RAMADHAN, HANI (2016) SENSOR FUSION AND TEMPORAL INTEGRATION FOR TOUCH INTERFACE INDOOR POSITIONING. Masters thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Dalam kunjungan wisata atau budaya, panduan terhadap objek menarik sangat
berguna untuk menambah pengetahuan dan pengalaman pengunjung di
lokasi tersebut. Dewasa ini, dengan bantuan teknologi modern, aplikasi bergerak
mampu menjadi pemandu wisata mandiri otomatis dengan sistem sadar
konteks. Kebanyakan, unsur konteks yang digunakan dalam aplikasi-aplikasi
ini adalah posisi dua dimensi (2D). Meskipun begitu, ada beberapa kemungkinan
lain agar tiap unsur konteks dari perangkat pintar ini dapat diteliti lebih
lanjut.
Berkat sensor dari ponsel pintar, konteks-konteks tersebut, yang terdiri dari
konteks dalam 3 dimensi (3D) dari posisi dan orientasi (dalam sumbu X, Y,
dan Z), dapat ditangkap oleh ponsel pintar. Dimensi-dimensi ini akan diteliti
untuk mendapatkan kemungkinan keberhasilan digunakannya ponsel pintar
yang digenggam sebagai pointer terhadap objek menarik. Hal ini dilakukan
karena posisi 2D tidak bisa menangani konteks ketinggian. Sehingga, pengalaman
pengguna dapat ditingkatkan karena mereka tidak terhalang secara
visual dan audio. Tetapi, sensor-sensor ini memiliki galat pengukuran yang
tinggi. Sehingga, suatu penggabungan sensor diterapkan untuk menangani
galat tersebut.
Penelitian ini menerapkan metode untuk memperkirakan orientasi sudut
dan posisi dengan berbagai filter, yakni Complementary Filter dan Kalman
Filter. Complementary Filter melibatkan gyroscope, magnetometer, dan accelerometer
dari sensor inersial ponsel pintar. Sedangkan, Kalman Filter melibatkan
accelerometer dan hasil Wi-Fi fingerprinting yang didapatkan dari
pengamatan lingkungan. Evaluasi perkiraan-perkiraan hasil penggabungan
observasi sensor oleh filter-filter tersebut menggunakan ilustrasi grafis dan
evaluasi statistika untuk mengukur kualitas reduksi galat dari tiap filter.
Hasil dari performa filter menunjukkan bahwa kualitas perkiraan orientasi
oleh Complementary Filter cukup baik untuk menghasilkan sudut yang sesuai.
Namun, perkiraan posisi oleh Kalman Filter menunjukkan hasil yang kurang
baik akibat integrasi ganda terhadap derau dan pengaruh besar Wi-Fi fingerprinting.
Hasil Wi-Fi fingerprinting menunjukkan perkiraan posisi yang tidak
akurat. Hal ini menunjukkan bahwa perkiraan posisi tidak dapat digunakan
dalam penelitian ini. Sedangkan, dalam percobaan menunjuk objek di laboratorium,
perkiraan orientasi sudut memberikan hasl yang cukup baik dengan
ponsel pintar.
Secara ringkas, perkiraan posisi dan orientasi 3D dengan Complementary
Filter dan Kalman Filter dalam ponsel untuk pointer tidak dapat digunakan menurut penelitian ini. Meskipun begitu, masih perlu diteliti mengenai penerapan
filter lainnya untuk perkiraan posisi dan observasi lain untuk membantu
perkiraan yang baik. Walaupun penggunaan filter dan observasi lain dapat
mengorbankan sumber daya dari ponsel pintar.
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During cultural or tourism visits, a guide of the interesting objects is useful to
enhance the knowledge and the experience of the visitors. Nowadays, because
of the modern technologies, mobile applications are capable to be a personal
autonomous guide in the case of context-aware system. Mostly, the context
element used in these applications is the position in two dimension (2D). However,
there are more possibilities using the context elements from smartphone
that can be explored.
Thanks to smartphone sensors, the contexts which can be captured by
smartphone are composed in 3 dimensions (3D) of both position and orientation
(in X, Y, and Z axes). Those dimensions are used to explore the feasibility
of smartphone which can held by hand as pointer to interesting objects, which
can’t be handled by 2D position only. Thus, the user experience can be enhanced,
as they don’t get vision-blocked or audio-blocked. However, those
sensors have erroneous measurements. Hence, a sensor fusion is applied to
overcome this drawback.
The sensor fusion can be implemented not only using the internal smartphone
sensors, but also the external environment. In this case of indoor environment,
the Wi-Fi fingerprinting approach, which widely used as indoor
positioning algorithm, can be considered as external observation. Even though
so, the quality of the fusion should be studied to assure that it is feasible to
use smartphone a pointing device in indoor environment.
This study proposed a method to estimate orientation and position using
different filters, namely Complementary Filter and Kalman Filter respectively.
The complementary filter involves the gyroscope, magnetometer, and
accelerometer from the smartphone inertial navigation sensors, while the Kalman
Filter involves accelerometer and the Wi-Fi fingerprinting result which come
from environmental measurement. To evaluate these estimations, the graphical
representation and statistical evaluation are used to measure the filters’
quality in reducing the errors.
The results of the filters’ performance showed that orientation estimation
was adequate to give acceptable angle. But, unfortunately, position estimation
had resulted in poor performance because of the double integration toward
noise and the heavy influence from Wi-Fi fingerprinting. The Wi-Fi fingerprinting
resulted inaccurate positioning. This concluded that the position
estimation cannot be used at all in this study. In laboratory object pointing
field experiment, the orientation estimation gave passable estimation to locate
an object by a fixed smartphone position. To sum up, the 3D position and orientation estimation using Complementary
Filter and Kalman Filter might not be feasible according to this study.
However, regarding to 3D position estimation, possibly there are other methods
than Kalman Filter which might be used as state estimator. And also,
there are various external measurements which might help to achieve better
estimation. Although, the drawbacks between the more sophisticated methods
and the computation power and capability of smartphone should be considered
for a good user experience.
| Item Type: | Thesis (Masters) |
|---|---|
| Uncontrolled Keywords: | Context-aware systems, Indoor positioning, Wi-Fi fingerprinting, sensor fusion |
| Subjects: | T Technology > T Technology (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5105 Data Transmission Systems |
| Divisions: | Faculty of Information Technology > Informatics Engineering > 55101-(S2) Master Thesis |
| Depositing User: | Mr. Fandika aqsa |
| Date Deposited: | 09 Jan 2017 02:09 |
| Last Modified: | 27 Dec 2018 06:19 |
| URI: | http://repository.its.ac.id/id/eprint/1384 |
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