TRACKING MOVING OBJECTS POSITION USING ANDROID LOCATION AND GOOGLE MAPS API BY UTILIZING THE KALMAN FILTER PREDICTION TECHNIQUE (CASE STUDY: TRACKING RUNNERS AT ITB ULTRA MARATHON 2019)

• Main Author: Halim, Andreas
• Format: Final Project
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/63687
• Institution: Institut Teknologi Bandung

ABSTRACT TRACKING MOVING OBJECTS POSITION USING ANDROID LOCATION AND GOOGLE MAPS API BY UTILIZING THE KALMAN FILTER PREDICTION TECHNIQUE (CASE STUDY: TRACKING RUNNERS AT ITB ULTRA MARATHON 2019) By ANDREAS HALIM Student Number : 13516003 Practically, tracking and prediction on maps are very widely used, especially for individuals or companies whose work is in the field of monitoring a real moving object or usually better known as real time. The technology so-called GPS, is the useful technology for tracking the location of an object. However, GPS doesn’t work properly when blocked by an obstacle, such as tunnels and buildings. In this final project, the development of a tracking application and prediction of the position of moving objects utilizes Android Location services and the Google Maps API as tracking moving objects to receive and display locations related. Meanwhile, the prediction of moving objects in this application utilizes a technique known as Kalman Filter. This technique utilizes a calculation that aims to eliminate noise caused by the satellite's inaccuracy in mapping the location of the device. By eliminating noise, the measurement results will approach the conditions that should be and something called prediction is reducing noise. Referring to the summary of the existing problems, this Final Project produces a software product, namely a tracking application and prediction of the position of moving objects with the case study being the ITB Ultra Marathon 2019 runner running on the Android mobile operating system. Based on the results of the tests carried out, the application has been implemented with Kalman Filter in it. So far this test was carried out, the results obtained were quite optimal. Keywords: GPS, Kalman Filter, prediction, tracking.