DEEP ASSISTED ATTENTION FOR GNSS SYSTEM TO TRACK VEHICLES
Abstract
Global Navigation Satellite Systems (GNSS) provide reliable location tracking for vehicles, but their accuracy can degrade in challenging environments such as urban canyons or tunnels. Traditional methods struggle to maintain precision under multipath interference and signal obstruction. To address this, a deep assisted attention mechanism is proposed, enhancing GNSS tracking by dynamically weighting input signals based on their relevance. The method integrates deep learning and attention modules to filter noise and amplify critical features from the GNSS data. Experimental results on real-world datasets show a significant improvement in tracking accuracy, with a reduction in position error from 15 meters to 3 meters under challenging conditions. Additionally, signal loss recovery improved by 40%, further enhancing the system's reliability. These results demonstrate the model's potential to significantly enhance vehicle tracking in harsh environments.

Authors
Karthikeyan Thangavel, Saravanan Velusamy, Baiju Karun, Jonathan Cansino
University of Technology and Applied Sciences, Oman

Keywords
GNSS, Deep Learning, Attention Mechanism, Vehicle Tracking, Multipath Interference
Yearly Full Views
JanuaryFebruaryMarchAprilMayJuneJulyAugustSeptemberOctoberNovemberDecember
0000000000122
Published By :
ICTACT
Published In :
ICTACT Journal on Data Science and Machine Learning
( Volume: 5 , Issue: 4 , Pages: 697 - 701 )
Date of Publication :
September 2024
Page Views :
52
Full Text Views :
14

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.