Machine learning assisted GNSS Direct Position Estimation for Urban Environments Applications

Sergio Vicenzo, Xin Qi, Bing Xu, Li Ta Hsu

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

The GNSS direct position estimation (DPE) technique was proposed as a superior alternative to the conventional two-step scalar tracking loop (STL). Existing literature proves DPE’s superiority with simulation data and theoretical bounds. However, in urban areas, its superiority to STL often falters as most of the satellites are error-affected from multipath (MP) and non-line-of-sight (NLOS) reception. The MP and NLOS signals mismatch the existing signal model of DPE, which only assumes line-of-sight (LOS) reception with additive white Gaussian noise. To that end, we aim to solve DPE’s misspecified signal model from MP by integrating DPE with a Random Forest Machine Learning (RF ML) regression approach. The RF ML uses multi-correlator outputs from STL’s tracking as inputs to estimate the code delays and amplitude of the reflected signals. The estimates are then used to correct the MP-affected autocorrelation function (ACF) to produce the LOS ACF. As a traditional DPE does not involve tracking, the RF ML will be integrated with a homegrown multi-correlator based DPE (Corr-DPE) which uses the correlator outputs and pseudorange estimates from STL. Results from real GNSS data point out that a RF ML-integrated Corr-DPE shows promise in offering more superior performance to STL in urban environments.

Original languageEnglish
Title of host publicationION 2024 International Technical Meeting Proceedings https://doi.org/10.33012/2024.19567
PublisherThe Institute of Navigation
Pages1129-1142
Number of pages14
ISBN (Electronic)9780936406367
DOIs
Publication statusPublished - Jan 2024
Event2024 International Technical Meeting of The Institute of Navigation, ITM 2024 - Long Beach, United States
Duration: 22 Jan 202425 Jan 2024

Publication series

NameProceedings of the International Technical Meeting of The Institute of Navigation, ITM
Volume2024-January
ISSN (Print)2330-3662
ISSN (Electronic)2330-3646

Conference

Conference2024 International Technical Meeting of The Institute of Navigation, ITM 2024
Country/TerritoryUnited States
CityLong Beach
Period22/01/2425/01/24

ASJC Scopus subject areas

  • Aerospace Engineering
  • Electrical and Electronic Engineering

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