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IR Frequency Region: Fingerprint Region01:03

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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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Adaptive Residual Weighted K-Nearest Neighbor Fingerprint Positioning Algorithm Based on Visible Light Communication.

Shiwu Xu1,2, Chih-Cheng Chen3,4, Yi Wu1

  • 1Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350007, China.

Sensors (Basel, Switzerland)
|August 14, 2020
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Summary

The adaptive residual weighted K-nearest neighbor (ARWKNN) algorithm improves positioning accuracy in visible light communication by dynamically adjusting K based on signal strength. This method significantly reduces average positioning errors compared to existing algorithms.

Keywords:
distance metricfingerprint positioningindoor positioning systemvisible light communicationweighted K-nearest neighbor

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Area of Science:

  • * Wireless communication and localization technologies.
  • * Signal processing and algorithm development.

Background:

  • * Weighted K-nearest neighbor (WKNN) is a common fingerprint positioning method.
  • * Optimizing the K value is crucial for minimizing positioning error in WKNN.
  • * Visible light communication (VLC) presents unique challenges for accurate localization.

Purpose of the Study:

  • * To propose an adaptive residual weighted K-nearest neighbor (ARWKNN) algorithm for enhanced fingerprint positioning in VLC systems.
  • * To dynamically optimize the K value in WKNN based on received signal strength indication (RSSI) residuals.
  • * To evaluate the performance of ARWKNN against various existing positioning algorithms.

Main Methods:

  • * Fingerprint matching using RSSI vectors.
  • * Dynamic adjustment of the K value based on matched RSSI residuals.
  • * Performance evaluation using simulation with a signal-to-noise ratio of 20 dB and Manhattan distance in a 2-D space.

Main Results:

  • * ARWKNN demonstrated reduced average positioning errors compared to Random Forest, Extreme Learning Machine, Artificial Neural Network, Grid-Independent Least Square, Self-Adaptive WKNN, WKNN, and KNN.
  • * ARWKNN achieved the minimum average positioning error in 2-D using Clark distance and in 3-D using minimum maximum distance metrics.
  • * The proposed algorithm significantly reduces average positioning error while maintaining similar algorithm complexity compared to SAWKNN, WKNN, and KNN.

Conclusions:

  • * The ARWKNN algorithm offers superior positioning accuracy in VLC environments.
  • * Dynamic K value optimization based on RSSI residuals is effective for improving localization performance.
  • * ARWKNN provides a significant advancement in fingerprint positioning accuracy and efficiency.