A dynamic model switching algorithm for WiFi fingerprinting indoor positioning

Xu Feng; Khuong An Nguyen; Zhiyuan Luo

doi:10.1109/IPIN57070.2023.10332521

A dynamic model switching algorithm for WiFi fingerprinting indoor positioning

Xu Feng, Khuong An Nguyen, Zhiyuan Luo

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Abstract

In 2023, there are various WiFi technologies and algorithms for an indoor positioning system. However, each technology and algorithm comes with their own strengths and weaknesses that may not universally benefit all building locations. Therefore, we propose a novel algorithm to dynamically switch to the most optimal positioning model at any given location, by utilising a Machine Learning based weighted model selection algorithm, with WiFi RSS and RTT signal measures as the input features. We evaluated our algorithm in three real-world indoor scenarios to demonstrate an improvement of up to 1.8 metres, compared to standard WiFi fingerprinting algorithm.

Original language	English
Title of host publication	13th International Conference on Indoor Positioning and Indoor Navigation (IPIN 2023)
Publisher	IEEE
ISBN (Electronic)	979-8-3503-2011-4
ISBN (Print)	979-8-3503-2012-1
DOIs	https://doi.org/10.1109/IPIN57070.2023.10332521
Publication status	Published - 25 Sept 2023

Access to Document

10.1109/IPIN57070.2023.10332521

Manuscript

https://ipin-conference.org/2023/

Cite this

@inproceedings{4893a866cb0f454e8dc61f0696f02c76,

title = "A dynamic model switching algorithm for WiFi fingerprinting indoor positioning",

abstract = "In 2023, there are various WiFi technologies and algorithms for an indoor positioning system. However, each technology and algorithm comes with their own strengths and weaknesses that may not universally benefit all building locations. Therefore, we propose a novel algorithm to dynamically switch to the most optimal positioning model at any given location, by utilising a Machine Learning based weighted model selection algorithm, with WiFi RSS and RTT signal measures as the input features. We evaluated our algorithm in three real-world indoor scenarios to demonstrate an improvement of up to 1.8 metres, compared to standard WiFi fingerprinting algorithm.",

author = "Xu Feng and Nguyen, {Khuong An} and Zhiyuan Luo",

year = "2023",

month = sep,

day = "25",

doi = "10.1109/IPIN57070.2023.10332521",

language = "English",

isbn = "979-8-3503-2012-1",

booktitle = "13th International Conference on Indoor Positioning and Indoor Navigation (IPIN 2023)",

publisher = "IEEE",

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T1 - A dynamic model switching algorithm for WiFi fingerprinting indoor positioning

AU - Feng, Xu

AU - Nguyen, Khuong An

AU - Luo, Zhiyuan

PY - 2023/9/25

Y1 - 2023/9/25

N2 - In 2023, there are various WiFi technologies and algorithms for an indoor positioning system. However, each technology and algorithm comes with their own strengths and weaknesses that may not universally benefit all building locations. Therefore, we propose a novel algorithm to dynamically switch to the most optimal positioning model at any given location, by utilising a Machine Learning based weighted model selection algorithm, with WiFi RSS and RTT signal measures as the input features. We evaluated our algorithm in three real-world indoor scenarios to demonstrate an improvement of up to 1.8 metres, compared to standard WiFi fingerprinting algorithm.

AB - In 2023, there are various WiFi technologies and algorithms for an indoor positioning system. However, each technology and algorithm comes with their own strengths and weaknesses that may not universally benefit all building locations. Therefore, we propose a novel algorithm to dynamically switch to the most optimal positioning model at any given location, by utilising a Machine Learning based weighted model selection algorithm, with WiFi RSS and RTT signal measures as the input features. We evaluated our algorithm in three real-world indoor scenarios to demonstrate an improvement of up to 1.8 metres, compared to standard WiFi fingerprinting algorithm.

U2 - 10.1109/IPIN57070.2023.10332521

DO - 10.1109/IPIN57070.2023.10332521

M3 - Conference contribution

SN - 979-8-3503-2012-1

BT - 13th International Conference on Indoor Positioning and Indoor Navigation (IPIN 2023)

PB - IEEE

ER -