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The Global Positioning System (GPS) has become an indispensable tool in fieldwork, offering unparalleled precision and efficiency for surveying, navigation, and infrastructure development. By harnessing signals from a constellation of satellites, GPS receivers determine the location of objects with remarkable speed and accuracy, often completing calculations within a second.Advantages of Modern GPS TechnologyContemporary GPS receivers are designed to meet the practical demands of field...
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Tapes are essential in surveying for accurate, durable, and short-distance measurements. Made from lightweight, nylon-coated steel, they offer flexibility and strength for rugged outdoor use. The nylon coating protects against rust and wear, extending the tape's life. Standard lengths, around 30 meters, are marked in meters and millimeters for precision.Surveyors select tapes based on site conditions and accuracy needs. Lightweight, nylon-coated tapes are commonly used for ease of handling and...
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Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...
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Co-Occurrence Fingerprint Data-Based Heterogeneous Transfer Learning Framework for Indoor Positioning.

Jian Huang1, Haonan Si1,2, Xiansheng Guo1,2

  • 1Department of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.

Sensors (Basel, Switzerland)
|December 11, 2022
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This study introduces a novel heterogeneous transfer learning framework using co-occurrence data for fingerprint-based indoor positioning systems. It improves accuracy and robustness against environmental changes without needing extensive new data.

Keywords:
co-occurrence dataheterogeneous transfer learningindoor positioning

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Area of Science:

  • Computer Science
  • Signal Processing
  • Machine Learning

Background:

  • Fingerprint-based indoor positioning systems (FIPS) face distribution discrepancy challenges due to environmental variations.
  • Reconstructing FIPS models requires frequent data collection, which is costly and often impractical.
  • Existing transfer learning methods struggle with feature space heterogeneity from diverse devices and lack labeled target data.

Purpose of the Study:

  • To propose a heterogeneous transfer learning framework based on co-occurrence data (HTL-CD) for FIPS.
  • To enhance positioning accuracy and robustness against environmental changes without database reconstruction.
  • To address limitations of traditional transfer learning in FIPS, particularly feature heterogeneity and the need for labeled target data.

Main Methods:

  • Mapping source domain samples into the target domain's feature space.
  • Aligning marginal and conditional distributions of source and target samples to minimize divergence.
  • Utilizing co-occurrence fingerprint data to calculate correlation coefficients without labeled target samples.
  • Employing a correlation restriction mechanism to mitigate negative transfer.

Main Results:

  • The proposed HTL-CD framework achieves higher positioning accuracy and robustness.
  • It effectively minimizes distribution divergence caused by device heterogeneity and environmental changes.
  • Real-world experiments show at least 17.15% smaller average localization errors (ALEs) compared to existing methods, even without labeled target data.

Conclusions:

  • HTL-CD offers a superior solution for FIPS by overcoming distribution discrepancy and feature heterogeneity.
  • The framework demonstrates effectiveness and superiority, particularly in scenarios with limited or no labeled target domain data.
  • This approach reduces the need for repeated fingerprint database reconstruction, making FIPS more practical and efficient.