Characterization of low latitude ionospheric scintillations using EEMD - DFA method

Sridhar, M.; Ratnam, D. Venkata; Sirisha, B.; Sasanka, Ch. Rupesh; Kumar, B. Saketh; Ajay, Ch.; Raju, K. Padma; Rao, Ch. Srinivasa

doi:10.1109/iciiecs.2015.7193046

Cited by 6 publications

(9 citation statements)

References 20 publications

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“…Hence, the adoption of MFDFA, which is considered more effective and computes the signal average root mean square (RMS) fluctuations. Its accuracy paves the way for its miscellaneous applications, such as the measurement of non-stationary heartbeat in order to sense pathological conditions [10]. The fluctuations of the signal can be comprehended as either small or large depending on the randomness of the non-stationary signal.…”

Section: Multifractal Detrended Fluctuation Analysismentioning

confidence: 99%

“…The scaling exponent, (α), is the slope of the curve, which is calculated by log(F(n))/log(n) and is otherwise known as the correlation exponent. If the S 4 index values are uncorrelated, just as that of white noise, then α takes the value of 0.5 [10]. The threshold is obtained from the summation of the noise slope and a confidential offset [10].…”

Section: Multifractal Detrended Fluctuation Analysismentioning

confidence: 99%

“…If the S 4 index values are uncorrelated, just as that of white noise, then α takes the value of 0.5 [10]. The threshold is obtained from the summation of the noise slope and a confidential offset [10]. For signal reconstruction, the scaling exponents above the threshold are considered.…”

Section: Multifractal Detrended Fluctuation Analysismentioning

confidence: 99%

“…Amplitude and phase scintillations degrade the satellite communication [2] and navigation system receivers tracking performance [9], which includes loss of lock and cycle slip [8]. In order to design and improve the positioning accuracy, navigation, availability and, timing information of advanced communication and global navigation satellite system (GNSS) receivers [10], the monitoring and mitigation of ionospheric scintillation effects is essential [11].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Multifractal Detrended Fluctuation Analysismentioning

confidence: 99%

Section: Multifractal Detrended Fluctuation Analysismentioning

confidence: 99%

Section: Multifractal Detrended Fluctuation Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Ahmed

Marlia

et al. 2019

Remote Sensing

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“…Afterwards, De Moura et al [17] used the DFA to analyze the bearing fault. Sridhar et al [18] combined the EEMD with DFA to denoise the noise-corrupted signal. The DFA are used to determine the noise components in IMFs.…”

Section: Introductionmentioning

confidence: 99%

Detrended Fluctuation Analysis and Hough Transform Based Self-Adaptation Double-Scale Feature Extraction of Gear Vibration Signals

Wang¹,

Xiao²,

Lv³

et al. 2016

Shock and Vibration

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This paper presents the analysis of the vibration time series of a gear system acquired by piezoelectric acceleration transducer using the detrended fluctuation analysis (DFA). The experimental results show that gear vibration signals behave as double-scale characteristics, which means that the signals exhibit the self-similarity characteristics in two different time scales. For further understanding, the simulation analysis is performed to investigate the reasons for double-scale of gear's fault vibration signal. According to the analysis results, a DFA double logarithmic plot based feature vector combined with scale exponent and intercept of the small time scale is utilized to achieve a better performance of fault identification. Furthermore, to detect the crossover point of two time scales automatically, a new approach based on the Hough transform is proposed and validated by a group of experimental tests. The results indicate that, comparing with the traditional DFA, the faulty gear conditions can be identified better by analyzing the double-scale characteristics of DFA. In addition, the influence of trend order of DFA on recognition rate of fault gears is discussed.

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