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Combining Multichannel RSSI and Vision with Artificial Neural Networks to Improve BLE Trilateration.

Sharareh Naghdi1, Kyle O'Keefe1

  • 1Position, Location, and Navigation (PLAN) Group, Department of Geomatics Engineering, Schulich School of Engineering, University of Calgary, 2500 University Drive, N.W., Calgary, AB T2N 1N4, Canada.

Sensors (Basel, Switzerland)
|June 24, 2022
PubMed
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This study enhances indoor positioning using Bluetooth Low Energy (BLE) by compensating for signal fluctuations with Artificial Neural Networks (ANNs) and camera data. The improved accuracy benefits critical applications like emergency response.

Area of Science:

  • Robotics and Automation
  • Computer Science
  • Electrical Engineering

Background:

  • Indoor positioning systems are crucial for applications like emergency services, but face accuracy challenges.
  • Bluetooth Low Energy (BLE) is a cost-effective indoor positioning technology, yet susceptible to Received Signal Strength Indicator (RSSI) fluctuations due to human shadowing.
  • Existing indoor localization methods like fingerprinting and trilateration struggle with RSSI variability.

Purpose of the Study:

  • To develop a novel method for compensating RSSI fluctuations in BLE-based indoor positioning systems.
  • To improve the accuracy and reliability of indoor localization in complex environments.
  • To integrate wearable camera data with BLE RSSI measurements for enhanced positioning.

Main Methods:

Keywords:
BLEadvertising channelsartificial intelligencehuman body shadowinglocalizationtrilateration

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  • Utilized Artificial Neural Network (ANN) algorithms to process RSSI measurements from three BLE advertising channels.
  • Incorporated data from a wearable camera to detect human obstacles and mitigate shadowing effects.
  • Applied path loss models to convert compensated RSSI values into distance estimates.
  • Employed trilateration techniques for final indoor position determination.

Main Results:

  • The proposed method significantly improved the accuracy of indoor localization compared to traditional approaches.
  • ANN-based RSSI compensation effectively reduced errors caused by human body shadowing.
  • Integration of camera data provided additional context to improve positioning robustness.

Conclusions:

  • The developed system offers a substantial advancement in indoor positioning accuracy for critical applications.
  • Combining BLE RSSI, ANNs, and camera data presents a promising approach to overcome indoor localization challenges.
  • This method enhances the reliability of positioning in dynamic environments with potential obstructions.