Determining the Indoor Location of an Emergency Caller in a Multi-story Building

Logan, Luke; Davids, Carol; Davids, Cary

doi:10.1109/cqr47547.2020.9101391

Cited by 1 publication

(1 citation statement)

References 12 publications

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“…With an advancement of computing devices and radio frequency (RF) technologies, the need for locationbased services has begun to extend beyond military use. Today, wireless localization can be found in a wide variety of industries, including security, commercial, healthcare, and tourism, with applications ranging from intruder detection [1,2], asset tracking [3][4][5], and emergency call locating system [6][7][8] to capsule endoscope tracking [9][10][11], geo-fencing [12,13], and automated museum tour [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

An ensemble approach to deep‐learning‐based wireless indoor localization

Wisanmongkol

Taparugssanagorn

Tran

et al. 2022

IET Wireless Sensor Systems

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The authors investigate the use of deep learning in wireless indoor localization to address the shortcomings of the existing range-based (e.g. trilateration and triangulation) and range-free (e.g. fingerprinting) localization. Instead of relying on geometric models and hand-picked features, deep learning can automatically extract the relationship between the observed data and the target's location. Nevertheless, a deep neural network (DNN) model providing a satisfactory accuracy might perform differently when it is retrained in the deployment. To mitigate this issue, the authors propose an ensemble method where DNN models obtained from multiple training sessions are combined to locate the target. In the authors' evaluation, several DNN models are trained on the data, which consists of the received signal strength (RSS), angle of arrival (AOA), and channel state information (CSI), used in the existing hybrid RSS/AOA and RSS/CSI fingerprinting, and their rootmean-square error (RMSE) values are compared accordingly. The results show that the proposed method achieves the lower RMSE than the existing methods, and the RMSE can be lowered by up to 1.47 m compared with the ones obtained from a single model. Moreover, for some DNN models, the RMSE values are even lower than the minimum RMSE obtained by their single-model counterparts.

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