Title :
Characterization of the ionospheric scintillations at high latitude using GPS signal
Author :
Mezaoui, H. ; Hamza, A.M. ; Jayachandran, P.T.
Author_Institution :
Univ. of New Brunswick, Fredericton, NB, Canada
Abstract :
Summary form only given. Transionospheric radio signals experience both amplitude and phase variations as a result of their propagation through a turbulent ionosphere; this phenomenon is known as ionospheric scintillations. As a result of these fluctuations, GPS receivers lose track of signals, and consequently induce position and navigational errors. Therefore, a need to study these scintillations and their causes arises in order to resolve the navigational problem, and at the same time develop analytical and numerical radio propagation models. In order to quantify and qualify the High latitude ionospheric scintillations, we analyze the probability density functions (PDFs) of L1 GPS signals at 50Hz using the Canadian High Arctic Ionospheric Network (CHAIN) measurements. The raw signal is detrended using a wavelet-based technique and the detrended power and phase of the signal are used to construct probability density functions (PDFs) of the scintillating signal. The resulting PDFs are non-Gaussian. From these PDFs higher order moments are estimated. The calculated moments of the Power and phase distribution functions will help to quantify some of the scintillation characteristics and in the process provide a base for forecasting, i.e. develop a scintillation climatology model. The profile of the skewness-kurtosis for the power fluctuation has been found to collapse to a parabolic line. This allows us to draw analogies with other plasma physics experiments and neutral fluid turbulence where such a relation has been observed. The intermittent aspect of the scintillations is investigated by estimating the probability density functions (PDFs) for different fluctuation time scales. The Kurtosis of the PDFs is used to quantify the intermittency of the amplitude and phase fluctuations. This aspect of the signal has been found to be scale dependent, and the dependence is quantified and presented. In order to characterize and model the statistical behavior of the phase and- the amplitude fluctuation we proceed to a functional fit of the resulting PDFs using the Castaing distribution function. The latter is a convolution of a Gaussian function with a lognormal distribution of its variance. The resulting fits are presented, and an analysis of the moments of these distributions is discussed. In the scope of providing an estimate for an average spatial scale of the irregularities characterizing the turbulent activities within the scattering layer, we verify the validity of the Taylor hypothesis for the Ionospheric scintillation case. The calculated higher-order moments of the amplitude and phase distribution functions will help provide a base for forecasting, i.e. develop a scintillation climatology model. This statistical analysis, including power spectra, along with a numerical simulation, will constitute the backbone of a high latitude scintillation model.
Keywords :
Global Positioning System; fluctuations; ionospheric electromagnetic wave propagation; radiowave propagation; statistical analysis; statistical distributions; CHAIN measurements; Canadian High Arctic Ionospheric Network; Castaing distribution function; GPS receivers; Gaussian-lognormal variance distribution convolution; L1 GPS signals; Taylor hypothesis; amplitude fluctuation statistical behavior; amplitude variation; detrended signal phase; detrended signal power; frequency 50 Hz; high latitude ionospheric scintillations; neutral fluid turbulence; nonGaussian PDF; phase fluctuation statistical behavior; phase variation; power fluctuation skewness-kurtosis; probability density function; radio signal propagation; raw signal detrending; scintillation climatology model; transionospheric radio signals; turbulent ionosphere; wavelet based technique; Distribution functions; Electronic mail; Facsimile; Fluctuations; Global Positioning System; Numerical models; Probability density function;
Conference_Titel :
General Assembly and Scientific Symposium (URSI GASS), 2014 XXXIth URSI
Conference_Location :
Beijing
DOI :
10.1109/URSIGASS.2014.6929748