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IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...
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Multiple Fingerprinting Localization by an Artificial Neural Network.

Jaehyun Yoo1

  • 1School of AI Convergence, Sungshin Women's University, Seoul 02844, Korea.

Sensors (Basel, Switzerland)
|October 14, 2022
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Summary
This summary is machine-generated.

This study introduces multiple fingerprinting localization using artificial neural networks for accurate indoor positioning. Experiments analyze the number of targets that can be estimated without accuracy loss.

Keywords:
WiFi fingerprinting localizationartificial neural networkmultiple targets estimation

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Signal Processing

Background:

  • Fingerprinting localization leverages existing infrastructure like WiFi routers for indoor positioning.
  • It involves creating a fingerprint database of signal strengths (RSSI) and locations during an offline phase.
  • Artificial neural networks are employed to model complex relationships between fingerprints and locations due to large datasets.

Purpose of the Study:

  • To develop a multiple fingerprinting localization method capable of estimating the positions of several targets simultaneously.
  • To address the limitation of existing methods that predict only single locations.
  • To analyze the trade-off between the number of estimated targets and localization accuracy.

Main Methods:

  • Development of a multiple fingerprinting localization algorithm based on artificial neural networks.
  • Utilizing WiFi Received Signal Strength Indicator (RSSI) measurements for fingerprinting.
  • Conducting experimental analysis to determine the maximum number of targets that can be localized without compromising accuracy.

Main Results:

  • Successfully developed an artificial neural network-based algorithm for multiple fingerprinting localization.
  • Experimental results provide insights into the performance limits of simultaneous localization.
  • Quantified the relationship between the number of targets and the achievable localization accuracy.

Conclusions:

  • The proposed multiple fingerprinting localization method enhances the utility of indoor positioning systems.
  • Artificial neural networks are effective for modeling complex fingerprinting data for multi-target scenarios.
  • The study provides a foundation for developing more scalable and practical indoor positioning solutions.