PENENTUAN POSISI OBJEK BERDASARKAN GLOBAL SYSTEM FOR MOBILE COMMUNICATION (GSM) MENGGUNAKAN METODE NA�VE BAYES
Most researches in indoor localization are based on the use of short-range signals, e.g. WiFi, Bluetooth, ultra sound, and infrared. This research discusses indoor localization using the Global System for Mobile Communication (GSM). The use of GSM has many advantages and works during electricity out...
| Main Authors: | , |
|---|---|
| Format: | Thesis |
| Published: |
[Yogyakarta] : Universitas Gadjah Mada
2012
|
| Subjects: |
| Summary: | Most researches in indoor localization are based on the use of short-range
signals, e.g. WiFi, Bluetooth, ultra sound, and infrared. This research discusses indoor
localization using the Global System for Mobile Communication (GSM). The use of
GSM has many advantages and works during electricity outage. The system can be used
in vast area coverage and requires no information about the position of radio
transmitter. The localization method uses Receive Signal Strength (RSS) GSM
fingerprinting. The method has advantages which does not need information of the
position of radio transmitter. The localization stage uses Naive Bayes (NB) method.
Afterwards, the results will be compared with Nearest Neighbour (NN) method.
Indoor localization is conducted on 3rd floor corridor of Department of
Electrical Engineering and Information Technology (JTETI), UGM with total area of ±302 m2. The experiments are performed in 5 scenarios. The first scenario is the
measurement of RSS fingerprint with 2 m2 grid size. The second scenario is the
measurement of RSS fingerprint with 1 m2 grid size. Both scenarios employ 3 Cell-ID
data from XL Axiata GSM Provider. The third scenario is similar to the first scenario
with different data set. Fourth scenario employs RSS fingerprint with 1 m2 grid size and
use up to 4 Cell-ID of the Telkomsel GSM Provider. The fifth scenario measures RSS
fingerprint with 1 m2 grid size using 3 Cell-ID from XL Axiata GSM Provider and 3
Access Point (AP) that is attached on the 3rd floor of Department of Electrical
Engineering and Information Technology (JTETI) UGM building.
The results show that the fifth scenario has the highest accuracy. The scenario
integrates GSM RSS fingerprint data and IEEE 802.11g RSS fingerprint. The use of
Nearest Neighbour gives minimum average error of 5.39 meters. Minimum average
error of 5.11 meters is obtained using Naive Bayes (NB) method. |
|---|