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Multicomponent Analysis of Ionospheric Scintillation Effects Using the Synchrosqueezing Technique for Monitoring and Mitigating their Impact on GNSS Signals

Published online by Cambridge University Press:  28 November 2018

G. Sivavaraprasad ,
D. Venkata Ratnam  and
Yuichi Otsuka
G. Sivavaraprasad
Affiliation:
(Department of ECE, KLEF, KL University, Vaddeswaram, Guntur Dt, 522502, Andhra Pradesh, India)
D. Venkata Ratnam*
Affiliation:
(Department of ECE, KLEF, KL University, Vaddeswaram, Guntur Dt, 522502, Andhra Pradesh, India)
Yuichi Otsuka
Affiliation:
(ISEE, Nagoya University, Nagoya, Japan)
*
(E-mail: [email protected])
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Abstract

Ionospheric scintillation effects degrade satellite-based radio communication/navigation links and influence the performance of Global Navigation Satellite Systems (GNSS). An adaptive wavelet-based decomposition technique, Synchrosqueezing Transform (SST), with a Detrended Fluctuation Analysis (DFA) algorithm has been implemented for time-frequency representation of GNSS multi-component signals and mitigation of scintillation effects. Synthetic In-phase (I) and Quadra-phase (Q) samples were collected from the Cornell Scintillation Model (CSM) and the CSM amplitude scintillation signal was processed with SST-DFA for the detection of noisy scintillation components and mitigation of ionospheric scintillation effects. Also, performance of the SST-DFA algorithm was tested for real-time GNSS ionospheric scintillation data collected from a GNSS Software Navigation Receiver (GSNRx) located at a low-latitude station in Rio de Janeiro, Brazil. The de-noising performance of the SST-DFA algorithm was further evaluated and compared with a low-pass Butterworth filter during different ionospheric scintillation time periods. The experimental results clearly demonstrated that the proposed method is reliable for mitigation of ionospheric scintillation noise both in time and frequency scales.

Keywords

Global Navigation Satellite SystemSynchrosqueezing TransformDetrended Fluctuation AnalysisIonospheric scintillation
Type
Research Article
Information
The Journal of Navigation , Volume 72 , Issue 3 , May 2019 , pp. 669 - 684
Copyright
Copyright © The Royal Institute of Navigation 2018 

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References

REFERENCES

Aarons, J. (1982). Global morphology of ionospheric scintillations. Proceedings of the IEEE, 70, 360–378.Google Scholar
Ahmed, A., Tiwari, R., Strangeways, H., Dlay, S. and Johnsen, M. (2015). Wavelet-based analogous phase scintillation index for high latitudes. Space Weather, 13, 503–520.Google Scholar
Aquino, M., Moore, T., Dodson, A., Waugh, S., Souter, J. and Rodrigues, F.S., (2005). Implications of ionospheric scintillation for GNSS users in Northern Europe. The Journal of Navigation, 58, 2, 241–256.Google Scholar
Crane, R. K. (1977). Ionospheric scintillation. Proceedings of the IEEE, 65, 180–199.Google Scholar
Daubechies, I., Lu, J. and Wu, H.-T. (2011). Synchrosqueezed wavelet transforms: An empirical mode decomposition-like tool. Applied and Computational Harmonic Analysis, 30, 243–261.Google Scholar
Domingues, M. O., Mendes, O. Jr and Da Costa, A. M. (2005). On wavelet techniques in atmospheric sciences. Advances in Space Research, 35, 831–842.Google Scholar
Forte, B. (2012). Analysis of the PLL phase error in presence of simulated ionospheric scintillation events. Radio Science, 47, 3, 1–21.Google Scholar
Fortes, L.P.S., Lin, T. and Lachapelle, G. (2015). Effects of the 2012–2013 solar maximum on GNSS signals in Brazil. GPS Solutions, 19, 2, 309–319.Google Scholar
Fu, W., Han, S., Rizos, C., Knight, M. and Finn, A. (1999). Real-time ionospheric scintillation monitoring. Proceedings of the 12th International Technical Meeting of the Satellite Division of the US Institute of Navigation GPS ION, 1–10.Google Scholar
Herrera, R. H., Han, J. and Van der Baan, M. (2014). Applications of the synchrosqueezing transform in seismic time-frequency analysis. Geophysics, 79, V55–V64.Google Scholar
Liu, Y., Yang, G., Li, M. and Yin, H. (2016). Variational mode decomposition denoising combined the detrended fluctuation analysis. Signal Processing, 125, 349–364.Google Scholar
Mallat, S. (1999). A Wavelet Tour of Signal Processing 2nd Edition. Elsevier, San Diego, CA. http://dx.doi.org/10.1016/B978-012466606-1/50000-3.Google Scholar
Materassi, M. and Mitchell, C.N. (2007). Wavelet analysis of GPS amplitude scintillation: A case study. Radio Science, 42, 1, 1–10.Google Scholar
Miriyala, S., Koppireddi, P. R. and Chanamallu, S. R. (2015). Robust detection of ionospheric scintillations using MF-DFA technique. Earth, Planets and Space, 67, 98, 1–5.Google Scholar
Mousavi, S. M., Langston, C. A. and Horton, S. P. (2016). Automatic microseismic denoising and onset detection using the synchrosqueezed continuous wavelet transform. Geophysics, 81, V341–V355.Google Scholar
Mushini, S. C., Jayachandran, P., Langley, R., MacDougall, J. and Pokhotelov, D. (2012). Improved amplitude-and phase-scintillation indices derived from wavelet detrended high-latitude GPS data. GPS Solutions, 16, 363–373.Google Scholar
Najmafshar, M., Skone, S. and Ghafoori, F., (2014). GNSS data processing investigations for characterizing ionospheric scintillation. In ION GNSS, 2014, 1–12.Google Scholar
Peng, C.-K., Buldyrev, S. V., Havlin, S., Simons, M., Stanley, H. E. and Goldberger, A. L. (1994). Mosaic organization of DNA nucleotides. Physical Review E, 49, 2, 1685–1689.Google Scholar
Ratnam, D. V., Sivavaraprasad, G. and Lee, J. (2015). Automatic ionospheric scintillation detector for global navigation satellite system receivers. IET Radar, Sonar & Navigation, 9, 702–711.Google Scholar
Ruan, H., Zhang, L., Luo, Y. and Long, T. (2017). GNSS Carrier Phase Tracking With Discrete Wavelet Transform Filtering Under Ionospheric Scintillation. IEEE Communications Letters, 21, 394–397.Google Scholar
Sivavaraprasad, G., Padmaja, R. S. and Ratnam, D. V. (2017). Mitigation of ionospheric scintillation effects on GNSS signals using variational mode decomposition. IEEE Geoscience and Remote Sensing Letters, 14, 389–393.Google Scholar
Thakur, G., Brevdo, E., Fučkar, N. S. and Wu, H.-T. (2013). The synchrosqueezing algorithm for time-varying spectral analysis: Robustness properties and new paleoclimate applications. Signal Processing, 93, 1079–1094.Google Scholar
Torrence, C. and Compo, G. P. (1998). A practical guide to wavelet analysis. Bulletin of the American Meteorological Society, 79, 61–78.Google Scholar
Wang, J., Morton, Y., Zhou, Q., van Graas, F. and Pelgrum, W. (2012). Time-frequency analysis of ionosphere scintillations observed by a GNSS receiver array. In Position Location and Navigation Symposium (PLANS), 2012 IEEE/ION, 274–281.Google Scholar
Weng, D., Ji, S., Chen, W. and Liu, Z. (2014). Assessment and mitigation of ionospheric disturbance effects on GPS accuracy and integrity. The Journal of Navigation, 67, 371–384.Google Scholar
Wu, H.-T., Flandrin, P. and Daubechies, I. (2011). One or two frequencies? The synchrosqueezing answers. Advances in Adaptive Data Analysis, 3, 29–39.Google Scholar