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NLOS Multipath Classification of GNSS Signal Correlation Output Using Machine Learning.

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This study introduces a machine learning method to detect non-line-of-sight (NLOS) multipath in Global Navigation Satellite System (GNSS) signals. The approach accurately identifies signal distortions, improving positioning accuracy in challenging environments.

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Area of Science:

  • * Navigation and Positioning Systems
  • * Signal Processing
  • * Machine Learning Applications

Background:

  • * Non-line-of-sight (NLOS) multipath propagation significantly degrades Global Navigation Satellite System (GNSS) positioning accuracy.
  • * Conventional GNSS receivers struggle to mitigate errors caused by NLOS multipath.
  • * Primitive GNSS signal correlation outputs contain exploitable features indicative of NLOS conditions.

Purpose of the Study:

  • * To develop and evaluate a machine learning-based method for detecting NLOS multipath using GNSS signal correlation data.
  • * To compare the effectiveness of Support Vector Machine (SVM) and Neural Network (NN) algorithms for NLOS detection.
  • * To propose an automated approach for generating training datasets for supervised learning models.

Main Methods:

  • * Utilizing multi-correlator outputs from GNSS receivers as the primary data source.
  • * Extracting signal shape features from correlation outputs to identify NLOS distortions.
  • * Implementing and comparing supervised learning models: Support Vector Machine (SVM) and Neural Network (NN).
  • * Developing an automated data collection method for Line-of-Sight (LOS) and NLOS signals.

Main Results:

  • * Neural Network (NN) outperformed Support Vector Machine (SVM) in discriminating NLOS signals.
  • * The proposed method achieved a 97.7% accuracy in correctly identifying NLOS signals during urban environment evaluations.
  • * Automated training data collection facilitated efficient model development.

Conclusions:

  • * Machine learning, particularly Neural Networks, offers a robust solution for detecting NLOS multipath in GNSS.
  • * Feature extraction from primitive GNSS signal correlation outputs is effective for NLOS identification.
  • * The developed method significantly enhances GNSS positioning reliability in urban and obstructed environments.