# Machine Learning-Based GPS Multipath Detection Method Using Dual Antennas

arXiv: https://arxiv.org/abs/2204.14001 · HTML: https://mlsanity.com/paper/2204.14001/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 62 references that are in the map.
- First posted on arXiv 6 Apr 2022.

## Builds on (most cited first, 23 of 23)

- [A GPS spoofing detection and classification correlator-based technique using the LASSO](https://mlsanity.com/paper/2004.13900/) (2020)
- [Fast User Adaptation for Human Motion Prediction in Physical Human-Robot Interaction](https://mlsanity.com/paper/2109.08881/) (2021)
- [Dynamic Difficulty Adjustment via Fast User Adaptation](https://mlsanity.com/paper/2006.15545/) (2020)
- [Motion Planning by Reinforcement Learning for an Unmanned Aerial Vehicle in Virtual Open Space with Static Obstacles](https://mlsanity.com/paper/2009.11799/) (2020)
- [Prediction of Human Trajectory Following a Haptic Robotic Guide Using Recurrent Neural Networks](https://mlsanity.com/paper/1903.01027/) (2019)
- [Indoor Path Planning for an Unmanned Aerial Vehicle via Curriculum Learning](https://mlsanity.com/paper/2108.09986/) (2021)
- [Sample-Efficient Training of Robotic Guide Using Human Path Prediction Network](https://mlsanity.com/paper/2008.05054/) (2020)
- [Development of Record and Management Software for GPS/Loran Measurements](https://mlsanity.com/paper/2009.11803/) (2020)
- [Effects of Initial Attitude Estimation Errors on Loosely Coupled Smartphone GPS/IMU Integration System](https://mlsanity.com/paper/2009.11807/) (2020)
- [Effect of Outlier Removal from Temporal ASF Corrections on Multichain Loran Positioning Accuracy](https://mlsanity.com/paper/2009.11812/) (2020)
- [Neural Network-Based Ranging with LTE Channel Impulse Response for Localization in Indoor Environments](https://mlsanity.com/paper/2009.11907/) (2020)
- [A Preliminary Study of Machine-Learning-Based Ranging with LTE Channel Impulse Response in Multipath Environment](https://mlsanity.com/paper/2009.11910/) (2020)
- [RSS-based LTE Base Station Localization Using Single Receiver in Environment with Unknown Path-Loss Exponent](https://mlsanity.com/paper/2009.11912/) (2020)
- [Integrity-Based Path Planning Strategy for Urban Autonomous Vehicular Navigation Using GPS and Cellular Signals](https://mlsanity.com/paper/2010.03761/) (2020)
- [Single-Antenna-Based GPS Antijamming Method Exploiting Polarization Diversity](https://mlsanity.com/paper/2010.06130/) (2020)
- [Optimal Action-based or User Prediction-based Haptic Guidance: Can You Do Even Better?](https://mlsanity.com/paper/2101.01870/) (2021)
- [Enhanced Accuracy Simulator for a Future Korean Nationwide eLoran System](https://mlsanity.com/paper/2108.06008/) (2021)
- [GPS Multipath Detection Based on Carrier-to-Noise-Density Ratio Measurements from a Dual-Polarized Antenna](https://mlsanity.com/paper/2108.09493/) (2021)
- [Development of an R-Mode Simulator Using MF DGNSS Signals](https://mlsanity.com/paper/2108.09496/) (2021)
- [First Demonstration of the Korean eLoran Accuracy in a Narrow Waterway Using Improved ASF Maps](https://mlsanity.com/paper/2109.08990/) (2021)
- [Indoor Navigation Algorithm Based on a Smartphone Inertial Measurement Unit and Map Matching](https://mlsanity.com/paper/2109.11706/) (2021)
- [SFOL DME Pulse Shaping Through Digital Predistortion for High-Accuracy DME](https://mlsanity.com/paper/2111.00717/) (2021)
- [Optimal Parameter Inflation to Enhance the Availability of Single-Frequency GBAS for Intelligent Air Transportation](https://mlsanity.com/paper/2111.01953/) (2021)

## Built on by (most cited first, 2 of 2)

- [A Survey of Machine Learning Techniques for Improving Global Navigation Satellite Systems](https://mlsanity.com/paper/2406.16873/) (2024)
- [Diff-GNSS: Diffusion-based Pseudorange Error Estimation](https://mlsanity.com/paper/2509.17397/) (2025)

## Abstract (arXiv)

In urban areas, global navigation satellite system (GNSS) signals are often reflected or blocked by buildings, thus resulting in large positioning errors. In this study, we proposed a machine learning approach for global positioning system (GPS) multipath detection that uses dual antennas. A machine learning model that could classify GPS signal reception conditions was trained with several GPS measurements selected as suggested features. We applied five features for machine learning, including a feature obtained from the dual antennas, and evaluated the classification performance of the model, after applying four machine learning algorithms: gradient boosting decision tree (GBDT), random forest, decision tree, and K-nearest neighbor (KNN). It was found that a classification accuracy of 82%-96% was achieved when the test data set was collected at the same locations as those of the training data set. However, when the test data set was collected at locations different from those of the training data, a classification accuracy of 44%-77% was obtained.