Employing machine learning techniques for estimating the differential code biases of GPS satellites

ABSTRACT

In this study, the capabilities of Machine Learning (ML) are exploited to predict the Differential Code Biases (DCBs) of Global Positioning System (GPS) satellites from the broadcast Total Group Delays (TGDs), satellite numbers, satellite block types, and Sunspot numbers. Firstly, a detailed analysis of the DCB and TGD values over five years is provided. Then, different ML models are trained and tested. The results showed that the bagged trees, the rational quadratic Gaussian Process Regression, and the Feed-Forward Neural Networks (2 hidden layers) models can be used efficiently to predict the DCB values of GPS satellites.

KEYWORDS:

• Differential Code Biases (DCBs)
• Total Group Delays (TGDs)
• Global Positioning System (GPS)
• machine learning (ML)

Disclosure statement

No potential conflict of interest was reported by the author(s).

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/14498596.2024.2371831