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Global Positioning System (GPS) technology has revolutionized navigation and positioning, but its accuracy is often compromised by various errors. These errors, stemming from environmental, satellite, and receiver-related factors, require careful mitigation to ensure reliable performance across applications.Atmospheric ErrorsGPS signals travel through the Earth’s ionosphere and troposphere, introducing delays which affect accuracy. The ionosphere is strongly influenced by charged particles,...
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Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
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Distance Measurements in UWB-Radio Localization Systems Corrected with a Feedforward Neural Network Model.

Peter Krapež1, Matjaž Vidmar2, Marko Munih1

  • 1Laboratory of Robotics, Faculty of Electrical Engineering, University of Ljubljana, Tržaška cesta 25, 1000 Ljubljana, Slovenia.

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Summary
This summary is machine-generated.

This study introduces a neural network (NN) method to correct orientation-induced distance errors in ultra-wideband (UWB) localization systems. The NN approach improves accuracy without needing signal strength or antenna parameters, enhancing localization precision in GPS-denied environments.

Keywords:
distance measurementerror mitigationreal-time localization systemtag orientationultra-wideband technology

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Area of Science:

  • Robotics
  • Localization Systems
  • Sensor Fusion

Background:

  • Ultra-wideband (UWB) localization systems offer an alternative to GPS in environments where satellite signals are unavailable.
  • Distance measurements in UWB systems using two-way protocols are susceptible to orientation-dependent errors.
  • Prior research attempted error correction by analyzing received power and channel response signals.

Purpose of the Study:

  • To present a novel neural network (NN) method for correcting orientation-induced distance errors in UWB localization.
  • To develop a method that does not require signal strength, channel response, or antenna/module parameters.
  • To improve the accuracy of UWB distance measurements by accounting for tag orientation.

Main Methods:

  • A neural network (NN) model was trained using machine learning with measurements of distance and tag orientation.
  • The NN method utilizes only the measured distance and the orientation of the UWB tag.
  • Experimental verification involved a setup with 12 anchors and one tag.

Main Results:

  • The proposed NN method achieved 5 cm better root mean square error (RMSE) compared to a calibration method lacking orientation information.
  • Using a least-square estimator with NN-corrected distances resulted in 14 cm 3D RMSE, a 9 cm improvement over raw distances.
  • The NN approach improved accuracy without requiring UWB module diagnostic parameters.

Conclusions:

  • The presented NN method effectively corrects orientation-induced distance errors in UWB localization systems.
  • This approach enhances localization accuracy without complex signal analysis or module parameter extraction.
  • The method maintains the minimum packet size for ranging protocols, offering practical advantages.