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Wireless Fingerprinting Uncertainty Prediction Based on Machine Learning.

You Li1, Zhouzheng Gao2,3, Zhe He4

  • 1Department of Geomatics Engineering, University of Calgary, 2500 University Dr NW, Calgary, AB T2N 1N4, Canada. liyou331@gmail.com.

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Summary
This summary is machine-generated.

This study predicts wireless fingerprinting location uncertainty using machine learning (ML). This enables adaptive noise setting in integrated localization systems for improved accuracy.

Keywords:
Kalman filterfingerprintingindoor localizationinertial navigationmachine learningneural networkreceived signal strength

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Robotics

Background:

  • Wireless fingerprinting is widely used for indoor localization but its performance quantification is challenging.
  • Accurate weighting of wireless fingerprinting in multi-sensor integration is difficult due to performance uncertainties.

Purpose of the Study:

  • To predict wireless fingerprinting location uncertainty using machine learning (ML).
  • To adaptively set measurement noises in an integrated localization system.

Main Methods:

  • Utilized artificial neural network (ANN) and Gaussian distribution (GD) for uncertainty prediction.
  • Integrated predicted uncertainty into an extended Kalman filter (EKF) for dead-reckoning/wireless fingerprinting localization.

Main Results:

  • Demonstrated the feasibility of predicting wireless fingerprinting uncertainty via ANN.
  • Showcased the effectiveness of adaptive measurement noise setting in the integrated EKF.

Conclusions:

  • Machine learning effectively predicts wireless fingerprinting location uncertainty.
  • Adaptive noise setting enhances the performance of integrated indoor localization systems.