Gaussian Filtered RSSI-based Indoor Localization in WLAN using Bootstrap Filter

Wang, Jingjing; Hwang, Jun; Peng, Jishen; Park, Jae-Woo; Park, Joon Goo

doi:10.1109/iceic51217.2021.9369804

Cited by 5 publications

(1 citation statement)

References 6 publications

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“…Fluctuations in the value of RSSI caused by various environmental parameters reduce its correlation with respect to distance, and thereby making RSSI somewhat unreliable to do localization with [16]. Therefore, a significant amount of work done on the development of RSSI-based positioning systems has been efforts towards developing techniques for negating the fluctuations in RSSI [20,21,22] and therefore minimizing errors in location estimation.…”

Section: Need For Filtering Rssi Valuesmentioning

confidence: 99%

Utilizing machine learning algorithms for localization using RSSI values of wireless LAN

Ganguly¹,

Nayak²,

Irene³

et al. 2022

ITU J-FET

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With the development of new technologies, there has been an upsurge in the demand for precise localization in both outdoor and indoor environments. While a Global Positioning System (GPS) provides sufficient positioning precision in outdoor settings, its accuracy declines in indoor scenarios, necessitating the development of novel positioning approaches that function accurately both indoors and outdoors. The use of various Wireless Local Area Network (WLAN) parameters for localization has been conceptualized. In this study, we attempt to do localization using machine learning methods on WLAN Received Signal Strength Indicator (WLAN RSSI) measurements. We compare the performance of multiple machine learning algorithms on the data set to see which can be used to design efficient future localization systems. The proposed study has achieved second place for the problem statement "ITU-ML5G-PS-016: Location estimation using RSSI of wireless LAN" in AI/ML in 5G Challenge 2021 organized by the International Telecommunication Union.

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Section: Need For Filtering Rssi Valuesmentioning

confidence: 99%