMU are depicted in Fig.
22. Although measuring the same angular dynamics, the
raw angular rate measurements of the MEMS IMU are
much noisier than those of the reference IMU. Hence it
can be concluded the MEMS navigation solutions are
greatly improved by the integration of the IMU with
GPS.
010 20 30 4050 60
-0.2
0
0.2 Raw Gyroscopes data (rad/s)
X (rad/s)
010 20 30 4050 60
-0.2
0
0.2
Y (rad/s)
010 20 30 4050 60
-0.5
0
0.5
time (s)
Z (rad/s)
SPAN
MEMS
Fig. 22. Raw angular rate measurements MEMS vs. SPAN
4 Conclusion s
This paper presents the design & experiment for a low
cost inertial/GPS integrated navigation platform based on
PC104 computers. The navigation system software has
been specifically designed in Simulink for low cost
inertial sensor applications. The simulation experiment
validates the proposed hardware and software designs.
The real-time/data test has demonstrated that the PC104
navigation platform can deliver the integrated navigation
solutions comparable to the reference solutions, which
are calculated in the conventional desktop computer
system, whereas with less power consumptions, less
system volume/complexity and much lower over-all
costs. Furthermore the platform hardware is compatible
with various inertial sensors of different grades.
Although there are various advantages in the Simulink
based software design proposed by this study, many
PC104 hardware devices are currently not supported by
the Simulink xPC Target. Moreover the C-programming
based digital mode navigation software comprising
attitude coning and velocity sculling compensation
algorithms is more appropriate for a high performance
navigation system. Hence fully functional C-
programming based platform software is currently under
development. By applying a newly acquired USB-based
digital MEMS nIMU (MEMSenseTM) instead of the
analog AccelRate 3D, a complete digital
hardware/software PC104 navigation platform design

88 Journal of Global Positioning Systems
with a C programming high performance navigation
software will be implemented.
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