Robust detection of ionospheric scintillations using MF-DFA technique

Sridhar, M.; Raju, K. Padma; Chanamallu, Srinivasa Rao

doi:10.1186/s40623-015-0268-1

Cited by 17 publications

(11 citation statements)

References 21 publications

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“…Various different approaches had been explored to understand nonlinear characteristics and intermittency in ionospheric irregularities, like structure function analysis (Dyrud et al, 2008;Spicher et al, 2015), fractal and multifractal analysis (Wernik et al, 2003;Alimov et al, 2008;Bolzan et al, 2013;Tanna and Pathak, 2014;Miriyala et al, 2015;Chandrasekhar et al, 2016;Fornari et al, 2016;Sivavaraprasad et al, 2018;Neelakshi et al, 2019), and multispectral optical imaging (Chian et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…The MFDFA has wide applications in many branches of science, such as medicine (Makowiec, 2011), physics (de Freitas et al, 2016), engineering (Lu et al, 2016), finance (Grech, 2016), and social sciences (Kantelhardt, 2009;Telesca and Lovallo, 2011), to understand the complexity of a system through its scaling exponents that characterize multifractal dynamics of the system. The MFDFA has been applied to study ionospheric scintillation index time series (Tanna and Pathak, 2014;Miriyala et al, 2015) and ionospheric total electron content data (Chandrasekhar et al, 2016;Sivavaraprasad et al, 2018). For example, a wavelet transform was applied to study ionospheric irregularities (Wernik et al, 2003;Bolzan et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

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Structural characterization of the equatorial F region plasma irregularities in the multifractal context

et al. 2020

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Abstract. In the emerging ionosphere–space–weather paradigm, investigating the dynamical properties of ionospheric plasma irregularities using advanced computational nonlinear algorithms provide new insights into their turbulent-seeming nature, for instance, the evidence of energy distribution via a multiplicative cascade. In this study, we present a multifractal analysis of the equatorial F region in situ data obtained from two different experiments performed at Alcântara (2.4∘ S, 44.4∘ W), Brazil, to explore their scaling structures. The first experiment observed several medium- to large-scale plasma bubbles whereas the second experiment observed vertical uplift of the base of the F region. The multifractal detrended fluctuation analysis and the p-model fit are used to analyze the plasma density fluctuation time series. The result shows the presence of multifractality with degree of multifractality 0.53–0.93 and 0.3≤p≤0.4 cascading probability for the first experiment. Other experimental data also exhibit multifractality with degree of multifractality 0.19–0.27 and 0.42≤p≤0.44 cascading probability in ionospheric plasma irregularities. Our results confirm the nonhomogeneous nature of plasma irregularities and characterize the underlying nonhomogeneous multiplicative cascade hypothesis in the ionospheric medium. Differences in terms of scaling and complexity in the data belonging to different types of phenomena are also addressed.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Structural characterization of the equatorial F region plasma irregularities in the multifractal context

et al. 2020

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“…Meanwhile, EMD's performance is limited [17] by its sensitivity to sampling and noise [18]. Multifractal detrended fluctuation analysis (MFDFA) has been used to investigate medical and geophysics applications, such as self-similarities and long-range correlations in the signals [19].…”

Section: Introductionmentioning

confidence: 99%

Mitigation of Ionospheric Scintillation Effects on GNSS Signals with VMD-MFDFA

Ahmed

Marlia

et al. 2019

Remote Sensing

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Severe scintillations degrade the satellite signal intensity below the fade margin of satellite receivers thereby resulting in failure of communication, positioning, and navigational services. The performance of satellite receivers is obviously restricted by ionospheric scintillation effects, which may lead to signal degradation primarily due to the refraction, reflection, and scattering of radio signals. Thus, there is a need to develop an ionospheric scintillation detection and mitigation technique for robust satellite signal receivers. Hence, variational mode decomposition (VMD) is proposed. VMD addresses the problem of ionospheric scintillation effects on global navigation satellite system (GNSS) signals by extracting the noise from the radio signals in combination with multifractal detrended fluctuation analysis (MFDFA). MFDFA helps as a criterion designed to detect and distinguish the intrinsic mode functions (IMFs) into noisy (scintillated) and noise-free (non-scintillated) IMF signal components using the MFDFA threshold. The results of the proposed method are promising, reliable, and have the potential to mitigate ionospheric scintillation effects on both the synthetic (simulated) and real GNSS data obtained from Manado station (latitude 1.34° S and longitude 124.82° E), Indonesia. From the results, the effectiveness of VMD-MFDFA over complementary ensemble empirical mode decomposition with MFDFA (CEEMD-MFDFA) is an indication of better performance.

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“…Most previous scintillation monitoring and detection methods have been based on lower order moments of the scintillation statistics, i.e., the probability density functions (PDFs) of the signal amplitude and phase, as well as the scintillation indices (e.g., S 4 and σ ϕ ) . These methods are simple to implement, but they overlook the higher‐moment features of scintillation signals, which results in a situation where scintillation can be easily confused with multipath and interference.…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of an Automatic GPS Ionospheric Phase Scintillation Detector Using a Machine‐Learning Algorithm

2017

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Ionospheric phase scintillation can cause errors or outage in GNSS navigation solutions. Timely detection of phase scintillation will enable adaptive processing to mitigate its effects on navigation solutions. This paper presents a machine-learning algorithm to autonomously detect phase scintillation based on frequency domain features. Validation using data from Gakona shows phase scintillation detection accuracy around 92 percent. Test results using data from Poker Flat, Jicamarca, Singapore, and Hong Kong demonstrate the capability of the trained detector to be applied more generally. Performance evaluation reveals that the values of phase scintillation index σ ϕ may be poor indications of scintillation activities. Concurrent phase and amplitude scintillation detection using similar machine-learning algorithms is further investigated with low-latitude data. Results suggest that at low latitudes an amplitude detector alone is sufficient to capture scintillation in general, while at high latitudes, a phase scintillation detector is necessary to capture the dominating phase scintillation events.

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Robust detection of ionospheric scintillations using MF-DFA technique

Cited by 17 publications

References 21 publications

Structural characterization of the equatorial F region plasma irregularities in the multifractal context

Structural characterization of the equatorial F region plasma irregularities in the multifractal context

Mitigation of Ionospheric Scintillation Effects on GNSS Signals with VMD-MFDFA

Performance Evaluation of an Automatic GPS Ionospheric Phase Scintillation Detector Using a Machine‐Learning Algorithm

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