Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/66487
Title: Hybrid localization using Wi-Fi and GPS signals (Machine learning)
Authors: Lin, Roy Weihao
Keywords: DRNTU::Engineering
Issue Date: 2016
Abstract: In this report, the student first analyzes an existing Android App that does indoor navigation. An indoor localization machine-learning approach is then proposed by making use of the Received Signal Strength Indicators (RSSI) of nearby Wi-Fi access points. The algorithm has two phases. In the first phase, RSSI will be collected on an Android smartphone at Level 2, Section B, of the School of Computer Science and Engineering (SCSE) building at Nanyang Technological University. The data collected will be used as a training model for the second phase - testing. The testing phase would make use of three different machine learning algorithms (with kernel options) on the trained model. The algorithm with the least cumulative error on the estimated localized results would be most preferred.
URI: http://hdl.handle.net/10356/66487
Rights: Nanyang Technological University
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

Files in This Item:
File Description SizeFormat 
AY1516 - Roy's FYP Amended Final Report.pdf
  Restricted Access
Machine Learning Regression Algorithms12.82 MBAdobe PDFView/Open

Page view(s)

175
Updated on Jun 23, 2021

Download(s) 50

33
Updated on Jun 23, 2021

Google ScholarTM

Check

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.