Beyond the RSSI value in BLE-based passive indoor localization

Author:

López-de-Teruel Pedro E.1,Cánovas Óscar1,García Félix J.1,González Rubén2,Carrasco José A.2

Affiliation:

1. University of Murcia Murcia, Spain

2. Diwow Murcia, Spain

Funder

Ministerio de Economía y Competitividad

Publisher

ACM

Reference11 articles.

1. Christopher M. Bishop. 2006. Pattern Recognition and Machine Learning (Information Science and Statistics). Springer-Verlag Berlin Heidelberg. Christopher M. Bishop. 2006. Pattern Recognition and Machine Learning (Information Science and Statistics). Springer-Verlag Berlin Heidelberg.

2. Performance evaluation of bluetooth low energy in indoor positioning systems

3. Experiences and Lessons Learned From Making IoT Sensing Platforms for Large-Scale Deployments

4. Location Fingerprinting With Bluetooth Low Energy Beacons

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1. On the accuracy of BLE indoor localization systems: An assessment survey;Computers and Electrical Engineering;2024-09

2. A Multidimensional Analysis of Released COVID-19 Location-Based Mobile Applications;Future Internet;2021-10-25

3. Passive BLE Sensing for Indoor Pattern Recognition and Tracking;Procedia Computer Science;2021

4. Trilateration, Fingerprinting, and Centroid: Taking Indoor Positioning with Bluetooth LE to the Wild;2020 IEEE 21st International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM);2020-08

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