Work

Enconv1d Model Based on Pseudolite System for Long-Tunnel Positioning

Year: 2025

Type: article

Abstract: Pseudolite positioning systems offer precise localization when GPS signals are unavailable, advancing the development of intelligent transportation systems. However, in confined indoor environments such as kilometer-long tunnels, where vehicles move at high speeds, traditional pseudolite algorithms struggle to establish accurate physical models linking signals to spatial domains. This study introduces a deep learning-based pseudolite positioning algorithm leveraging a spatio-temporal fusion framework to address challenges such as signal attenuation, multipath effects, and non-line-of-sight (NLOS) effects. The Enconv1d model we developed is based on the spatio-temporal characteristics of the pseudolite observation signals. The model employs the encoder module from the Transformer to capture multi-step time constraints while introducing a multi-scale one-dimensional convolutional neural network module (1D CNN) to assist the encoder module in learning spatial features and finally outputs the localization results of the Enconv1d model after the dense layer integration. Four experimental tests in a 4.6 km long real-world tunnel demonstrate that the proposed framework delivers continuous decimeter-level positioning accuracy.

Source: Remote Sensing

DOI: 10.3390/rs17050858

Authors: Changgeng Li, Yuting Zhang, Changshui Liu

Institution: Central South University

Language: English

Topic: GNSS positioning and interference

Subfield: Aerospace Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $3,258