Abstract

The performance of Global Navigation Satellite System (GNSS) receivers is limited by the ionospheric scintillation effects that cause signal degradation due to refraction, reflection and scattering of the signals. Hence, there is a need to develop an ionospheric scintillation detection technique for robust GNSS receivers. In this paper, a new algorithm based on multifractal detrended fluctuation analysis (MF-DFA) is proposed for detecting the ionospheric irregularities. The ionospheric and scintillation GNSS data recorded at Koneru Lakshmaiah (KL) University, Guntur, India, was considered for the analysis. The carrier to noise ratio (C/N 0) time series data of GNSS satellite vehicles that are affected due to scintillations was decomposed using adaptive time–frequency methods like empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD) and complementary ensemble empirical mode decomposition (CEEMD). It was observed that the CEEMD method combined with MF-DFA provides better results as compared to the EMD and EEMD techniques.

Highlights

  • The performance of Global Navigation Satellite System (GNSS) receivers is limited by the ionospheric scintillation effects that cause signal degradation due to refraction, reflection and scattering of the signals

  • A white noise of 0.1 standard deviation was added to the GPS signal in the ensemble empirical mode decomposition (EEMD) and complementary ensemble empirical mode decomposition (CEEMD) techniques before decomposing for solving the mode-mixing problem

  • Due to the inclusion of complementary noise components, the modes generated using CEEMD matched with the inherent characteristics of the signal

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Summary

Introduction

The performance of Global Navigation Satellite System (GNSS) receivers is limited by the ionospheric scintillation effects that cause signal degradation due to refraction, reflection and scattering of the signals. The multifractality nature of the ionospheric time series data due to long-range correlations can be analysed using the MF-DFA technique. A novel algorithm, complementary ensemble empirical mode decomposition (CEEMD)–MF-DFA, was proposed and implemented to extract the noise components of the GPS signal.

Results
Conclusion
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