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Detection of ionospheric scintillation effects using LMD–DFA

Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The performance and measurement accuracy of global navigation satellite system (GNSS) receivers is greatly affected by ionospheric scintillations. Rapid amplitude and phase variations in the received GPS signal, known as ionospheric scintillation, affects the tracking of signals by GNSS receivers. Hence, there is a need to investigate the monitoring of various activities of the ionosphere and to develop a novel approach for mitigation of ionospheric scintillation effects. A method based on Local Mean Decomposition (LMD)–Detrended Fluctuation Analysis (DFA) has been proposed. The GNSS data recorded at Koneru Lakshmaiah (K L) University, Guntur, India were considered for analysis. The carrier to noise ratio (C/N0) of GNSS satellite vehicles were decomposed into several product functions (PF) using LMD to extract the intrinsic features in the signal. Scintillation noise was removed by the DFA algorithm by selecting a suitable threshold. It was observed that the performance of the proposed LMD–DFA was better than that of empirical mode decomposition (EMD)–DFA.
Czasopismo
Rocznik
Strony
777--784
Opis fizyczny
Bibliogr. 24 poz.
Twórcy
  • Department of ECE, KL University, Guntur, India
  • Department of ECE, Andhra Loyola Institute of Engineering and Technology, Vijayawada, India
autor
  • Department of ECE, KL University, Guntur, India
  • Department of ECE, JNTUK, Kakinada, India
  • Department of ECE, KL University, Guntur, India
Bibliografia
  • 1. Aarons J (1982) Global morphology of ionospheric scintillations. Proc IEEE 70:360–378
  • 2. Acharya RU, Lim CM, Joseph P (2002) Heart rate variability analysis using correlation dimension and detrended fluctuation analysis. ITBM-RBM 23(6):333–339
  • 3. Basu et al (2002) Specification and forecasting of scintillations in communication/navigation links: current status and future plans. J Atmos Solar Terr Phys 64:1745–1754
  • 4. Cheng J, Zhang K, Yang Y (2012) An order tracking technique for the gear fault diagnosis using local mean decomposition method. Mech Mach Theory 55:67–76
  • 5. Echeverria J, Crowe J, Woolfson M et al (2001) Application of empirical mode decomposition to heart rate variability analysis. Med Biol Eng Compu 39:471–479
  • 6. Golinska AK (2012) Detrended fluctuation analysis (DFA) in biomedical signal processing: selected examples. Stud Logic Grammar Rhetoric 29:107–115
  • 7. Huang NE, Shen Z, Long SR et al (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc R Soc Lond A 454(1971):903–995
  • 8. Ihlen EA (2012) Introduction to multifractal detrended fluctuation analysis in Matlab. Front Physiol 3:141
  • 9. Kantelhardt JW, Koscielny-Bunde E et al (2001) Detecting long-range correlations with detrended fluctuation analysis. Physica A 295(3):441–454
  • 10. Kantelhardt JW, Zschiegner SA, Koscielny-Bunde E et al (2002) Multifractal detrended fluctuation analysis of nonstationary time series. Physica A 316:87–114
  • 11. Kintner P, Ledvina B, De Paula E (2007) GPS and ionospheric scintillations. Space Weather 5:9
  • 12. Li G, Ning B, Zhao B et al (2008) Effects of geomagnetic storm on GPS ionospheric scintillations at Sanya. J Atmos Solar Terr Phys 70:1034–1045
  • 13. Li Y, Xu M, Wang R et al (2016) A fault diagnosis scheme for rolling bearing based on local mean decomposition and improved multiscale fuzzy entropy. J Sound Vib 360:277–299
  • 14. Miriyala S, Koppireddi PR, Chanamallu SR (2015) Robust detection of ionospheric scintillations using MF-DFA technique. Earth Planets Space 67:1–5
  • 15. Park C, Looney D, Van Hulle MM et al (2011) The complex local mean decomposition. Neurocomputing 74:867–875
  • 16. Qian XY, Gu GF, Zhou WX (2011) Modified detrended fluctuation analysis based on empirical mode decomposition for the characterization of anti-persistent processes. Physica A 390:4388–4395
  • 17. Ratnam DV, Sivavaraprasad G, Lee J (2015) Automatic ionospheric scintillation detector for global navigation satellite system receivers. Radar Sonar Navigation IET 9:702–711
  • 18. Smith JS (2005) The local mean decomposition and its application to EEG perception data. J R Soc Interface 2:443–454
  • 19. Sun J, Xiao Q, Wen J et al (2016) Natural gas pipeline leak aperture identification and location based on local mean decomposition analysis. Measurement 79:147–157
  • 20. Tanna HJ, Pathak KN (2014) Multifractal behaviour of the ionospheric scintillation index time series over an Indian low latitude station Surat. J Atmos Solar Terr Phys 109:66–74
  • 21. Wang Y, He Z, Zi Y (2009) A demodulation method based on improved local mean decomposition and its application in rub-impact fault diagnosis. Meas Sci Technol 20(2):025704
  • 22. Wang Y, He Z, Zi Y (2010) A comparative study on the local mean decomposition and empirical mode decomposition and their applications to rotating machinery health diagnosis. J Vib Acoust 132(2):021010
  • 23. Yu D, Cheng J, Yang Y (2005) Application of EMD method and Hilbert spectrum to the fault diagnosis of roller bearings. Mech Syst Signal Process 19:259–270
  • 24. Yue J, Zhao X, Shang P (2010) Effect of trends on detrended fluctuation analysis of precipitation series. Math Probl Eng 30:1015–1030
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-ffca7a04-23a9-4ba4-bf6c-25c80d1e8b8b
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