A machine-learning approach to estimate satellite-based position errors
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Anil Kumar Ramavath
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Naveen Kumar Perumalla
Abstract
Satellite-based navigation systems are widely used in transportation. GNSS signal’s strength or quality can easily be degraded by local environments. As a result, the position accuracy of satellite-based navigation systems decreases. In this paper, a novel approach for estimating the positioning error is proposed using ML/DL technique. For learning the relationship between position errors and increased data from GNSS receivers without any prior experience, neural networks have become the machine learning option of choice in the past few years. Signal degradation is best measured by dilution of precision, elevation angles, and carrier-to-noise ratios. To estimate the position error of satellite-based navigation systems, neural networks are trained in this paper. This paper applies a long-short-term memory (LSTM) network to model the temporal correlation of position error measurements. Therefore, neural networks are capable of learning the trend of position errors through training.
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Research ethics: Not applicable.
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Author contributions: The authors has accepted responsibility for the entire content of this manuscript and approved its submission.
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Competing interests: The authors declare no competing interests regarding this article.
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Research funding: No funds granted for this research
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Data availability: Not applicable.
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Articles in the same Issue
- Frontmatter
- Review
- A proposed neural network model for obtaining precipitable water vapor
- Original Research Articles
- Assessment of GNSS observations and positioning performance from non-flagship Android smartphones
- Dynamic mode decomposition and bivariate autoregressive short-term prediction of Earth rotation parameters
- Comparison of selected reliability optimization methods in application to the second order design of geodetic network
- Monte Carlo variance propagation for the uncertainty modeling of a kinematic LiDAR-based multi-sensor system
- Ensemble based deep learning model for prediction of integrated water vapor (IWV) using GPS and meteorological observations
- Keypoint-based registration of TLS point clouds using a statistical matching approach
- PPP_Mansoura: an open-source software for multi-constellation GNSS processing
- Ionospheric TEC prediction using FFNN during five different X Class solar flares of 2021 and 2022 and comparison with COKSM and IRI PLAS 2017
- Analysis of differential code biases for GPS receivers over the Indian region
- A machine-learning approach to estimate satellite-based position errors
- Monitoring of spatial displacements and deformation of hydraulic structures of hydroelectric power plants of the Dnipro and Dnister cascades (Ukraine)
