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A Post-Processing Multipath/NLoS Bias Estimation Method Based on DBSCAN.

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Summary
This summary is machine-generated.

This study introduces a novel method using DBSCAN to estimate Global Navigation Satellite System (GNSS) pseudorange biases caused by urban multipath and Non-Line-of-Sight (NLoS) effects, improving positioning accuracy.

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Area of Science:

  • * Satellite Navigation Systems
  • * Signal Processing
  • * Geomatics Engineering

Background:

  • * Urban environments present significant challenges for Global Navigation Satellite Systems (GNSS) due to multipath and Non-Line-of-Sight (NLoS) effects.
  • * These effects introduce pseudorange biases that degrade the accuracy of GNSS positioning applications.
  • * Current methods for identifying and classifying multipath/NLoS events remain challenging.

Purpose of the Study:

  • * To propose a post-processing method for estimating pseudorange biases caused by multipath/NLoS effects.
  • * To provide accurate pseudorange bias data for training machine learning models for multipath/NLoS detection and mitigation.
  • * To establish a benchmark for evaluating new methods designed to detect multipath/NLoS effects.

Main Methods:

  • * Extraction of multipath/NLoS biases from pseudorange measurements using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm.
  • * Validation of the method using real-world data collections in static and dynamic urban multipath/NLoS scenarios.
  • * Performance evaluation based on positioning accuracy by comparing solutions derived from bias-corrected pseudoranges against ground truth.

Main Results:

  • * Successful estimation of pseudorange biases attributed to multipath/NLoS conditions.
  • * Demonstrated effectiveness of the proposed method in both static and dynamic scenarios.
  • * Validation through improved positioning performance when estimated biases are applied.

Conclusions:

  • * The proposed DBSCAN-based method effectively estimates pseudorange biases in urban GNSS environments.
  • * The estimated biases can significantly enhance the training of machine learning algorithms for improved GNSS positioning.
  • * This approach provides a reliable validation strategy for multipath/NLoS detection techniques.