Literature DB >> 32182758

Doubling the Accuracy of Indoor Positioning: Frequency Diversity.

Berthold K P Horn1.   

Abstract

Determination of indoor position based on fine time measurement (FTM) of the round trip time (RTT) of a signal between an initiator (smartphone) and a responder (Wi-Fi access point) enables a number of applications. However, the accuracy currently attainable-standard deviations of 1-2 m in distance measurement under favorable circumstances-limits the range of possible applications. An emergency worker, for example, may not be able to unequivocally determine on which floor someone in need of help is in a multi-story building. The error in position depends on several factors, including the bandwidth of the RF signal, delay of the signal due to the high relative permittivity of construction materials, and the geometry-dependent "noise gain" of position determination. Errors in distance measurements have unusal properties that are exposed here. Improvements in accuracy depend on understanding all of these error sources. This paper introduces "frequency diversity," a method for doubling the accuracy of indoor position determination using weighted averages of measurements with uncorrelated errors obtained in different channels. The properties of this method are verified experimentally with a range of responders. Finally, different ways of using the distance measurements to determine indoor position are discussed and the Bayesian grid update method shown to be more useful than others, given the non-Gaussian nature of the measurement errors.

Entities:  

Keywords:  Bayesian grid; FTM; IEEE 802.11mc; IEEE 802.11–2016; RTT; bandwidth diversity; fine time measurement; frequency diversity; indoor location; indoor position; observation model; round trip time; spatial diversity; time diversity; transition model

Year:  2020        PMID: 32182758     DOI: 10.3390/s20051489

Source DB:  PubMed          Journal:  Sensors (Basel)        ISSN: 1424-8220            Impact factor:   3.576


  4 in total

1.  Toward Accurate Indoor Positioning: An RSS-Based Fusion of UWB and Machine-Learning-Enhanced WiFi.

Authors:  Ghazaleh Kia; Laura Ruotsalainen; Jukka Talvitie
Journal:  Sensors (Basel)       Date:  2022-04-21       Impact factor: 3.847

2.  Indoor Localization Using Uncooperative Wi-Fi Access Points.

Authors:  Berthold K P Horn
Journal:  Sensors (Basel)       Date:  2022-04-18       Impact factor: 3.576

3.  Outdoor Localization Using BLE RSSI and Accessible Pedestrian Signals for the Visually Impaired at Intersections.

Authors:  Kiyoung Shin; Ryan McConville; Oussama Metatla; Minhye Chang; Chiyoung Han; Junhaeng Lee; Anne Roudaut
Journal:  Sensors (Basel)       Date:  2022-01-04       Impact factor: 3.576

4.  A Two-Step Fusion Method of Wi-Fi FTM for Indoor Positioning.

Authors:  Shenglei Xu; Yunjia Wang; Minghao Si
Journal:  Sensors (Basel)       Date:  2022-05-09       Impact factor: 3.576

  4 in total

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