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RF-Based Location Using Interpolation Functions to Reduce Fingerprint Mapping.

Santiago Ezpeleta1, José M Claver2, Juan J Pérez-Solano3

  • 1Departament d'Informàtica, Universitat de València, Avd. de la Universitat, Burjassot 46100, Spain. Santiago.Ezpeleta@uv.es.

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|October 31, 2015
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Summary
This summary is machine-generated.

This study reduces the effort for indoor radio frequency (RF) localization fingerprinting by using interpolation functions. Accurate positioning is achievable with only 50% of the initial reference points, optimizing the training process.

Keywords:
802.15.4 networksRF-Locationfinger-printinginterpolation

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Area of Science:

  • Engineering
  • Computer Science
  • Signal Processing

Background:

  • Indoor localization using RF fingerprinting relies on extensive training data collection.
  • Dynamic environments necessitate frequent database updates, increasing the training burden.
  • Existing methods require a comprehensive set of Radio Signal Strength Indicator (RSSI) measurements.

Purpose of the Study:

  • To investigate the use of interpolation functions to reduce the training effort in RF-based indoor localization.
  • To evaluate the impact of different interpolation strategies and reference point distributions on localization accuracy.
  • To determine the minimum required reference points for achieving acceptable localization performance.

Main Methods:

  • Exploration of various interpolation functions for fingerprint map completion.
  • Evaluation of different test map and reference point distributions.
  • Comparative analysis of localization accuracy with reduced fingerprint databases.

Main Results:

  • Interpolation functions can effectively complete fingerprint maps, reducing the need for extensive initial training.
  • Similar localization accuracy can be maintained with significantly fewer reference points (e.g., 50%).
  • The choice of interpolation method and reference point distribution impacts performance and trade-offs.

Conclusions:

  • Interpolation significantly reduces the time and effort required for RF fingerprint database creation.
  • Optimized training strategies enable efficient and accurate indoor localization systems.
  • This approach offers a practical solution for dynamic indoor environments.