Abstract
AbstractIndoor/Outdoor (IO) detection (IOD) using Wi-Fi- and smartphone-based technologies is in high demand and interest in both the industrial and research fields. This paper proposes a novel and effective hybrid IOD (HIOD) approach for detecting a smartphone user's IO location. The HIOD approach uses signals received from both Wi-Fi and GPS as well as the latest positioning technologies such as multilateration, fingerprinting and machine learning. This paper proposes and implements two-level signal-to-noise ratio (SNR) threshold parameters for the first time, which are specifically derived from GPS signals through 42 empirical tests at seven test sites with adequate environmental factors considered. Using the newly derived IOD threshold parameters and a set of IO detection rules, the HIOD approach is then tested at 20 test points (TPs) in a city canyon area, where most of the TPs are under semi-indoor or semi-outdoor conditions. The final test results show that a 100% IOD rate is achieved.
Publisher
Cambridge University Press (CUP)
Subject
Ocean Engineering,Oceanography
Reference33 articles.
1. Bhargava, P. , Gramsky, N. and Agrawala, A. (2014). Senseme: A System for Continuous, On-Device, and Multi-Dimensional Context and Activity Recognition. In Proceedings of the 11th international conference on Mobile and ubiquitous systems: Computing. Networking and Services.
2. Environmental Context Detection for Adaptive Navigation using GNSS Measurements from a Smartphone
3. A Robust Dead Reckoning Algorithm Based on Wi-Fi FTM and Multiple Sensors
4. Bai, Y. B. , Kealy, A. , Retscher, G. and Holden, L. (2020). A Comparative Evaluation of Wi-Fi RTT and GPS Based Positioning Sydney, Australia.
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