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GPS surveying methods vary in application, accuracy, and data collection techniques, catering to diverse surveying and mapping needs. Static GPS, kinematic GPS, and real-time kinematic (RTK) surveying are widely used. Each technique offers distinct advantages.Static GPS involves placing one receiver at a known reference point and another at the target point. It collects exact positional data by observing multiple satellite ranges over an extended period, achieving centimeter-level accuracy for...
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Using a Real-Time Locating System to Measure Walking Activity Associated with Wandering Behaviors Among Institutionalized Older Adults
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An Efficient Indoor Positioning Method Based on Wi-Fi RSS Fingerprint and Classification Algorithm.

Balaji Ezhumalai1, Moonbae Song2, Kwangjin Park1

  • 1Department of Information and Communication Engineering, Wonkwang University, Iksan 570-749, Korea.

Sensors (Basel, Switzerland)
|June 2, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces an improved Wi-Fi Received Signal Strength (RSS) measurement technique (IRSSMT) to enhance indoor positioning accuracy. The new method significantly reduces errors caused by signal fluctuations and multipath interference.

Keywords:
RSS extractionSAP similarityWi-Fi fingerprintfingerprint clusteringindoor positioningreceived signal strength (RSS)

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Signal Processing

Background:

  • Wi-Fi Received Signal Strength (RSS) fingerprinting is a cost-effective indoor positioning method.
  • Multipath interference and dynamic signal changes degrade Wi-Fi RSS-based positioning accuracy.
  • Existing methods fail to address the nonlinear signal distribution and environmental dynamics.

Purpose of the Study:

  • To enhance indoor positioning accuracy by minimizing RSS observation errors.
  • To develop a novel clustering technique that accounts for signal dynamics and nonlinear similarities.
  • To improve the reliability of Wi-Fi RSS fingerprinting in complex indoor environments.

Main Methods:

  • An improved RSS measurement technique (IRSSMT) using selected RSS median values and strongest access point (SAP) information.
  • A clustering technique grouping reference points (RPs) based on SAP similarity.
  • Experimental validation in two distinct indoor environments.

Main Results:

  • The proposed IRSSMT significantly minimizes RSS observation errors.
  • The SAP similarity-based clustering effectively groups RPs considering signal dynamics.
  • Experimental results demonstrate substantial improvements in positioning accuracy.

Conclusions:

  • The IRSSMT and SAP-based clustering offer a robust solution for accurate indoor positioning.
  • The method effectively overcomes limitations of traditional RSS fingerprinting techniques.
  • Significant accuracy improvements (up to 89.06%) validate the proposed approach.