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Updated: Jul 16, 2026

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring

Published on: October 24, 2025

Random-Forest-Based Smartphone GNSS Position Correction Using Satellite-Wise LOS Projection Error Estimation and

Kyeongdong Jang¹, Keonwon Seo¹

¹Department of Civil Engineering, School of Architectural, Civil, Environmental, and Energy Engineering, Kyungpook National University, Daegu 41566, Republic of Korea.

Sensors (Basel, Switzerland)

|July 15, 2026

Summary

This study introduces a new method to improve smartphone Global Navigation Satellite System (GNSS) accuracy by analyzing satellite line-of-sight errors. The technique significantly reduces horizontal positioning errors, enhancing navigation reliability.

Keywords:

LOS projection error code pseudorange position correction random forest smartphone GNSS temporal WLS weighted least squares

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Last Updated: Jul 16, 2026

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring

Published on: October 24, 2025

Area of Science:

Geomatics Engineering
Satellite Navigation Systems
Machine Learning Applications

Background:

Smartphone Global Navigation Satellite System (GNSS) positioning suffers from signal degradation due to low-cost hardware and environmental factors like multipath propagation.
Current correction methods often lack the ability to precisely quantify individual satellite contributions to horizontal position errors while maintaining line-of-sight (LOS) geometry.

Purpose of the Study:

To develop a geometry-aware correction method for smartphone GNSS positioning.
To estimate satellite-specific line-of-sight (LOS) projection errors.
To improve the accuracy of horizontal position estimation in challenging GNSS environments.

Main Methods:

A random-forest model was trained using 26 diverse smartphone GNSS features.
Satellite-wise LOS projection errors were estimated using the horizontal error between smartphone (NMEA) and reference (F9P) positions.
Exponential temporal weighted least squares (Temporal WLS) was employed to fuse predicted LOS errors with satellite geometry.

Main Results:

The horizontal root mean square (RMS) error was reduced from 2.747 m to 1.033 m using same-session validation.
Excluding a potentially non-co-located reference session further improved accuracy, reducing RMS error from 2.867 m to 0.362 m.

Conclusions:

The proposed random-forest-assisted, geometry-aware method effectively improves smartphone GNSS positioning accuracy.
This approach offers a novel way to correct for satellite-specific errors, enhancing navigation performance in real-world conditions.