Disentangling ionospheric refraction and diffraction effects in GNSS raw phase through fast iterative filtering technique

Abstract

We contribute to the debate on the identification of phase scintillation induced by the ionosphere on the global navigation satellite system (GNSS) by introducing a phase detrending method able to provide realistic values of the phase scintillation index at high latitude. It is based on the fast iterative filtering signal decomposition technique, which is a recently developed fast implementation of the well-established adaptive local iterative filtering algorithm. FIF has been conceived to decompose nonstationary signals efficiently and provide a discrete set of oscillating functions, each of them having its frequency. It overcomes most of the problems that arise when using traditional time-frequency analysis techniques and relies on a consolidated mathematical basis since its a priori convergence and stability have been proved. By relying on the capability of FIF to efficiently identify the frequencies embedded in the GNSS raw phase, we define a method based on the FIF-derived spectral features to identify the proper cutoff frequency for phase detrending. To test such a method, we analyze the data acquired from GPS and Galileo signals over Antarctica during the September 2017 storm by the ionospheric scintillation monitor receiver (ISMR) located in Concordia Station (75.10° S, 123.33° E). Different cases of diffraction and refraction effects are provided, showing the capability of the method in deriving a more accurate determination of the index. We found values of cutoff frequency in the range of 0.73-0.83 Hz, providing further evidence of the inadequacy of the choice of 0.1 Hz, which is often used when dealing with ionospheric scintillation monitoring at high latitudes.

Ionospheric Disturbances Over the Indian Sector During 8 September 2017 Geomagnetic Storm: Plasma Structuring and Propagation

“…Despite the different number of receivers used for ROTI (25) and for S4 (2) calculations, and the fact that the different coverage limits the possibility of a thorough integration, the joint use of ROTI and S4 allows speculating on the scale sizes of the irregularities during the storm. ROTI is more sensitive to large‐scale depletion changes (above few kilometers), while S4 index is sensitive to small‐scale ones, that is, those below the Fresnel's frequency for L‐band signals, being of the order of a few hundreds of meters (see, e.g., Ghobadi et al., 2020 and references therein). Usually when small‐scale irregularities exist and trigger scintillation, they are formed in the plasma cascade from large‐scale plasma structures, typically EPBs.…”

GNSS positioning error forecasting in the Arctic: ROTI and Precise Point Positioning error forecasting from solar wind measurements

“…In this study, ionospheric disturbances are measured by the Rate of TEC Index (ROT I) (Pi et al, 1997). The ROT I index is able to detect ionosphere disturbances and is a robust index, not affected by detrending of the phase measurements needed in the phase scintillation index definition (Ghobadi et al, 2020). While the use of dual-frequency observations allows the correction of ionospheric delay to the first order, higher order terms remain.…”

New insights and best practices for the successful use of Empirical Mode Decomposition, Iterative Filtering and derived algorithms

“…As a matter of fact, they are largely implemented in geophysical studies [30][31][32] , with applications in Seismology (data denoising and/or detrending [33][34][35] , pre-seismic signal analysis [36][37][38] , earthquake-induced co/post-seismic anomalies analysis 39 ), Exploration Seismology (for improving signal-to-noise ratio in seismic data processing routines 40,41 or for seismic interpretation 42 ), Geomagnetism [43][44][45][46][47][48][49] , Engineering Seismology (mainly for analysing ground motion data [50][51][52][53][54] ), climate, atmospheric and oceanographic sciences [55][56][57][58][59][60] . Their use is also common in Physics (for data analysis [61][62][63][64][65] , data denoising and/or detrending [66][67][68] , to assess causal relationships between two time series 69 , or to extract information on multiple time scales 70 ); Medicine and Biology [71][72][73][74]…”