Rapid deployment indoor localization without prior human participation

• Main Authors: XU, Han, ZHOU, Zimu, SHANGGUAN, Longfei
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2016
• Subjects: Localization; Field Division; Smart Phone; Digital Communications and Networking; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/4745; https://ink.library.smu.edu.sg/context/sis_research/article/5748/viewcontent/lcn16_short_xu.pdf
• Institution: Singapore Management University

In this work, we propose RAD, a RApid Deployment localization framework without human sampling. The basic idea of RAD is to automatically generate a fingerprint database through space partition, of which each cell is fingerprinted by its maximum influence APs. Based on this robust location indicator, fine-grained localization can be achieved by a discretized particle filter utilizing sensor data fusion. We devise techniques for CIVD-based field division, graph-based particle filter, EM-based individual character learning, and build a prototype that runs on commodity devices. Extensive experiments show that RAD provides a comparable performance to the state-of-the-art RSSbased methods while relieving it of prior human participation.