Journal of Global Positioning Systems (2004)
Vol. 3, No. 1-2: 191-199
The Advantage of an Integrated RTK-GPS System in Monitoring
Structural Deformation
Xaiojing Li
Schools of Electrical Engineering & Telecommunications, School of Surveying & Spatial Information Systems, The University of New South Wales,
Sydney NSW 2052, Australia
Received: 15 Nov 2004 / Accepted: 3 Feb 2005
Abstract. Monitoring structural response induced by
severe loadings such as typhoon is an efficient way to
mitigate or prevent damage. Because the measured signal
can be used to activate an alarm system to evacuate
people from an endangered building, or to drive a control
system to suppress typhoon excited vibrations so as to
protect the integrity of the structure. A 108m tall tower in
Tokyo has been monitored by an integrated system
combining RTK-GPS and accelerometers. Data collected
by the multi-sensor system have been analysed and
compared to the original finite element modeling (FEM)
result for structural deformation monitoring studies.
Especially, the short time Fast Fourier Transform (FFT)
analysis results have shown that the time-frequency
relation does give us almost instantaneous frequency
response during a typhoon event. In this paper the
feasibility of integrating advanced sensing technologies
such as RTK-GPS with traditional accelerometer sensors,
for structural vibration response and deformation
monitoring under severe loading conditions, is discussed.
The redundancy within the integrated system has shown
robust quality assurance.
Key words: vibration, displacement, deformation,
integrity, structural response
1. Introduction
Civil engineering structures are typically designed based
on the principles of material and structural mechanics.
Finite element model (FEM) analysis and wind tunnel
tests of scaled models are often carried out to assist
structural design (see, e.g., Penman et al, 1999).
However, loading conditions in the real world are always
much more complicated than can be imagined, and hence
key man-made structures must be monitored to ensure
that they maintain integrity of design, construction and
operation.
In general, until recently, monitoring the dynamic
response of civil structures for the purpose of assessment
of damage has relied on measurements from
accelerometer sensors deployed on the structure. Studies
conducted on such data records have been useful in
assessing structural design procedures, improving
building codes and correlating the response of the
structure with the damage caused. However, a double
integration process is required to arrive at the relative
displacements, and there is no way to recover the static or
quasi-static displacement from the acceleration. Also it is
difficult to overcome drift natural to the accelerometer.
Therefore, it has been proposed to integrate an
accelerometer sensor with GPS and other sensors in order
to best utilize the advantageous properties of each.
In contrast to accelerometers, GPS can measure directly
the position coordinates (Parkinson & Spilker, 1996),
hence providing an opportunity to monitor, in real-time
and full scale, the dynamic characteristics of the structure
to which the GPS antennas are attached. In order to
achieve cm-level accuracy, the standard mode of precise
differential GPS positioning locates one reference
receiver at a base station whose three dimensional
coordinates are known, so that the second receiver's
coordinates are determined relative to this reference
receiver. Preliminary studies have proven the technical
feasibility of using GPS to monitor dynamic structural
deformation due to winds, traffic, earthquakes and similar
loading events in, e.g., the UK (Ashkenazi and Roberts,
1997), USA (Kilpatrick et al, 2003), Singapore
(Brownjohn et al, 1998) and Japan (Tamura et al, 2002).
Although GPS offers real-time solutions, it has its own
limitations. For example, GPS can only sample at a rate
of up to 20Hz (Trimble, 2003), although higher rates may
be possible in the future.

192 Journal of Global Positioning Systems
The paper is organized as follows. The second part
presents an analysis of data collected using GPS,
accelerometer and anemometer sensors while monitoring
the responses of a 108m tall steel tower during Typhoon
No. 21 on the first of October 2002. The third part
discusses the necessity of integration of GPS and
accelerometer sensors; and finally the paper concludes
with a summary of the research findings.
2. Deformations Monitored by RTK-GPS and
Accelerometer
2.1 The field monitoring system
As part of the collaborative research between the Tokyo
Polytechnic University and the UNSW, a combined
RTK-GPS and accelerometer system has been deployed
on a 108m steel tower in Tokyo owned by the Japan
Urban Development Corporation, in order to monitor the
tower’s deformation on a continuous basis. At the top of
the tower, a GPS antenna together with accelerometers
and an anemometer were installed. Another GPS antenna
was setup on the top of a 16m high rigid building, as a
reference point 110m away from the tower. In addition,
strain gauges were set in the foundation of the tower to
measure member stresses. Figure 1 illustrates the
experimental setup. Note the local/monitoring coordinate
system has been established so that X is East, Y is North
and Z is pointing to the zenith.
The RTK-GPS and accelerometer data were recorded at
10Hz and 20Hz sampling rates respectively. And the data
was collected from 19:00 to 21:00 Japan Standard Time
(JST) during Typhoon No. 21 on 1 October 2002
(Tamura et al., 2002). Because the weather was cloudy
and rainy the solar heating effect on the steel tower will
be ignored in the analysis of later sections.
It is well known that light, flexible buildings are more
favorable for resisting seismic force, while heavy, stiff
buildings are more favorable for resisting wind force. Tall
buildings in Japan have to satisfy these two opposite
design criteria, and this is one of the most difficult design
issues for tall buildings in Japan. The focus in this paper
will be only on the effects of typhoon on the 108 m
tower.
2.2 Typhoon data set
The overall plots of time series of the RTK-GPS
measured displacements in X, Y and Z directions are
shown in Figure 2. Measurements from the
accelerometers (X and Y directions only) are given in
Figure 4. From the least-squares polynomial fitting (blue
lines) in Figure 2 the maximum displacements in the X
and Y directions are around 5.8cm and -4.5cm
respectively, indicating significant static and quasi-static
movements. However in the Z direction the signal seems
to bounce between 2cm and -2cm. This confirms the less
accurate characteristics of the vertical measurement by
using GPS. Figure 3 are the recorded wind speed and
direction time series. The higher speed corresponds to
larger displacement and stronger acceleration. The
changes of the wind direction agreed with the signs of
displacements in X and Y direction measured by RTK-
GPS. For example, at 20:00 the wind was NW (Figure 9).
Therefore, the displacements on X and Y directions
should be positive and negative respectively and this is
exactly what the GPS has recorded (Figure 2).
Figure 1 The 108m steel tower used for deformation monitoring study.
19:00:00 19:30:00 20:00:00 20:30:00 21:00:00
-2
0
2
4
6
8
X-direction(cm)
19:00:00 19:30:00 20:00:00 20:30:00 21:00:00
-10
-5
0
5
Y-direction(cm)
19:00:00 19:30:00 20:00:00 20:30:00 21:00:00
-5
0
5
Z-direction(cm)
Figure 2 Displacement measured by RTK-GPS.

Li: The Advantage of an Integrated RTK-GPS System in Monitoring Structural Deformation 193
01000 2000 3000 4000 5000 6000 7000
0
10
20
30
m/sec
01000 2000 3000 4000 5000 6000 7000
250
300
350
400
450
Direction
Time (sec)
Figure 3 Wind speed and direction time series.
01000 2000 30004000 5000 6000 7000
-30
-20
-10
0
10
20
30
X-direction (cm/sec2)
01000 2000 30004000 5000 6000 7000
-30
-20
-10
0
10
20
30
Y-direction (cm/sec2)
Time (sec)
Figure 4 Acceleration measured by accelerometer.
2.3 Signals obtained from the typhoon data set
According to a previous study on the basic characteristics
and applicability of RTK-GPS (Tamura et al, 2002), GPS
results seem to follow closely the actual displacement
under conditions when the vibration frequency is lower
than 2Hz, and the vibration amplitude is larger than 2cm.
Consequently it is possible to assess the accuracy of
recorded data by analysing the spectrum of the wind-
induced vibration of the tower. Using the FFT and
zooming in the RTK-GPS spectrum, it is obvious that
there is a 0.57Hz component in the Y direction (blue peak
in Figure 5). However, in the X direction (red) there are
two peaks at 0.54Hz and 0.61Hz. In the 0–0.20Hz range,
the static and quasi-static responses can be clearly
distinguished by comparing with the wind speed
fluctuation spectrum Figure 6. And the noises such as
multipath are contributing to all three directions coupled
in the 0.05-0.2Hz range.
Figure 7 is a zoom-in of the accelerometer spectrum. It
appears to be very clean at the lower frequency end (0 to
0.20Hz). It is obvious also that there is a 0.57Hz
component in the Y direction (green peak). However, in
the X direction (blue peaks) there are two peaks at
0.54Hz and 0.61Hz by further zooming in, which are
identical to what RTK-GPS has picked up. In addition,
there are less significant peaks around 1.86Hz and
2.16Hz for both the X and Y directions, while at 1.86Hz
the X direction is much stronger than the Y. In the
meantime, the overall FFT spectrum gives a peak at
4.56Hz as well (Figure 23).
00.10.2 0.3 0.4 0.50.6
0
1000
2000
3000
4000
5000
6000
7000
8000
Frequency (Hz)
Amplit ude
Spectrum of GPS (Typhoon)
X-d ir ec t i o n
Y-direction
Z-direction
Figure5 Zoom-in of the FFT spectrum of RTK-GPS.
00.2 0.4 0.6 0.811. 2
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5x 104
Frequency (Hz)
Amplitude
Spectrum of Wind Speed change
Figure 6 The wind speed fluctuation spectrum.
According to the anemometer recordings, the wind
direction was mostly North and North-West during the
two hour experiment. Figure 8 gives the mean direction
every 51.2 seconds. North corresponds to the Y-direction
of the monitoring system. Hence, it is useful to focus on
the Y direction (blue in Figure 5).
It is clear that the spectrums for both the accelerometer
and RTK-GPS have the dominant frequency of 0.57Hz,
indicating that it is the lowest natural frequency of the
steel tower. This result agrees with Tamura et al (2002),

194 Journal of Global Positioning Systems
using the power spectral density analysis and the random
decrement (RD) technique through other typhoon event
recording. FEM and frequency domain decomposition
(FDD) analysis has also confirmed that 0.57Hz is the first
mode natural frequency of the tower, 2.16Hz and 4.56Hz
are the 2nd mode and 3rd mode respectively (Figure 9,
after Yoshida et al, 2003). Although it is not clear what
are the driving factors for the other peaks, there is no
doubt that the GPS and accelerometer are complementary
(by comparing Figures 5 and 7).
00.5 11.5 22.5
0
2000
4000
6000
8000
10000
12000
14000
16000
Spectrum of Acc (Typhoon)
Frequency (Hz)
Amplit ude
Accx
Accy
Figure 7 Zoom-in of the FFT spectrum of accelerometer.
19:00:00 19:30:0020:00:00 20:30:00 21:00:00
W
NW
N
NE
Figure 8 Wind direction change in 51.2s mean.
2.4 Wind-induced response of the tower: comparison
between RTK-GPS and accelerometer
Wind-induced response of a structure generally consists
of three components: a static component due to mean
wind force; a quasi-static component caused by the low
frequency wind force fluctuations; and a resonant
component caused by the wind force fluctuation near the
structure’s first mode natural frequency (Tamura, 2003).
Can the integrated GPS and accelerometer system
provide all of the required information? The answer is
probably “yes”. Yet no single sensor can do it alone.
From the signal analysis in Section 2.3, the GPS sensor
gives more information at the low frequency end while
the accelerometer sensor gives more at the high
frequency end. This means that both of them lose
information which is very important for civil engineers.
The following analysis will study this problem in more
detail.
Figure 9 Modes shape obtained by FEM and FDD.
First, a double differentiation procedure is applied to a
segment of RTK-GPS measured displacements data (both
X and Y directions) of 100 second duration in order to
convert them into acceleration. Figures 10 and 12 show
the results of acceleration derived from RTK-GPS
measured displacements, compared to accelerometer-
measured accelerations. In the figures, the upper plots are
the GPS displacements; the middle plots are the
accelerations derived from the GPS measurements; and
the bottom plots are the accelerometer-measured
accelerations. Comparing the time series in the middle
and bottom plots, it can be seen that they agree very well
with each other, although the accelerometer does reveal
more high frequency details, as indicated from the FFT
spectrum analysis previously.
The reverse data processing is also possible. The same
segments of RTK-GPS data (Figure10 and 12) are
compared with the displacements derived from
corresponding accelerometer measurements through a
double integration process. In Figures 11 and 13, the red
and blue are displacements measured by GPS and derived
from accelerometer data respectively. The green and pink
lines are the results of polynomial fitting. From the
results shown in Figures 11 and 13, it can be seen that the
vibrations (dynamic components) can be related to each
other. But it is very hard to compare them quantitatively
because the static and quasi-static displacements are
missing from the accelerometer derived results, since
there is no way to determine the integration constant.
1st Mode2nd Mode 3
rd Mode
f1 = 0.57Hz
Mode shape ratio
Height
f3 = 4.58Hzf2 = 2.18Hz
f1 = 0.57Hzf3 = 4.48Hzf2 = 2.15Hz
FEM FEM FEM
FDD
FDD
FDD
-1 01
H
2
H/3
H/3
0-101 -1 01

Li: The Advantage of an Integrated RTK-GPS System in Monitoring Structural Deformation 195
Note that no intentional offset is used when plotting the
two results.
010 20 30 40 50 60 70 80 90 100
0
2
4
6
8
X-GPS (cm)
010 20 30 40 50 60 70 80 90 100
-20
0
20
Conv Acc (cm/s2)
010 20 30 40 50 60 70 80 90 100
-20
0
20
X-Acc (cm/s2)
Time (sec)
Figure 10 RTK-GPS derived and accelerometer-measured accelerations
(X-direction).
010 20 30 40 50 60 70 80 90 100
-2
-1
0
1
2
3
4
5
6
7
Displacement (cm)
Time (sec)
Measured displacement
Polyfitting
Converted displacement
Polyfitting
Figure 11 RTK-GPS measured and accelerometer derived
displacements (X-direction).
010 2030 4050 60 70 80 90 100
-8
-6
-4
-2
0
Y-GPS (cm)
010 2030 4050 60 70 80 90 100
-20
0
20
Conv Acc (cm/s2)
010 2030 4050 60 70 80 90 100
-20
0
20
Y-Acc (cm/s2)
Time (sec)
Figure 12 RTK-GPS derived and accelerometer-measured accelerations
(Y-direction).
010 20 30 40 50 60 70 80 90 100
-8
-7
-6
-5
-4
-3
-2
-1
0
1
2
Displacement (cm)
Time (sec)
Measured displacement
Polyfitting
Converted displacement
Polyfitting
Figure 13 RTK-GPS measured and accelerometer derived
displacements (Y-direction).
2.5 Time-Frequency analysis
The results of Section 2.3 are obtained by using the
global FFT, whose spectrum represents the frequency
composition of the WHOLE time series. It tells the
relative strength (amplitude) of the frequency
components, but does NOT tell when a particular
frequency component occurs or takes over as the most
significant frequency. It might be very useful to assess
the response of or damage to the structure if it can be
detected when a particular frequency component becomes
dominant. A way of determining this frequency-time
relationship is to apply the FFT to a short segment of the
time series each time, or the so-called short time FFT
analysis.
Consider the measured digital signal of x[n] as a time
series with respect to t. The spectrum X[fa] obtained by
using Fourier transform of a discrete signal (DTFT) is a
function of t, which can be derived as follows. First,
Fourier transform of a discrete signal (Ambikairajah,
2003) is:
[][] ,
jn
n
Xxne
θ
θ
πθπ
∞
−
=−∞
=
−≤≤
∑ (1)
Where, T
ω
θ
=
is the relative frequency, T is sampling
period,
ω
is the frequency in radians.
Considering fa is the frequency of the signal,
and a
f
π
ω
2
=
. The sampling frequency is determined
as
T
fs
1
=. Therefore,
θ
can be given by:
s
a
f
f
πθ 2= (2)

196 Journal of Global Positioning Systems
Then, the time length t of the measurements can be
calculated by using the sampling period T multiple the
numbers of samples n.
NnnTt ≤≤= 0, (3)
Substituting Eq. (1) with (2) and (3), Eq. (1) can be
rewritten as:
∑
∑∑
=
−
=
−
=
−
=
==
N
n
tfj
N
n
nTfj
N
n
f
f
jn
a
a
as
a
enx
enxenxfX
0
2
0
2
0
2
][
][][][
π
π
π
(4)
Hence, from Eq. (4) we can see that the frequency a f of
the digital signal x[n] is a function of time t. When t
approaches zero, the output frequency from DTFT
becomes as the instantaneous frequency. In other words,
by applying DTFT on small samples of measurements,
the frequency- time relationship can be established. The
instantaneous frequency output can be expressed as:
∑
=
−
=
2
1
2
][][
N
Nn
tfj
a
a
enxfX
π
,12 NNn −=∆ & nTt ∆= (5)
By taking the samples n∆ in power of 2, the efficient
algorithm for the short time FFT analysis is obtained.
We can now use Eq. (5) to analyze the tower’s frequency
response with respect to time during this typhoon event.
In order to represent the typical frequency which
appeared in the FFT spectrum, it is important to select
appropriate number of samples of measurements to be
analysed each time, and only the maximum peak to be
taken into account. Figures 14 and 15 are the results for
GPS measurements. A total of 512 samples were
processed each time. Hence the time length is 51.2s. It
can be seen clearly by comparing Figure 16 that when the
wind speed was over 20m/s, the tower’s first mode
natural frequency 0.57Hz became dominant in Y
direction. The peaks of 0.61Hz and 0.54Hz appeared in X
direction at different instants of time. Figure 17 is the
wind speed measurement analyzed by the short time FFT,
given us that the wind force fluctuation between 0.02-
0.05 Hz mostly. This can be used to explain the static and
quasi-static components of the wind induced response of
the tower.
The time-frequency results from the accelerometer
measurements are shown in Figures 18, 19, 20 and 21.
Figures 18 and 19 are analyzed in window length of 128
samples. Therefore the frequency with maximum
amplitude will be registered every 6.4 seconds. In Figure
18, the two frequencies of 2.16 Hz and 1.86Hz which
have been picked up by using global FFT analysis in X
direction appeared at different instants of time as well. It
could be caused by the change of wind speed and
direction. The frequency of 0.57 Hz is the central of the
appeared frequency bandwidth.
Figures 20 and 21 are processed with a window length of
256 samples. These two figures give us more clearly
frequency boundary of the first mode natural vibration of
the tower. Furthermore, it looks like that the tower
responded with the higher mode vibration of small
displacement to show that it is almost still in the weak
wind, i.e. when the wind speed is lower than 10m/s.
19:00:00 19:30:00 20:00:00 20:30:00 21:00:00
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Frequency (GPS-X) (Hz)
Figure 14 Time-Frequency for GPS x-dir analysis.
19:00:00 19:30:00 20:00:00 20:30:00 21:00:00
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Frequency (GPS-Y) (Hz)
Figure 15 Time-Frequency for GPS y-dir analysis.
19:00:00 19:30:0020:00:00 20:30:00 21:00:00
5
10
15
20
25
30
m/s ec
Figure 16 Mean wind speed every 51.2 seconds.

Li: The Advantage of an Integrated RTK-GPS System in Monitoring Structural Deformation 197
19:00:00 19:30:00 20:00:00 20:30:00 21:00:00
0
0.02
0.04
0.06
0.08
0.1
0.12
Frequency (Wind Speed) (Hz)
Figure 17 Time-Frequency for wind speed fluctuation.
19:00:00 19:30:00 20:00:00 20:30:00 21:00:00
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
Frequency (Acc-X) (Hz)
Figure 18 Time-Frequency analysis for Acc x-dir with a
window of 6.4s.
19:00:00 19:30:00 20:00:00 20:30:00 21:00:00
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
2.2
Frequency (Acc-X) (Hz)
Figure 20 Time-Frequency analysis for Acc x-dir with a window of
12.8s.
All these results agree well with the previous global FFT
analysis, but with the timing of events/signals, which
enables cross-correlation with wind speed and direction
data.
19:00:00 19:30:00 20:00:00 20:30:00 21:00:00
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
Frequency (Acc-Y) (Hz)
Figure 19 Time-Frequency analysis for Acc y-dir with a window of
6.4s.
19:00:00 19:30:00 20:00:00 20:30:00 21:00:00
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
2.2
Frequency (Acc-Y) (Hz)
Figure 21 Time-Frequency analysis for Acc y-dir with a window of
12.8s.
It can be seen that in general GPS can pick up signals at
the low frequency end (0-0.2Hz), probably contaminated
by GPS-specific noise such multipath, while it is easier
for the accelerometer to record high frequency signals
(2Hz and above). Therefore, the two sensors are
complementary. On the other hand, the two sensors do
have some overlapping capability in the band between 0.2
– 2Hz, i.e., whatever is picked up by the accelerometer
will also be picked up by the RTK-GPS. This overlap
provides redundancy within the integrated system which
enables robust quality assurance (i.e., double integration
and differentiation as shown in Section 2) and the
overlapping band is likely to increase with the
advancement of GPS receiver technology.
Note currently the RTK-GPS data are collected using the
single-base RTK approach. It is possible to enhance the
RTK performance by employing network RTK,
especially applicable in Japan considering the available
nation-wide, very dense, and continuous GPS (CGPS)
network.

198 Journal of Global Positioning Systems
00.5 11.5 22.5 33.5 44.5 5
0
0.5
1
1.5
2
2.5
3x 104
Frequency (Hz)
Amplitude
Spectrum of GPS (Typhoon)
X- di r ec t i o n
Y-direction
Z-direction
Figure 22 Overall FFT spectrums of GPS measurements during the
typhoon.
0 12 3 45 6 7 8910
0
0.5
1
1.5
2
2.5
3
3.5
4x 104Spectrum of Acc (Typhoon)
Frequency (Hz)
Amplitude
Accx
Accy
Figure 23 Overall FFT spectrums of accelerometer measurements
during the typhoon.
The limitation of single-base GPS-RTK is the distance
between the reference receiver and the user receiver so as
to ensure that distance-dependent biases such as orbit
error, and ionospheric and tropospheric signal refraction,
can cancel. This has restricted the inter-receiver distance
to 10km or less if very rapid ambiguity resolution is
desired (i.e. less than a few seconds). Moreover, in dense
urban environments the identification of a site for a
reference station can be especially challenging. The
reference station must have a clear view of the sky in
order to track the satellites, be in close proximity to the
user to minimize baseline separation errors, be relatively
multipath free, and be on a stable platform. In most cases,
rigid structures are low rise and often overshadowed by
taller neighbouring buildings, making their ability to
track satellites highly variable.
In network-RTK there is a data processing ‘engine’ with
the capability to resolve the integer ambiguities between
the static reference receivers that make up the CGPS
network. The ‘engine’ is capable of handling data from
receivers 50-100km apart, operates in real-time,
instantaneously for all satellites at elevation cut-off
angles down to a couple of degrees (even with high noise
data that are vulnerable to a higher multipath
disturbance). The network-RTK correction messages can
then be generated.
Because there will be several reference stations available
in the 100km radius of the user receiver, there are several
advantages of network-RTK over the standard single-
base RTK configuration (Rizos, 2002):
• No need for the user to operate his own
reference station (cost reduction).
 Elimination of orbit bias and ionosphere delay.
• Better reduction of troposphere delay, multipath
disturbance and observation noise.
• RTK can be extended to what might be
considered ‘medium-range’ baselines
(up100km).
• Low-cost single-frequency receivers can be used
for RTK (further cost reduction).
• Improve the accuracy, reliability, integrity,
productivity and capacity of GPS positioning.
Therefore, network-based GPS-RTK has been proposed
as a key component for the future integrated system. It
will be tested on the 108m steel tower currently
monitored using the single-base GPS-RTK.
4. Concluding Remarks
GPS and accelerometer sensors have been installed on a
108m tall steel tower and data have been collected at
10Hz and 20Hz during a typhoon on 1 October 2002. The
wind induced deformation has been analysed in both the
time and frequency domains. In the frequency domain,
both the GPS and accelerometer results show strong
peaks at 0.57Hz, which agrees with previous studies by
using different methods, although GPS measurements are
noisy in the low frequency end and accelerometer
measurements at the high frequency end. Measurements
have been converted to displacement (in the case of
accelerometer) and acceleration (in the case of GPS)
through double integration and double differentiation
respectively, for the purpose of direct comparison. The
results agree with each other very well, except that the
static component is missing from the accelerometer
derived results.
Measurements from GPS, accelerometer and anemometer
have been analysed in detail using short-time FFT. The

Li: The Advantage of an Integrated RTK-GPS System in Monitoring Structural Deformation 199
spectrum features have been well explained using such an
approach.
The benefits of an integrated system of GPS and
accelerometer sensors to monitor the structural
deformation have been highlighted.
Acknowledgements
The author wishes to thanks several members of the
Satellite Navigation And Positioning Group (SNAP) and
the Photonics and Optical Communications Group
(POCG) at UNSW for useful discussions and help.
Special thanks to the UNSW Faculty of Engineering for
the scholarship supporting the author’s PhD studies under
the joint supervision of the Schools of Surveying and
Spatial Information Systems, and Electrical Engineering
and Telecommunications. This work would not have been
possible without this joint supervision. The research is
also sponsored by a Faculty Research Grant of the
UNSW.
The collaboration of Professor Yukio Tamura and Mr
Akihito Yoshida from the Tokyo Polytechnic University
has been invaluable for these studies.
The author has benefited enormously from Associate
Professor Ambikairajah’s course on Digital signal
processing.
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