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Calibration-Free 3D Indoor Positioning Algorithms Based on DNN and DIFF.

Jingmin Yang1,2,3, Shanghui Deng1,2, Li Xu3

  • 1School of Computer Science, Minnan Normal University, Zhangzhou 363000, China.

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|August 12, 2022
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Summary
This summary is machine-generated.

This study introduces a new method to improve Wi-Fi positioning accuracy by using signal strength differences (DIFF) and deep neural networks (DNNs). The approach enhances efficiency and accuracy for multi-building, multi-floor, and specific location indoor positioning.

Keywords:
3D indoor positioningcalibration-freedeep denoising autoencoder (DDAE)fingerprint databasesignal strength difference (DIFF)

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Signal Processing

Background:

  • Received Signal Strength Indication (RSSI) based indoor positioning suffers from accuracy degradation due to device heterogeneity, interference, and multi-path effects.
  • Signal Strength Difference (DIFF) mitigates device variance but drastically increases data dimensionality, reducing positioning efficiency.
  • Existing methods struggle with the high-dimensional data and 3D indoor positioning challenges.

Purpose of the Study:

  • To develop a novel data hierarchical processing strategy to improve high-dimensional data efficiency for Wi-Fi positioning.
  • To design three deep neural network (DNN)-based positioning algorithms for multi-building, multi-floor, and specific-location scenarios, enabling 3D indoor positioning.
  • To enhance the accuracy and efficiency of indoor positioning systems compared to traditional and advanced machine learning algorithms.

Main Methods:

  • Data preprocessing involved creating original, optimized, and DIFF databases by cleaning and transforming RSSI data.
  • A hierarchical data processing strategy was implemented to manage high-dimensional DIFF data.
  • Three DNN-based algorithms were developed: a denoising autoencoder (DAE) for multi-building, an enhanced DNN for multi-floor, and a deep denoising autoencoder (DDAE) for specific-location positioning.

Main Results:

  • The proposed algorithms demonstrated superior positioning efficiency and accuracy over traditional machine learning and current advanced deep learning methods.
  • The data hierarchical processing strategy effectively addressed the dimensionality explosion issue caused by DIFF.
  • The 3D positioning capability was successfully extended from single-plane positioning.

Conclusions:

  • The developed DNN-based algorithms and data processing strategy significantly improve indoor Wi-Fi positioning accuracy and efficiency.
  • The hierarchical processing and DNN approach effectively handles the challenges of RSSI fluctuations and high-dimensional data.
  • This research offers a robust solution for accurate and efficient 3D indoor positioning systems.