Real-Time Monitoring of Structure Movements
Using Low-Cost, Wall-Mounted, Hand-held
RTK-GNSS Receivers
So Takahashi, Tokyo University of Marine Science and Technology
Nobuaki Kubo, Tokyo University of Marine Science and Technology
Norihiro Yamaguchi, Shimizu Corporation
Takahashi Yokoshima, Shimizu Corporation

BIOGRAPHY (IES)
Mr. So Takahashi received his Bachelor of Engineering degree in Logistics and Information Engineering from Tokyo
University of Marine Science and Technology, Tokyo, Japan, in 2017. He is now a graduate student at Tokyo University ofMarine Science and Technology. His research interest is low-cost RTK-GNSS receivers.
ABSTRACT
It is useful and innovative to monitor the displacement of structures caused by seismic shaking and landslides irrespective ofobservation points by using a single piece of equipment to collect data for some construction companies, research institutes andso on. Global Navigation Satellite System (GNSS) receivers and antenna on the roof of a steel tower to monitor the
displacement of a structure (caused by wind, other natural elements, etc.) have already been reported in Japan, but our study isthe first study on low-cost GNSS systems that can be attached to the walls of a building to monitor movement. Specifically,
this research focuses on the real-time monitoring of structure movement using wall-mounted, hand-held Real-Time Kinematic
GNSS (RTK-GNSS) receivers. Generally, it is not easy to set the antenna on the roof of buildings, so this approach attempting
to attach the antenna to the wall has an advantage of the aspect of the readily installing. RTK positioning is normally
performed using expensive, dual-frequency, geodetic-level receivers and antennas because it is commonly used in surveying
world. However, aside from its high cost, this type of equipment is not easy to procure. This study addresses this problem
using a low-cost, single-frequency, consumer-level receiver, which produces comparable centimeter-level results. The methods
presented use horizontal spread and statistical values to determine positioning results. Our work on the proposed monitoring
system, with its robust RTK improvement method, will serve as a reference for monitoring the fluctuations and movements ofstructures in the fields of architecture, construction and so on.
I. INTRODUCTION
The need for accurately identifying locations is increasing because of the advent of a variety of smartphone applications thatuse location information. Multi- Global Navigation Satellite System (GNSS) positioning is now possible owing to progress inthe deployment of positioning satellites and the high quality of firmware supplied by GNSS receiver manufacturers around theworld. Therefore, real-time kinematic (RTK) positioning in open-sky environments (without structural barriers that affectpositioning) is no longer a problem. However, it is also well known that the presence of urban buildings, trees and otherobstacles decreases the performance of RTK positioning markedly in that area, which is a problem that needs to be addressed.
In open-sky environments, RTK positioning is often used for surveying on construction sites owing to its high performance.
The Ministry of Land, Infrastructure, Transport and Tourism in Japan launched a project called “i-Construction” thatencourages using and adapting ICT in its entirety on construction sites. However, dual-frequency receivers that can be used insuch situations are expensive; therefore, in the future, systems will emerge that use single-frequency low-cost handheld
receivers that are easy to produce and operate.
In this research, we affixed a low-cost, handheld GNSS receiver’s patch antenna to the wall of a building in a suboptimal
environment (i.e., partially shielded by trees or other structures). RTK positioning was performed with the antenna, which
received the radio waves from the satellite and was perpendicular to the surface of the wall. This innovative system, which weare the first to propose, monitors a building’s shaking using centimeter-level information from positioning epochs. In Japan,

31st International Technical Meeting of the Satellite Division of the Instituteof Navigation (ION GNSS+ 2018), Miami, Florida, September 24-28, 20182074

https://doi.org/10.33012/2018.16021

which is a highly seismic country, the above system, which can detect the shaking of a building, is extremely useful. This study
presents a system that can detect fluctuations in existing structures, as well as in buildings that are under construction, andimportant structural measurements can be obtained from the data. In contrast, the conventional method requires expensive
measuring instruments and many employees to measure the movements of buildings. Our proposed method requires only threeelements: power supply (lithium-ion mobile battery, PC, etc.), a handheld receiver, and an antenna (shown below in Fig. 1).
The equipment is installed on the wall of a building where measurements need to be taken. The logged observational data isanalyzed using software, which has considerable advantages in terms of personnel and cost. In the case of real-time application,
we just put 3G SIM card and small computer which is Raspberry Pi. We have already developed them and the raw GNSS dataof GNSS receiver installed on the surface of the wall is automatically transmitted to our PC server including RTK software.
Several challenges exist in developing the above system. First, as it is a single-frequency receiver, the number of satellites
for RTK positioning is sometimes severely restricted. Therefore, it is very important to know if the number of satellites
required will be available (due to limited sky view). Factors that significantly influence performance include the time zone
with optimal satellite arrangement (BeiDou satellites in China tend to concentrate from the northeast to the southwest in thesky above Tokyo), dilution of precision caused by the geometrical arrangement of satellites, and the direction of the place
where the experiment is conducted in relation to the above factors. Moreover, compared to that of dual-frequency receivers the
RTK position information obtained by single-frequency receivers is low in terms of reliability and availability, so technologies
for improving these metrics are also important.
Such systems will be easier to handle if the existence of the structures that shield radio wave propagation paths (hence
deteriorate the surrounding environment) are known in advance. One way to cope with such environmental obstacles is to
optimally apply the elevation mask and make it easy to receive more than 10 direct signals. With this technique, the influenceof diffracted wave signals and reflected wave signals (multipath waves) from non-line-of-sight satellites can be reduced, andepochs obtained at the centimeter levels will increase. In existing research, the “GPS-Studio” (by KOZO KEIKAKUENGINEERING Inc., Japan), which is a radio-wave propagation simulation, uses both the ray tracing and 3D map methods toprovide precise positions of buildings, but it has not been widely used practically yet. Accumulation of these kinds ofenvironmental data over time provides an advanced arbitrary measurement using AI.
The easy-to-obtain around low-cost ($100) handheld receiver can achieve an accuracy of a few centimeters at manymeasurement points, and it is possible to introduce several receivers at many places. In this paper, we propose a low-cost RTK
system, which has not been designed earlier, as a monitoring system for building shaking, intended for practical applications.
Moreover, previous studies such as the monitoring of a steel tower’s displacements caused by the wind using RTK
positioning [1][2][3] and the monitoring of a landslide caused by rain on the land near the river [4][5] do not employ thesesystems in an extreme environment. The aim of this research is to implement the RTK monitoring system in this type ofenvironment to test its efficiency and accuracy.

Fig. 1. Low-Cost Rover Equipment
II. RTKLIB AND ROBUST RTK METHOD
Before we discuss about algorithm of RTK, it is quite important to discuss about satellite selection to remove the bad quality
carrier-phase. First of all, we check LLI flag of the output from u-blox receiver to eliminate bad quality carrier-phase causing
cycle-slip and so on. Generally, when the antenna is installed on the surface of the wall, the sky view is obstructed in half by
the structure, so the influence of the diffracted wave from NLOS satellites should be taken into consideration in particular. On
the other hand, the reflected wave can be ignored as there is almost no influence of the code/carrier phase multipath because2075

the delay of the multipath is almost zero. We still need to check the effect of carrier phase multipath carefully because there isa few cm distance between the wall and antenna. Although the effect of DOP value may increase significantly but we just needto set the threshold of DOP according to our expected accuracy. Previous studies have shown that some diffracted signals fromNLOS satellites can be almost completely eliminated using the picture of fisheyes lens [6]. Based on this satellite selection
method, as a result, RTK performance dramatically improves using the standard method mentioned in the above. Fig. 2 shows
the picture of the fisheyes lens and the transition of SNR (PRN24 of GPS). It can be confirmed that we need to adopt
appropriate SNR mask to make RTK successful. Based on a lot of test data using u-blox M8 series, we found the suitable
threshold of SNR which is 35 dB-Hz.
In the concept of normal installation of the antenna, the most common way is to set antenna upward. However, for thisresearch, we did an initial test to check the performance according to the degree pattern of 0°, 45°, and 90° with respect to theground (Fig. 3). From these results, we adopted the 0° orientation since the RTK performance was the best.

Fig. 2. Pictures of Fisheyes Lens (southwest) and Transition of SNR (PRN24 of GPS)

Fig. 3. Initial Experiment (0°, 45°, and 90°)

A. Using RTKLIB
To process the raw data logged by the receiver, we used RTKLIB (ver. 2.4.3 b29), which is an internationally famous, free
software, developed by Mr. Tomoji Takasu who is a visiting researcher at our university. The results of this experiment
seemed to be best when adjusting “Options” for RTKPOST, which is a post-processing function in RTKLIB; the improved
results from our CUI post-processing software using Microsoft Visual Studio incorporating the proposed method.
Once we had the raw data, we had to search for the best “Options” while manually changing some parameters. This method
will not be needed unless we employ the use of skyward angular range of view using fisheyes lens. Normally this process
would be a difficult and laborious task. However, we do not have to change some parameters when using our proposed method.

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Therefore, the proposed method has an advantage in this part. The below settings (Fig. 4 – Fig. 7) are for the southwest
antenna, which will be discussed in Sections III and IV.

Fig. 4. Setting1

Fig. 5. SNR Mask

Fig. 6. Setting2

Fig. 7. Stats

Detailed setting in “Options” of RTKPOST is shown (in above Fig. 4 – Fig. 7). Currently, favorable results are obtainedusing these settings. The results of RTKLIB are presented in Section IV.
B. Using Robust RTK method
This section briefly introduces the proposed RTK method under severe environments. The most part is similar to the normal
algorithm of RTK which means that we use LAMBDA method to resolve correct integer ambiguities and ratio test to enhance
the reliability of RTK fixed solutions.
(a) Float Solution and Ambiguity Resolution
The RTK-GNSS uses a double difference (DD) code and phase observation. The DD measurements efficiently cancel thereceiver and satellite-dependent biases. In addition, the atmospheric reflection errors that occur when two receivers are close
are so similar that they can be eliminated. The DD observation models can be written as follows [7]:(1)

DDP = ( Psv1ref – Psv2ref ) – (Psv1rov – Psv2rov33) = ρcode + εcode(2)

DDφ = (φsv1ref – φsv2ref ) – (φsv1rov – φsv2rov) = ρphase + λN + εphase

where P and φ are the code and phase observations, respectively, ρ is the DD geometric distance, λ is the wavelength, N is the
DD integer ambiguity, and ε is the DD measurement noise including the multipath error.
First, the DD observations are used to determine the 3D position solution (i.e., float solution) and real number ambiguity
solutions at the same time. The details of the estimation of the float solutions can be referred to part (c) in the following. Then,the ambiguity resolution (AR) method is used to optimize the integer ambiguity solution. The AR method searches for the
integer ambiguity that minimizes the cost function, which is given by(3)

C (N) = (N – N^)TWN(N – N^)

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where N is the minimized integer ambiguity in the cost function, N^ is the float solution ambiguities, and W N is the inverse of
its covariance matrix.
The LAMBDA method is a well-known and efficient integer AR method based on the least-squares method [8] and wasused to determine the optimal integer ambiguities.
(b) Ambiguity Validation Test
A test was performed to validate the integer ambiguities determined by the LAMBDA method. Several tests are available for
ambiguity validation [9]. A ratio test based on integer least-squares is a clean and effective method to vilification tests. In thisstudy, the ratio test with a fixed value is used for short base line RTK. According to the ratio test, the ratio of the differencesfrom float ambiguity solution residuals (computed by the cost function) is compared.
The ratio value of the ratio test is defined as(4)

Ratio = C(N2) / C(N1) > Thresholdwhere C(N1) is the cost function value of the best integer vector and C(N 2) is the cost function value of the second-best integer
vector. If this ratio is large enough, these ambiguities are acceptable. The threshold value is determined from the empiricalresults. The ratio test threshold is usually 2–3 depending on the environment. In this study, the ratio test threshold was set to 3.
(c) Robust RTK under severe condition
We need to modify the algorithm to find correct ambiguities to enhance the RTK performance under severe conditions. In
addition to that, selecting high-quality satellites is important. Generally, Kalman-filter-based RTK has been used for thispurpose because it can reduce the noise of float solutions, and it uses signal strength-based satellite selection. We use a similarsatellite selection method according to the suitable threshold of the expected signal strength. However, we do not use theKalman filter to reduce the error and noise of float solutions. In some applications such as determination of the structuraldeformation of the building, the movement of the structure is very slow and small. Even in the case of a bridge, the amount of
displacement could be less than 10 cm per day. In such situations, we can tighten antenna movement constraint according to
the dynamics of the target position. Furthermore, the initial position of the target is very important, and we can know the
precise target position beforehand. Even without previous target position information, once we resolve the reliable precise
position, we will use this information as an initial position. The difficulty in RTK under severe conditions is that large code
multipath errors deteriorates the float solutions, as a result, RTK fix rate as well as reliability of RTK decreases. If we know
the approximate precise position within 10 cm, we don’t have to use code measurements to resolve the carrier phaseambiguities. By doing so, the performance of RTK-GNSS under very severe conditions, such as almost half masking of the sky,
is improved dramatically, even when compared to the RTK engine of geodetic-level GNSS receiver that cost over $10,000.
In parallel with the above robust RTK, resolved ambiguities of each satellite are saved and held. Based on the cycle slip
information of the carrier phase, the RTK positions are produced using the saved ambiguities separately. Two RTK positions
are checked weather they match within about 10 cm to enhance the reliability. Furthermore, we rely on the RTK positions
deduced from the saved ambiguities in the case of large deformation due to big earthquake that the speed of the deformation
exceeds over a few cm/s as long as we have enough satellites without cycle slips. If we don’t have usable satellites at least 5-6,
it is impossible to estimate the position. The reason why we don’t use the above robust RTK solutions is that it does not cover
the movement over approximately 20 cm in total because of the limitation of the ambiguity search space. In reality, thefluctuation of the building in big earthquake exceeds over 50 cm on higher floors. The brief flowchart of our proposed method
is shown in Fig. 8.

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Fig. 8. Flowchart of Our Proposed Method
III. EXPERIMENTS
Over several days during November 2017, we conducted the positioning experiments mentioned in Section I, on various
walls of a building at Tokyo University of Marine Science and Technology for about 24 h and 23 h. The equipment used in theexperiment, installation location, and installation status are given below (Table 1, Fig. 9 – Fig. 13).

Table 1. Equipment Used

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Fig. 9. Installation Location

Fig. 10. Installation Status (southwest)

Fig. 11. Installation Status (southeast)

Fig. 12. Installation Status (northwest)

Fig. 13. Installation Status (northeast)

This experiment makes use of Trimble Inc., (an expensive antenna) for reference, but it is possible to replace the antenna
with a patch antenna to minimize the cost of the project without performance deterioration. To investigate building movement,
it is desirable to analyze data spanning months or years. However, for this experiment, we processed 24 h and 23 h of data.
Post-processing of RTK positioning was performed using observation data at the rover station obtained by positioning, the2080

observation data from the reference station, and the navigation message. We will investigate long-term data over one month in
the near future.
IV. RESULTS AND EVALUATION(i) Results of the Proposed Method
The experimental results are presented in the following tables and graphs: (Table 2 – Table 5, Fig. 14 – Fig. 17). The
standard deviation and average represent the two-dimensional horizontal gap from the true value, and the epoch number
represents the number of times RTK positioning was possible during 24 h (southwest) or 23 h (excluding southwest). The total
number of epochs was 86,400 in the southwest, and 82,800 in all other cases. However, as it is important to judge whether thesolution determined as the “fixed state” is a really correct solution, the reliability (%) is also included in these tables.
The reliability of RTK is defined as follows:
Reliability (%) = (Number of Reliable solutions / Number of Fix solutions) × 100
RTK Fix rate is defined as follows:
RTK Fix rate (%) = (Number of Fix solutions / Number of all epochs) × 100

Table 2. Southwest Results

Table 3. Southeast Results

Table 4. Northwest Results

Table 5. Northeast Results2081

Fig. 14. Horizontal Results (southwest)

Fig. 15. Horizontal Results (southeast)

Fig. 16. Horizontal Results (northwest)

Fig. 17. Horizontal Results (northeast)

In any direction of these tests, the results from the proposed method were better than those of RTKLIB. The number of
epochs that have the RTK flag set also increased. As for the reliability of the southwest and southeast results, which had arelatively good satellite constellation because of several BeiDou satellites, the horizontal results (within ±10 cm of the true
value) can be obtained with high reliability at a rate of more than 99%. As can be seen from the horizontal results, as thesatellite constellation and direction in which radio waves can be received were biased, RTK fixed solutions were elongated in
the direction perpendicular to the surface of the wall. However, in the northeast, RTK fix rate was lower than the other threecases, simply because of the shortage of the number of usable satellites. With more visible satellites soon becoming
available—like QZSS and Galileo—and the appearance of low-cost, dual-frequency receivers, performance will improve2082

further. On the other hand, determining the position of northeast using the RTKLIB has been difficult due to limitations in lackof satellites. As a workaround, we used SPS855 receiver of Trimble Inc. to get the precise position. This confirms that theaccuracy of the proposed method is relatively better than the result of RTKLIB, regardless of the fact that the RTK fix rate ofproposed method is lower than RTKLIB.
Based on these test results, it is possible to detect a vibration of at least 3–4 cm of a wall in the case of the southern direction.
In high-rise buildings, the walls vibrate even more, and when an earthquake occurs, large shakes can be detected, and it ispossible to precisely determine the occurrence time and amplitude of shaking. This information is very important for
construction companies. Almost all RTK fixed solutions are within 10cm in horizontal. This can be very useful for detection oflong term transition of positions and movement of buildings if the day by day average is taken from those data. However, there
is still a need to adapt an algorithm that can deal with a big seismic shakings such as a few decimeters moving in the near
future.
(ii) RTK Performance of Commercial Receivers on the Wall
To investigate the performance of the low-cost commercial receivers with RTK, we obtained 1 hour data of NEO-M8P with
the newest firmware (u-blox), which can obtain the receiver’s calculated output (NMEA-format) of low-cost RTK andcompare it to the corresponding data obtained with the proposed method. Sampling rate was 5 Hz and the installation point was
southeast. Horizontal results are shown below (Fig. 18).

Fig. 18. Horizontal Result (u-blox NEO-M8P)

Fig. 19. Quality Flag

Based on the results, NEO-M8P could not properly obtain RTK fixed solutions in this environment. In addition, Fig. 19
shows the quality flag in a time series. It turned out that there were almost no RTK fixed solutions, and most of the solutions
were float solutions; hence, in this kind of severe environment, RTK positioning of NEO-M8P was not good. Therefore, theeffect of the proposed method was also confirmed from this experiment.
In addition to the above test, the geodetic-level GNSS receiver SPS855 manufactured by Trimble Inc. was also checked. We
obtained 1 hour data on the southwest wall and sampling rate was 10 Hz. Horizontal and height results are shown below (Fig.
20, Fig. 21).

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Fig. 20. Horizontal Result (Trimble SPS855)

Fig. 21. Height Result (Trimble SPS855)

Based on the test results, there were no RTK fixed solutions in this environment using SPS855. Red plots are standalone, blue
plots are DGNSS, and yellow plots are RTK float in Fig. 20 and Fig. 21. Therefore, even two very famous companies of GNSS
receiver, they do not support these severe situations, hence the effect of the proposed method was also confirmed from thisexperiment. We actually don’t think that these commercial receivers are not good. We can say that it is quite important tocustomize the RTK software to achieve the better performance in the very severe condition.
(iii) Further Experiments
Furthermore, the following horizontal results were those obtained when the antenna was manually moved on the southwest
wall’s surface to simulate shaking. As it is difficult to perform positioning experiments involving considerable outdoor
shakings, we decided to manually move the antenna very close to the wall. It was moved in the lateral and vertical direction
with respect to the wall’s surface, every minute, from left to right and from top to bottom. The installation status as shown
below (Fig. 22).

Fig. 22. Box for a Patch Antenna

Fig. 23. Horizontal Result (southwest)

Fig. 24. Height Result (southwest)

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The above figures (Fig. 23, Fig. 24) were the results of sampling at 5Hz, and it is assumed that it was shaken similar to when
an earthquake occurs, in a short time and moving approximately 10 cm from the true position at a constant speed from left toright. The elongation of the solution shows that RTK fixed solutions can firmly follow the direction of the antenna. RTK fixed
rate was almost 100%. Even if an external dynamic force works under such circumstances, it can detect several centimeters ofmotion, so it was found that it is a method that can be used for monitoring.
Moreover, following two results (Fig. 27, Fig. 28) were also obtained from a similar experiment considering a very big
earthquake with a magnitude of 5 or more analyzed by RTKLIB and the proposed method. The antenna with a pole was moved
approximately 40 cm from the true position at a slow and fast speed from left to right. The pole used in the experiment andinstallation status are given below (Fig. 25, Fig. 26).

Fig. 25. Pole Used in the ExperimentFig. 26. Installation Status (southwest)

Fig. 27. Result of RTKLIB (southwest)

Fig. 28. Result of the Proposed Method (southwest)

There are some float solutions and wrong RTK fixed solutions in the result of RTKLIB, on the other hand, 100% RTK fixedsolutions were obtained by the left part of the flowchart of the proposed method shown in Fig. 8.
In conclusion, from the above results, in an environment that satisfies conditions such as the number of satellites and thesurroundings not being completely closed by buildings, it is possible to monitor the shakings of buildings inexpensively using
the proposed method.
V. SUMMARY AND FUTURE SUBJECTSIn this paper, we proposed a method for structure movement detection with a low-cost, handheld RTK in severe
environments. Although only one building at Tokyo University of Marine Science and Technology was shown as an example,
we plan to include the following in our research: experimental results of low-cost handheld receivers on other buildings andskyscrapers in other areas, and methods to obtain more accurate positions, and applications in real-world scenarios.

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RTK fix rate was as high as 90% or more in the southwest, southeast and northwest, which can be considered a practicallevel. Regarding the northeast wall, if the antenna is surrounded by buildings, securing a sufficient number of satellites isdifficult. However, as the availability increases owing to the soon-increasing number of usable satellites, it is expected that
performance will improve regardless of the direction.
Currently, as a post-processing step of RTK, “forward” (which is processing from the front in time series), is regarded as
real-time performance. We need to develop the real-time software and it is necessary to obtain long-term data (taken over
several months) to put this system into practical use.
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[4] M. S. Rawat, Varun Joshi, B. S. Rawat, and K. Kumar “Landslide movement monitoring using GPS technology: A casestudy of Bakthang landslide, Gangtok. East Sikkim, India” Journal of Development and Agricultural Economics Vol. 3(5),pp. 194-200, May 2011[5] Toyomitu Ezaki, Mutumi Fujimoto, Kouji Kawanishi, Shinichi Sawada, and Moriyoshi Okamoto “Observation ofLandslide Movement by GPS Realtime Kinematic Survey”[6] Hiroko Tokura, Nobuaki Kubo “Effective Satellite Selection Methods for RTK-GNSS NLOS Exclusion in Dense Urban
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[8] Teunissen PJG “The least-squares ambiguity decorrelation adjustment: a method for fast GPS integer ambiguity
estimation” Journal of Geodesy 70.1-2.1995[9] Verhagen, S. “The GNSS integer ambiguities: estimation and validation” TU Delft, Delft University of Technology, 2005.

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