Machine learning for indoor positioning based on received signal strength

• Main Author: Felis Dwiyasa
• Other Authors: Lim Meng Hiot
• Format: Theses and Dissertations
• Language: English
• Published: 2018
• Subjects: DRNTU::Engineering::Electrical and electronic engineering::Wireless communication systems; DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
• Online Access: https://hdl.handle.net/10356/89317; http://hdl.handle.net/10220/46235
• Institution: Nanyang Technological University

Indoor positioning is a key technology enabler for various smart systems that require location-based optimization and automation. Despite its potential, the field of indoor positioning faces numerous challenges in terms of accuracy, precision, computational complexity, power consumption, robustness,...