Exploiting Different Combinations of Complementary Sensor's data for Fingerprint-based Indoor Positioning in Industrial Environments

Wi-Fi fingerprinting is a popular technique for smartphone-based indoor positioning. However, well-known RF propagation issues create signal fluctuations that translate into large positioning errors. Large errors limit the usage of Wi-Fi fingerprinting in industrial environments, where the reliabili...

Full description

Saved in:
Bibliographic Details
Main Authors: Torres-Sospedra, Joaquin, Moreira, Adriano, Mendoza-Silva, German M., Joao Nicolau, Maria, Matey-Sanz, Miguel, Silva, Ivo, Huerta, Joaquin, Pendao, Cristiano
Format: Conference Proceeding
Language:English
Subjects:
autonomous vehicle
Data models
ensemble models
Estimation
Indoor navigation
Indoor positioning
IP networks
Merging
multiple interfaces
Reliability
RSSI
Wi-Fi fingerprinting
Wireless fidelity
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Wi-Fi fingerprinting is a popular technique for smartphone-based indoor positioning. However, well-known RF propagation issues create signal fluctuations that translate into large positioning errors. Large errors limit the usage of Wi-Fi fingerprinting in industrial environments, where the reliability of position estimates is a key requirement. One successful approach to deal with signal fluctuations is to average the signals collected simultaneously through independent Wi-Fi interfaces. Another successful approach is to average the estimates provided by models built on independent radio maps. This paper explores multiple combinations of both approaches and determines the procedure to select the best model based on them through a simulated environment. The evaluation of the proposed model in a real-world industrial scenario shows that the positioning error (according to different metrics including the 95 th and 99 th percentiles) is highly improved with respect to the traditional fingerprint.
ISSN:2471-917X
DOI:10.1109/IPIN.2019.8911758